Metabolomics and transcriptomics strategies to reveal the mechanism of diversity of maize kernel color and quality

BACKGROUND

Maize has many kernel colors, from white to dark black. However, research on the color and nutritional quality of the different varieties is limited. The color of the maize grain is an important characteristic. Colored maize is rich in nutrients, which have received attention for their role in diet-related chronic diseases and have different degrees of anti-stress protection for animal and human health.

METHODS

A comprehensive metabolome (LC-MS/MS) and transcriptome analysis was performed in this study to compare different colored maize varieties from the perspective of multiple recombination in order to study the nutritional value of maize with different colors and the molecular mechanism of color formation.

RESULTS

Maize kernels with diverse colors contain different types of health-promoting compounds, highlighting that different maize varieties can be used as functional foods according to human needs. Among them, red-purple and purple-black maize contain more flavonoids than white and yellow kernels. Purple-black kernels have a high content of amino acids and nucleotides, while red-purple kernels significantly accumulate sugar alcohols and lipids.

CONCLUSION

Our study can provide insights for improving people's diets and provide a theoretical basis for the study of food structure for chronic diseases.

Corn360: a method for quantification of corn kernels

BACKGROUND

The rapidly advancing corn breeding field calls for high-throughput methods to phenotype corn kernel traits to estimate yield and to study their genetic inheritance. Most of the existing methods are reliant on sophisticated setup, expertise in statistical models and programming skills for image capturing and analysis.

RESULTS

We demonstrated a portable, easily accessible, affordable, panoramic imaging capturing system called Corn360, followed by image analysis using freely available software, to characterize total kernel count and different patterned kernel counts of a corn ear. The software we used did not require programming skills and utilized Artificial Intelligence to train a model and to segment the images of mixed patterned corn ears. For homogeneously patterned corn ears, our results showed accuracies of 93.7% of total kernel count compared to manual counting. Our method allowed to save an average of 3 min 40 s per image. For mixed patterned corn ears, our results showed accuracies of 84.8% or 61.8% of segmented kernel counts. Our method has the potential to greatly decrease counting time per image as the number of images increases. We also demonstrated a case of using Corn360 to count different categories of kernels on a mixed patterned corn ear resulting from a cross of sweet corn and sticky corn and showed that starch:sweet:sticky segregated in a 9:4:3 ratio in its F2 population.

CONCLUSIONS

The panoramic Corn360 approach enables for a portable low-cost high-throughput kernel quantification. This includes total kernel quantification and quantification of different patterned kernels. This can allow for quick estimate of yield component and for categorization of different patterned kernels to study the inheritance of genes controlling color and texture. We demonstrated that using the samples resulting from a sweet × sticky cross, the starchiness, sweetness and stickiness in this case were controlled by two genes with epistatic effects. Our achieved results indicate Corn360 can be used to effectively quantify corn kernels in a portable and cost-efficient way that is easily accessible with or without programming skills.

Genome-wide association mapping and genomic prediction for kernel color traits in intermediate wheatgrass (Thinopyrum intermedium)

BACKGROUND

Intermediate wheatgrass (IWG) is a novel perennial grain crop currently undergoing domestication. It offers important ecosystem benefits while producing grain suitable for human consumption. Several aspects of plant biology and genetic control are yet to be studied in this new crop. To understand trait behavior and genetic characterization of kernel color in IWG breeding germplasm from the University of Minnesota was evaluated for the CIELAB components (L*, a*, b*) and visual differences. Trait values were used in a genome-wide association scan to reveal genomic regions controlling IWG's kernel color. The usability of genomic prediction in predicting kernel color traits was also evaluated using a four-fold cross validation method.

RESULTS

A wide phenotypic variation was observed for all four kernel color traits with pairwise trait correlations ranging from - 0.85 to 0.27. Medium to high estimates of broad sense trait heritabilities were observed and ranged from 0.41 to 0.78. A genome-wide association scan with single SNP markers detected 20 significant marker-trait associations in 9 chromosomes and 23 associations in 10 chromosomes using multi-allelic haplotype blocks. Four of the 20 significant SNP markers and six of the 23 significant haplotype blocks were common between two or more traits. Evaluation of genomic prediction of kernel color traits revealed the visual score to have highest mean predictive ability (r² = 0.53); r² for the CIELAB traits ranged from 0.29-0.33. A search for candidate genes led to detection of seven IWG genes in strong alignment with MYB36 transcription factors from other cereal crops of the Triticeae tribe. Three of these seven IWG genes had moderate similarities with R-A1, R-B1, and R-D1, the three genes that control grain color in wheat.

CONCLUSIONS

We characterized the distribution of kernel color in IWG for the first time, which revealed a broad phenotypic diversity in an elite breeding germplasm. Identification of genetic loci controlling the trait and a proof-of-concept that genomic selection might be useful in selecting genotypes of interest could help accelerate the breeding of this novel crop towards specific end-use.

Chromosome-level genome assembly of a regenerable maize inbred line A188

BACKGROUND

The maize inbred line A188 is an attractive model for elucidation of gene function and improvement due to its high embryogenic capacity and many contrasting traits to the first maize reference genome, B73, and other elite lines. The lack of a genome assembly of A188 limits its use as a model for functional studies.

RESULTS

Here, we present a chromosome-level genome assembly of A188 using long reads and optical maps. Comparison of A188 with B73 using both whole-genome alignments and read depths from sequencing reads identify approximately 1.1 Gb of syntenic sequences as well as extensive structural variation, including a 1.8-Mb duplication containing the Gametophyte factor1 locus for unilateral cross-incompatibility, and six inversions of 0.7 Mb or greater. Increased copy number of carotenoid cleavage dioxygenase 1 (ccd1) in A188 is associated with elevated expression during seed development. High ccd1 expression in seeds together with low expression of yellow endosperm 1 (y1) reduces carotenoid accumulation, accounting for the white seed phenotype of A188. Furthermore, transcriptome and epigenome analyses reveal enhanced expression of defense pathways and altered DNA methylation patterns of the embryonic callus.

CONCLUSIONS

The A188 genome assembly provides a high-resolution sequence for a complex genome species and a foundational resource for analyses of genome variation and gene function in maize. The genome, in comparison to B73, contains extensive intra-species structural variations and other genetic differences. Expression and network analyses identify discrete profiles for embryonic callus and other tissues.

Exome QTL-seq maps monogenic locus and QTLs in barley

Background

QTL-seq, in combination with bulked segregant analysis and next-generation sequencing (NGS), is used to identify loci in small plant genomes, but is technically challenging to perform in species with large genomes, such as barley. A combination of exome sequencing and QTL-seq (exome QTL-seq) was used to map the mono-factorial Mendelian locus black lemma and pericarp (Blp) and QTLs for resistance to net blotch disease, a common disease of barley caused by the fungus Pyrenophora teres, which segregated in a population of 100 doubled haploid barley lines.

Methods

The provisional exome sequences were prepared by ordering the loci of expressed genes based on the genome information and concatenating genes with intervals of 200-bp spacer "N" for each chromosome. The QTL-seq pipeline was used to analyze short reads from the exome-captured library.

Results

In this study, short NGS reads of bulked total DNA samples from segregants with extreme trait values were subjected to exome capture, and the resulting exome sequences were aligned to the reference genome. SNP allele frequencies were compared to identify the locations of genes/QTLs responsible for the trait value differences between lines. For both objective traits examined, exome QTL-seq identified the monogenic Mendelian locus and associated QTLs. These findings were validated using conventional mapping approaches.

Conclusions

Exome QTL-seq broadens the utility of NGS-based gene/QTL mapping in organisms with large genomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3511-2) contains supplementary material, which is available to authorized users.