Kafirins are the major storage proteins in sorghum (Sorghum bicolor) grains and form protein bodies with poor digestibility. Since kafirins are devoid of the essential amino acid lysine, they also impart poor protein quality to the kernel. The α-kafirins, which make up most of the total kafirins, are largely encoded by the k1C family of highly similar genes. We used a clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) gene editing approach to target the k1C genes to create variants with reduced kafirin levels and improved protein quality and digestibility. A single guide RNA was designed to introduce mutations in a conserved region encoding the endoplasmic reticulum signal peptide of α-kafirins. Sequencing of kafirin PCR products revealed extensive edits in 25 of 26 events in one or multiple k1C family members. T1 and T2 seeds showed reduced α-kafirin levels, and selected T2 events showed significantly increased grain protein digestibility and lysine content. Thus, a single consensus single guide RNA carrying target sequence mismatches is sufficient for extensive editing of all k1C genes. The resulting quality improvements can be deployed rapidly for breeding and the generation of transgene-free, improved cultivars of sorghum, a major crop worldwide.

A metabolic network-guided genome-wide association study of seed free amino acids facilitates the identification of a histidine-specific transporter in Arabidop

MGWAS of Gln-related traits reveals an unexpected seed-specific interaction between glutamine and glucosinolates and in shaping the metabolic homeostasis in Ara

Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.

Advanced publicly available sequencing data from large populations have enabled informative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis such as outlier removal, data transformation, and calculation of Best Linear Unbiased Predictions (BLUPs) or Best Linear Unbiased Estimates (BLUEs). In addition, post-GWAS analysis such as haploblock analysis and candidate gene identification are lacking. Here, we present HAPPI GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS, and post-GWAS analysis in an automated pipeline using the command-line interface. HAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git). Supplementary data are available at Bioinformatics online.