Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (3): 438-450.doi: 10.3864/j.issn.0578-1752.2022.03.002

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Screening and Application of Biomarkers Related to Maize Nitrogen Status

SHI Xi1,2(),NING LiHua2,GE Min2,WU Qi2,ZHAO Han1,2,*()   

  1. 1 College of Agriculture, Nanjing Agricultural University, Nanjing 210014
    2 Institute of Crop Germplasm and Biotechnology, Jiangsu Academy of Agricultural Sciences/Provincial Key Laboratory of Agrobiology, Nanjing 210014
  • Received:2021-07-30 Accepted:2021-10-18 Online:2022-02-01 Published:2022-02-11
  • Contact: Han ZHAO E-mail:nancy1448374364@163.com;zhaohan@jaas.ac.cn

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Abstract: 【Background】 The transcriptional levels of selected genes, referred as biomarkers, have been widely applied in clinical diagnosis processes. They were yet rarely used in agricultural cultivation for determining the nutrient statues in maize. 【Objective】 This study aims to explore the genes that can be used as biomarkers to reflect the nitrogen abundance in maize, so as to help the precise application of nitrogen fertilizer. 【Method】 Based on the data of gene chip and RNA-Seq under different nitrogen treatments, we chose the genes with high transcriptional abundance in response to N fluctuation as candidates. These genes were further screened by qRT-PCR and Kjeldahl methods using maize materials with different genotypes under different nitrogen treatments. The generalized linear models for predicting nitrogen status were constructed to accurately indicate the nitrogen nutrition status of maize. 【Result】 Firstly, we selected ten candidate genes with high expression level that are responsible for N fluctuation. Secondly, we found eight candidate genes that are differentially expressed under N treatment; Next, twenty-seven inbred and four hybrid lines covering a rich array of genetic diversity were selected to screen the candidate genes, and found that four genes stably expressed in different genotypes of maize. The expression abundance difference of these four genes were significantly correlated with total nitrogen content in panicle leaves through correlation analysis (R2 was greater than 0.6) with sufficient nitrogen and limited nitrogen treatment in thirty materials. According to the above results, these four genes can be used as nitrogen response biomarkers to indicate maize nitrogen status. The two-genes, three-genes and four-genes models were constructed by these four biomarker genes for predicting nitrogen status. The three-genes model was composed of Zm000011d024281 (X2), Zm000011d039049 (X3) and Zm000011d037680 (X4) were the most useful model for predicting the nitrogen status of maize plants, and the functional relationship was Y=1.143+0.017X2-0.302X3+0.017X4. Finally, the prediction function of the three-genes model was verified with six hybrids planted in the field. The results show that the three-genes model can accurately diagnose the nitrogen nutrition status of maize planted in the field environment. 【Conclusion】 We explored and verified four biomarker genes highly responsive to maize nitrogen status. The three-genes model works best in predicting the maize nitrogen nutrition status. The development of the biomarker can effectively and real-timely monitor the nitrogen status of maize plants, thus is helpful for optimizing the use of nitrogen fertilizer, thereby maximize the crop yield at the lowest cost.


Key words: Zea mays, biomarker, nitrogen, panicle leaf, determination of total nitrogen, generalized linear models

Table 1

The primer sequences used in this study"

基因编号 Gene No. 基因 ID Gene ID 正向引物序列 Forward primer sequence (5′-3′) 反向引物序列 Reverse primer sequence (5′-3′)
NM01 Zm00001d009599 TCAAGTCTGCCGAGGAGGTG CGGGTCCAGAATCCCTGTGTC
NM02 Zm00001d022630 ACACCGTACCGTCGTCTCTG TCGTTGGGCATCATATCACAGTG
NM03 Zm00001d024281 TGGTCCGACAGGTTCTACAAGG AGTGCTCGCTGGTGTGCTC
NM04 Zm00001d045519 GAAGAAGGGCACCACCAT TTCTCTGGATCGTCTCCCT
NM05 Zm00001d026696 TGGATGAAGCAGCTGATGTT GAAATTCTTGAAGGCGTGGATG
NM06 Zm00001d039049 CTACGGCGAGGGCACATCC GACAGACAGACAGAGGTCCATCC
NM07 Zm00001d037680 CGCACCGAGGACCAGAGG CAGCGCACCTCCTCAGCAG
NM08 Zm00001d042867 GCGACCAGTGCGGCATTTG ATCATCAGTACGACGGGTGCAG
NM09 Zm00001d003924 GTCGCCTACGTCGAGTTC GTTGCGTGTAGATGAAGTTGTC
NM10 Zm00001d002052 ATTGCCATCCACGGCGG TGCGCGAAGCGGAGGAGGA
NM11 Zm00001d006438 CACCCGGTTGGCTATGCTGTAC TGTGCTCCACCAGAAGGCTGAC

Table 2

Preliminary screening of candidate genes for nitrogen responsive biomarkers"

阶段
Phase		 数据处理步骤
Data-processing step		 输入探针数量
No. of input probe set		 输出探针数量
No. of output probe set
数据过滤
Data filtering		 （1）Log2强度值>9.0 Log2 intensity>9.0 84246 11891
（2）t-test P<0.01 11891 6632
（3）基因表达强度差异>10或<0.1
The fold difference of gene expression>10 or <0.1		 6632 69
氮响应生物标记物候选基因筛选
Screened the candidate genes for nitrogen responsive biomarkers		 （1）筛选在2组数据中均显著响应氮处理的候选基因
Screened the candidate genes that significantly responded to nitrogen treatment in both groups of data		 63 25
（2）MAIZE GDB筛选叶片中表达的候选基因
Screened candidate genes expressed in leaves by MAIZE GDB		 25 10

Fig. 1

B73 material responded to N1 and N0 nitrogen treatment A, B: Plant type difference of B73 under N1 and N0 nitrogen treatment; C: Difference in total nitrogen content of B73 under N1 and N0 nitrogen treatment; D: Expression of 10 candidate genes responded to N1 and N0 nitrogen treatment"

Fig. 2

Expression of four candidate genes in the test maize varieties"

Fig. 3

Determination of total nitrogen and correlation analysis A: Determination of total nitrogen in panicle leaf by Kjeldahl method; B-E: Correlation analysis between expression abundance of NM02, NM03, NM06, NM07 genes and total nitrogen content"

Table 3

Four biomarker candidate genes"

基因编号
Gene No.		 基因ID
Gene ID		 功能
Function		 N0处理后基因表达丰度变化
Changes of gene expression abundance under N0 treatment
NM02 Zm00001d022630 编码KNOTTED INDUCED1蛋白 Coding KNOTTED INDUCED 1 protein 下调 Down-regulated
NM03 Zm00001d024281 编码多胺氧化酶1 Coding POLYAMINE OXIDASE 1 下调 Down-regulated
NM06 Zm00001d039049 编码MYB蛋白Coding MYB protein 上调 Up-regulated
NM07 Zm00001d037680 编码营养贮藏蛋白2 Coding VEGETATIVE STORAGE PROTEIN 2 下调 Down-regulated

Table 4

Model function relationship"

广义线性模型
Generalized linear models		 函数关系
Function relationship		 相关性
Correlation (R2)		 P值
P value
两基因模型 The two-genes model Y=1.152–0.306X3+0.020X4 0.51 7.283E-9
三基因模型 The three-genes model Y=1.143+0.017X2–0.302X3+0.017X4 0.53 1.870E-8
四基因模型 The four-genes model Y=1.142+0.020X2–0.303X3+0.017X4 0.53 8.411E-8

Fig. 4

The generalized linear model A: The two-genes model; B: The three-genes model; C: The four-genes model"

Fig. 5

Nitrogen content predicted by the three-genes model"

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