Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (1): 152-166.doi: 10.3864/j.issn.0578-1752.2022.01.013

• HORTICULTURE • Previous Articles     Next Articles

Identification of Co-Expression Gene Related to Tea Plant Response to Glyphosate Based on WGCNA

GUO YongChun1(),WANG PengJie1,JIN Shan1,HOU Binghao1,WANG ShuYan1,ZHAO Feng2(),YE NaiXing1()   

  1. 1College of Horticulture, Fujian Agriculture and Forestry University/Key Laboratory of Tea Science in Universities of Fujian Province, Fuzhou 350002
    2School of Pharmacy, Fujian University of Chinese Medicine, Fuzhou 350122
  • Received:2021-03-12 Accepted:2021-05-10 Online:2022-01-01 Published:2022-01-07
  • Contact: Feng ZHAO,NaiXing YE E-mail:1062941682@qq.com;zhaofeng0591@fjtcm.edu.cn;ynxtea@126.com

Abstract:

【Objective】 This study aimed at analyzing both expression patterns and regulatory pathways of tea plants in response to glyphosate stressing, which could revealed the effect of glyphosate herbicides on tea plants at transcriptional level and identify key genes of tea plants. 【Method】 C. sinensis cv Jin-guanyin was applied as material plant. A recommended concentration of glyphosate was irrigated to test plants. The leave samples were collected at different time intervals (0, 0.25, 1, 3 and 7 d). The samples were sequenced by transcriptome, the content of shikimic acid was also quantified. The WGCNA method was used to jointly analyze transcriptome and shikimic acid content data, to identify co-expressed gene modules related to glyphosate response, and to screen out key regulatory genes. 【Result】 The content of shikimic acid in tea leaves reduced gradually during first 3 days. However, it suddenly reached a peak on the 7th day (6.99 times compared with no glyphosate treated sample). A total of 12 568 differential expression genes (DEGs) were also identified, which mainly enriched in phenylpropane, flavonoid biosynthesis and plant hormone signal transduction pathways. In addition, the glyphosate treatment induced 24, 52, 31 and 69 genes respectively which related to shikimic acid metabolism, phenylpropane, flavonoid biosynthesis and hormone signal transduction pathways. A total of 19 modules were screened out by WGCNA method. The correlation analysis of transcriptome and shikimic acid content indicated two key modules, including 2 024 and 2 305 genes, respectively. The top 50 genes with the highest connectivity in the key modules were selected for co-expression analysis, and 6 key response genes were obtained, including 2 resistance genes (SHMT and RPM), 1 drug resistance gene (PDR), 1 ion transport gene (At), 1 membrane transport gene (GPT), and 1 transcription factor gene (ERF).【Conclusion】 Glyphosate could affect downstream genes transcription of phenylpropane, flavonoid biosynthesis and hormone signal transduction pathways by interfering shikimic acid metabolism of tea plants. In addition, this study also identified two co-expression modules closely related to glyphosate response, and found that multiple potential candidate genes and transcription factors could resist glyphosate stress, such as SHMT, RPM, At, PDR, ERF and GPT.

Key words: Camellia sinensis, glyphosate, shikimic acid, transcriptome, WGCNA

Fig. 1

Leaf phenotype under glyphosate treatment"

Fig. 2

Shikimic acid accumulation under glyphosate treatment Different lowercase letters indicate significant difference (P<0.05)"

Table 1

Quality of the transcriptome data of each sample"

样本名称
Sample ID
原始序列
Raw reads
过滤序列
Clean reads
过滤碱基
Clean bases
Q30
(%)
总比对率
Total mapped (%)
外显子
CDS (%)
内含子
Intron (%)
基因间区Intergenic (%)
0 d-1 51744132 51299070 7572138776 94.01 89.05 86.05 3.31 4.12
0 d-2 46244874 45818216 6794947547 94.05 89.23 86.93 2.63 3.95
0 d-3 44626020 44225824 6553924919 93.95 89.10 85.88 3.54 4.19
0.25 d-1 45064488 44614914 6599116801 93.83 87.83 84.28 3.83 4.62
0.25 d-2 43067110 42652694 6305386229 94.07 88.74 84.05 3.87 4.70
0.25 d-3 48340622 47899314 7086415271 93.78 88.49 84.08 4.18 4.57
1 d-1 41929042 41488506 6120727532 93.69 88.40 85.75 2.92 4.16
1 d-2 48077998 47713728 7065390194 94.14 88.63 85.69 3.52 4.16
1 d-3 42384792 42031890 6227498606 94.22 88.69 85.87 3.26 4.04
3 d-1 48203390 47847756 7076619275 94.13 89.21 84.08 5.28 4.23
3 d-2 55290358 54899776 8119654579 94.13 89.15 84.91 4.74 4.06
3 d-3 47899016 47516386 7037854655 93.80 88.67 85.40 4.16 3.96
7 d-1 51246042 50703198 7458204310 94.33 89.79 85.08 3.88 4.13
7 d-2 46843028 46469848 6887767322 93.98 87.69 85.01 4.18 4.12
7 d-3 52226524 51742808 7626325817 93.97 89.64 85.04 3.93 4.06
合计 Total 713187436 706923928 104531971833

Fig. 3

Sample correlation and DEGs of tea plants under glyphosate treatment A: Correlation. B: The horizontal bar graph on the left represents the DEGs of each set. In the middle matrix, a single point represents the unique elements of a set, and the lines between points represent the unique intersection of different sets. In the vertical bar graph, the DEGs of corresponding intersection are respectively represented"

Fig. 4

KEGG enrichment analysis of DEGs The vertical axis represents the KEGG pathway enriched by multiple genes, the horizontal axis represents the Rich factor (the larger the Rich factor, the greater the degree of enrichment), the size of the dot represents the number of genes in this pathway, and the colors correspond to different ranges of P value"

Fig. 5

Phenylpropane, flavonoids and shikimic acid biosynthetic pathways and the expression levels of related DEGs The red border in the pathway indicates the DEGs annotated; the heat map indicates the expression level of all DEGS related to the pathway, which is generated by log2 conversion from the average value calculated by three repeated samples. The same as below"

Fig. 6

Phytohormone signal transduction pathway and the expression analysis of related DEGs A: The plant hormone signal pathway enriched by DEGs; B: The expression level of DEGs related to plant hormone signal transduction"

Fig. 7

Sample clustering, soft threshold screening and gene module construction A: The sample clustering tree; B: The scale-free fitness curve and the average connectivity curve; C: The gene clustering tree and module cutting, and each branch of the gene clustering tree corresponds to a module"

Fig. 8

Correlation analysis between modules and phenotypes The abscissa represents different phenotypes, and the ordinate represents different modules. The number in the left column of the figure represents the number of genes in the module, and each group of data on the right represents the r value of the correlation coefficient between the module and the phenotype and the significance P value (in parentheses). Red means the module has a greater correlation with the phenotype, and blue means the module has a lower correlation with the phenotype"

Fig. 9

KEGG enriched bubble diagram of green (A) and brown (B) module The vertical axis represents the name of the pathway, and the horizontal axis represents the rich factor; the size of the dot indicates the number of genes in the pathway, and the color of the dot corresponds to different P value ranges. The figure only shows the KEGG Pathway whose enrichment degree is in the top 20 under the premise of P-value<0.05"

Fig. 10

Gene co-expression network and hub genes in green (A) and brown (B) modules The triangle nodes are the top 3 genes in connectivity, and the black border nodes are transcription factor genes"

Fig. 11

Validation and correlation analysis of 15 DEGs Heat maps indicate the gene expression levels of RNA-Seq and qRT-PCR, respectively; the value between the two heat maps represents the correlation coefficient of the qRT-PCR and RNA-seq values of each gene"

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