Nitrogen (N) is crucial for maize (Zea mays L.) growth, development and yield. Dysfunction of the maize NIN-like protein 5 (ZmNLP5) reduces N assimilation, but the precise mechanism by which ZmNLP5 modulates N metabolism and its contribution to agricultural applications are not well understood. In this study, our transcriptomic profiling and chromatin immunoprecipitation followed by sequencing (ChIP-seq) analyses reveal 581 ZmNLP5-modulated target genes. We confirmed that ZmNLP5 physically interacted and transactivated contributors to N assimilation, including ZmNRT1.1D, ZmNIR1.1, ZmNR2.1, ZmGS4, ZmAS1, and ZmLBD6. Overexpression of ZmNLP5 upregulated N metabolism genes, and enhanced the enzymatic activities of nitrate reductase and glutamine synthase. Furthermore, overexpressing ZmNLP5 increased grain yield under both normal-N and low-N conditions. Introgression of the overexpressed ZmNLP5 allele into Zhengdan 958 resulted in comparable yield increases in field trials. Our study unveiled that ZmNLP5 is a potential genetic target for increasing nitrogen use efficiency and grain yield in maize.
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Open Access
Research paper
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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.
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.
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.
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.
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.
Open Access
Research Article
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Heterosis, the phenomenon in which hybrids outperform their parents, has been utilized in maize (Zea mays L.) for over 100 years. To provide a more complete understanding of heterosis, we collected a comprehensive transcriptome and translatome dataset on seedling leaves for B73, Mo17, and their F1 hybrid, which provided a dynamic landscape of transcriptomic and translatomic variation in maize. Although additivity accounted for a large proportion of variation at two omics-levels, an elevated nonadditive effect was observed in the translatome, especially in the translated subgenome maize1 genes, and the genes that switched from additivity in the transcriptome to nonadditivity in the translatome were significantly enriched in the subgenome maize1. Many genes with allele-specific expression and translation show dramatic regulatory switches between the transcriptome and translatome, and partial genes with allele-specific translation underlying regulatory mechanism also exhibited subgenome bias. Interestingly, we found the translated isoforms show different expression patterns compared with transcriptome and more genes changed their dominant isoforms during the genetic flow from parents to the hybrid at the translatome level. The translated genes with switched dominant isoforms significantly biased to the subgenome maize2 while genes with conserved dominant isoforms significantly enriched in subgenome maize1. Together, the dynamic changed patterns in translatome across hybrid and parental lines show translational fractionation of the maize subgenomes, which may be associated with heterosis in maize and provides a potential theoretical basis for breeding.
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