玉米氮状况相关生物标记物的筛选和应用

doi:10.3864/j.issn.0578-1752.2022.03.002

摘要/Abstract

摘要： 【背景】 RNA表达丰度作为一种生物标记物已广泛应用于临床诊断阶段,但在农业栽培中诊断作物营养状况的应用较少。【目的】挖掘和验证转录水平上可以作为生物标记物精确指示玉米氮营养状况的基因,指导精准施用氮肥。【方法】基于不同氮素处理的基因芯片和RNA-Seq数据,通过生物信息学和统计学方法初步筛选出基因表达丰度高度响应氮素处理的生物标记物候选基因;利用不同基因型、不同氮处理的玉米材料,通过荧光定量PCR方法和凯氏定氮法,进一步筛选氮响应生物标记物基因;并构建预测玉米氮状况的广义线性模型,准确指示玉米氮营养状况。【结果】首先初步筛选出10个表达水平较高的基因,且mRNA表达丰度高度响应氮素处理的生物标记物候选基因;利用不同氮素条件下种植的B73材料从10个候选基因中进一步筛选出8个在充足氮、限制氮处理后基因表达丰度存在显著差异的基因;随后选取遗传多样性丰富、生态区域广泛的26种自交系材料和4种杂交种材料进一步筛选,发现有4个基因表达独立于基因型,可以在不同基因型玉米材料中稳定表达;并且通过相关性分析发现,在充足氮和限制氮处理下,这4个基因表达丰度差异与穗位叶总氮含量差异具有显著相关性（R²均大于0.6）,表明这4个基因可以作为氮响应生物标记物进行实际应用;将4个氮响应生物标记物基因分别组合构建两基因、三基因、四基因线性模型,由Zm00001d024281（X₂）、Zm00001d039049（X₃）和Zm00001d037680（X₄）这三个基因构建的线性模型用于预测玉米植株氮状况的应用性最强,其函数关系为Y=1.143+0.017X₂–0.302X₃+0.017X₄;选取大田种植的6个杂交种材料对三基因模型预测功能进行验证,结果表明,三基因模型能够在大田环境中准确诊断玉米植株氮素营养状况。【结论】获得4个高度响应玉米氮状况的生物标记物基因,构建的三基因模型可以准确预测玉米氮素营养状况。该生物标记物的开发可以有效实时监测玉米植株氮状态,优化氮肥使用,从而实现成本最低时农作物产量的最大化。

关键词: 玉米, 生物标记物, 氮素, 穗位叶, 总氮测定, 广义线性模型

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+0.017X_4. 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

石习, 宁丽华, 葛敏, 邬奇, 赵涵. 玉米氮状况相关生物标记物的筛选和应用[J]. 中国农业科学, 2022, 55(3): 438-450.

SHI Xi, NING LiHua, GE Min, WU Qi, ZHAO Han. Screening and Application of Biomarkers Related to Maize Nitrogen Status[J]. Scientia Agricultura Sinica, 2022, 55(3): 438-450.

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链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2022.03.002

https://www.chinaagrisci.com/CN/Y2022/V55/I3/438

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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

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

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

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