Nitrogen-dependent binding of the transcription factor PBF1 contributes to the balance of protein and carbohydrate storage in maize endosperm.

Fluctuations in nitrogen (N) availability influence protein and starch levels in maize (Zea mays) seeds, yet the underlying mechanism is not well understood. Here, we report that N limitation impacted the expression of many key genes in N and carbon (C) metabolism in the developing endosperm of maize. Notably, the promoter regions of those genes were enriched for P-box sequences, the binding motif of the transcription factor prolamin-box binding factor 1 (PBF1). Loss of PBF1 altered accumulation of starch and proteins in endosperm. Under different N conditions, PBF1 protein levels remained stable but PBF1 bound different sets of target genes, especially genes related to the biosynthesis and accumulation of N and C storage products. Upon N-starvation, the absence of PBF1 from the promoters of some zein genes coincided with their reduced expression, suggesting that PBF1 promotes zein accumulation in the endosperm. In addition, PBF1 repressed the expression of sugary1 (Su1) and starch branching enzyme 2b (Sbe2b) under normal N supply, suggesting that, under N-deficiency, PBF1 redirects the flow of C skeletons for zein toward the formation of C compounds. Overall, our study demonstrates that PBF1 modulates C and N metabolism during endosperm development in an N-dependent manner.

Single-parent expression drives dynamic gene expression complementation in maize hybrids.

Single-parent expression (SPE) is defined as gene expression in only one of the two parents. SPE can arise from differential expression between parental alleles, termed non-presence/absence (non-PAV) SPE, or from the physical absence of a gene in one parent, termed PAV SPE. We used transcriptome data of diverse Zea mays (maize) inbreds and hybrids, including 401 samples from five different tissues, to test for differences between these types of SPE genes. Although commonly observed, SPE is highly genotype and tissue specific. A positive correlation was observed between the genetic distance of the two inbred parents and the number of SPE genes identified. Regulatory analysis showed that PAV SPE and non-PAV SPE genes are mainly regulated by cis effects, with a small fraction under trans regulation. Polymorphic transposable element insertions in promoter sequences contributed to the high level of cis regulation for PAV SPE and non-PAV SPE genes. PAV SPE genes were more frequently expressed in hybrids than non-PAV SPE genes. The expression of parentally silent alleles in hybrids of non-PAV SPE genes was relatively rare but occurred in most hybrids. Non-PAV SPE genes with expression of the silent allele in hybrids are more likely to exhibit above high parent expression level than hybrids that do not express the silent allele, leading to non-additive expression. This study provides a comprehensive understanding of the nature of non-PAV SPE and PAV SPE genes and their roles in gene expression complementation in maize hybrids.

A cost-effective tsCUT&Tag method for profiling transcription factor binding landscape.

Knowledge of the transcription factor binding landscape (TFBL) is necessary to analyze gene regulatory networks for important agronomic traits. However, a low-cost and high-throughput in vivo chromatin profiling method is still lacking in plants. Here, we developed a transient and simplified cleavage under targets and tagmentation (tsCUT&Tag) that combines transient expression of transcription factor proteins in protoplasts with a simplified CUT&Tag without nucleus extraction. Our tsCUT&Tag method provided higher data quality and signal resolution with lower sequencing depth compared with traditional ChIP-seq. Furthermore, we developed a strategy combining tsCUT&Tag with machine learning, which has great potential for profiling the TFBL across plant development.

The NIN-like protein 5 (ZmNLP5) transcription factor is involved in modulating the nitrogen response in maize.

Maize exhibits marked growth and yield response to supplemental nitrogen (N). Here, we report the functional characterization of a maize NIN-like protein ZmNLP5 as a central hub in a molecular network associated with N metabolism. Predominantly expressed and accumulated in roots and vascular tissues, ZmNLP5 was shown to rapidly respond to nitrate treatment. Under limited N supply, compared with that of wild-type (WT) seedlings, the zmnlp5 mutant seedlings accumulated less nitrate and nitrite in the root tissues and ammonium in the shoot tissues. The zmnlp5 mutant plants accumulated less nitrogen than the WT plants in the ear leaves and seed kernels. Furthermore, the mutants carrying the transgenic ZmNLP5 cDNA fragment significantly increased the nitrate content in the root tissues compared with that of the zmnlp5 mutants. In the zmnlp5 mutant plants, loss of the ZmNLP5 function led to changes in expression for a significant number of genes involved in N signalling and metabolism. We further show that ZmNLP5 directly regulates the expression of nitrite reductase 1.1 (ZmNIR1.1) by binding to the nitrate-responsive cis-element at the 5' UTR of the gene. Interestingly, a natural loss-of-function allele of ZmNLP5 in Mo17 conferred less N accumulation in the ear leaves and seed kernels resembling that of the zmnlp5 mutant plants. Our findings show that ZmNLP5 is involved in mediating the plant response to N in maize.

Development of novel InDel markers and genetic diversity in Chenopodium quinoa through whole-genome re-sequencing.

BACKGROUND: Quinoa (Chenopodium quinoa Willd.) is a balanced nutritional crop, but its breeding improvement has been limited by the lack of information on its genetics and genomics. Therefore, it is necessary to obtain knowledge on genomic variation, population structure, and genetic diversity and to develop novel Insertion/Deletion (InDel) markers for quinoa by whole-genome re-sequencing. RESULTS: We re-sequenced 11 quinoa accessions and obtained a coverage depth between approximately 7× to 23× the quinoa genome. Based on the 1453-megabase (Mb) assembly from the reference accession Riobamba, 8,441,022 filtered bi-allelic single nucleotide polymorphisms (SNPs) and 842,783 filtered InDels were identified, with an estimated SNP and InDel density of 5.81 and 0.58 per kilobase (kb). From the genomic InDel variations, 85 dimorphic InDel markers were newly developed and validated. Together with the 62 simple sequence repeat (SSR) markers reported, a total of 147 markers were used for genotyping the 129 quinoa accessions. Molecular grouping analysis showed classification into two major groups, the Andean highland (composed of the northern and southern highland subgroups) and Chilean coastal, based on combined STRUCTURE, phylogenetic tree and PCA (Principle Component Analysis) analyses. Further analysis of the genetic diversity exhibited a decreasing tendency from the Chilean coast group to the Andean highland group, and the gene flow between subgroups was more frequent than that between the two subgroups and the Chilean coastal group. The majority of the variations (approximately 70%) were found through an analysis of molecular variation (AMOVA) due to the diversity between the groups. This was congruent with the observation of a highly significant FST value (0.705) between the groups, demonstrating significant genetic differentiation between the Andean highland type of quinoa and the Chilean coastal type. Moreover, a core set of 16 quinoa germplasms that capture all 362 alleles was selected using a simulated annealing method. CONCLUSIONS: The large number of SNPs and InDels identified in this study demonstrated that the quinoa genome is enriched with genomic variations. Genetic population structure, genetic core germplasms and dimorphic InDel markers are useful resources for genetic analysis and quinoa breeding.

Genome-wide identification and functional prediction of nitrogen-responsive intergenic and intronic long non-coding RNAs in maize (Zea mays L.).

BACKGROUND: Nitrogen (N) is an essential and often limiting nutrient to plant growth and development. Previous studies have shown that the mRNA expressions of numerous genes are regulated by nitrogen supplies; however, little is known about the expressed non-coding elements, for example long non-coding RNAs (lncRNAs) that control the response of maize (Zea mays L.) to nitrogen. LncRNAs are a class of non-coding RNAs larger than 200 bp, which have emerged as key regulators in gene expression. RESULTS: In this study, we surveyed the intergenic/intronic lncRNAs in maize B73 leaves at the V7 stage under conditions of N-deficiency and N-sufficiency using ribosomal RNA depletion and ultra-deep total RNA sequencing approaches. By integration with mRNA expression profiles and physiological evaluations, 7245 lncRNAs and 637 nitrogen-responsive lncRNAs were identified that exhibited unique expression patterns. Co-expression network analysis showed that the nitrogen-responsive lncRNAs were enriched mainly in one of the three co-expressed modules. The genes in the enriched module are mainly involved in NADH dehydrogenase activity, oxidative phosphorylation and the nitrogen compounds metabolic process. CONCLUSIONS: We identified a large number of lncRNAs in maize and illustrated their potential regulatory roles in response to N stress. The results lay the foundation for further in-depth understanding of the molecular mechanisms of lncRNAs' role in response to nitrogen stresses.

Heterotic quantitative trait loci analysis and genomic prediction of seedling biomass-related traits in maize triple testcross populations.

BACKGROUND: Heterosis has been widely used in maize breeding. However, we know little about the heterotic quantitative trait loci and their roles in genomic prediction. In this study, we sought to identify heterotic quantitative trait loci for seedling biomass-related traits using triple testcross design and compare their prediction accuracies by fitting molecular markers and heterotic quantitative trait loci. RESULTS: A triple testcross population comprised of 366 genotypes was constructed by crossing each of 122 intermated B73 × Mo17 genotypes with B73, Mo17, and B73 × Mo17. The mid-parent heterosis of seedling biomass-related traits involved in leaf length, leaf width, leaf area, and seedling dry weight displayed a large range, from less than 50 to ~ 150%. Relationships between heterosis of seedling biomass-related traits showed congruency with that between performances. Based on a linkage map comprised of 1631 markers, 14 augmented additive, two augmented dominance, and three dominance × additive epistatic quantitative trait loci for heterosis of seedling biomass-related traits were identified, with each individually explaining 4.1-20.5% of the phenotypic variation. All modes of gene action, i.e., additive, partially dominant, dominant, and overdominant modes were observed. In addition, ten additive × additive and six dominance × dominance epistatic interactions were identified. By implementing the general and special combining ability model, we found that prediction accuracy ranged from 0.29 for leaf length to 0.56 for leaf width. Different number of marker analysis showed that ~ 800 markers almost capture the largest prediction accuracies. When incorporating the heterotic quantitative trait loci into the model, we did not find the significant change of prediction accuracy, with only leaf length showing the marginal improvement by 1.7%. CONCLUSIONS: Our results demonstrated that the triple testcross design is suitable for detecting heterotic quantitative trait loci and evaluating the prediction accuracy. Seedling leaf width can be used as the representative trait for seedling prediction. The heterotic quantitative trait loci are not necessary for genomic prediction of seedling biomass-related traits.

A cost-effective barcode system for maize genetic discrimination based on bi-allelic InDel markers.

BACKGROUND: Maize is one of the most important cereal crop all over the world with a complex genome of about 2.3 gigabase, and exhibits tremendous phenotypic and molecular diversity among different germplasms. Along with the phenotype identification, molecular markers have been accepted extensively as an alternative tool to discriminate different genotypes. RESULTS: By using previous re-sequencing data of 205 lines, bi-allelic insertions and deletions (InDels) all over maize genome were screened, and a barcode system was constructed consisting of 37 bi-allelic insertion-deletion markers with high polymorphism information content (PIC) values, large discriminative size among varieties. The barcode system was measured and determined, different maize hybrids and inbreds were clearly discriminated efficiently with these markers, and hybrids responding parents were accurately determined. Compared with microarray data of more than 200 maize lines, the barcode system can discriminate maize varieties with 1.57% of different loci as a threshold. The barcode system can be used in standardized easy and quick operation with very low cost and minimum equipment requirements. CONCLUSION: A barcode system was constructed for genetic discrimination of maize lines, including 37 InDel markers with high PIC values and user-friendly. The barcode system was measured and determined for efficient identification of maize lines.

Analysis of heterosis and quantitative trait loci for kernel shape related traits using triple testcross population in maize.

Kernel shape related traits (KSRTs) have been shown to have important influences on grain yield. The previous studies that emphasize kernel length (KL) and kernel width (KW) lack a comprehensive evaluation of characters affecting kernel shape. In this study, materials of the basic generations (B73, Mo17, and B73 × Mo17), 82 intermated B73 × Mo17 (IBM) individuals, and the corresponding triple testcross (TTC) populations were used to evaluate heterosis, investigate correlations, and characterize the quantitative trait loci (QTL) for six KSRTs: KL, KW, length to width ratio (LWR), perimeter length (PL), kernel area (KA), and circularity (CS). The results showed that the mid-parent heterosis (MPH) for most of the KSRTs was moderate. The performance of KL, KW, PL, and KA exhibited significant positive correlation with heterozygosity but their Pearson's R values were low. Among KSRTs, the strongest significant correlation was found between PL and KA with R values was up to 0.964. In addition, KW, PL, KA, and CS were shown to be significant positive correlation with 100-kernel weight (HKW). 28 QTLs were detected for KSRTs in which nine were augmented additive, 13 were augmented dominant, and six were dominance × additive epistatic. The contribution of a single QTL to total phenotypic variation ranged from 2.1% to 32.9%. Furthermore, 19 additive × additive digenic epistatic interactions were detected for all KSRTs with the highest total R2 for KW (78.8%), and nine dominance × dominance digenic epistatic interactions detected for KL, LWR, and CS with the highest total R2 (55.3%). Among significant digenic interactions, most occurred between genomic regions not mapped with main-effect QTLs. These findings display the complexity of the genetic basis for KSRTs and enhance our understanding on heterosis of KSRTs from the quantitative genetic perspective.