Search for: All records

Abstract Seed storage proteins (SSPs) are of great importance in plant science and agriculture, particularly in cereal crops, due to their nutritional value and their impact on food properties. During seed maturation, massive amounts of SSPs are synthesized and deposited either within protein bodies derived from the endoplasmic reticulum, or into specialized protein storage vacuoles (PSVs). The processing and trafficking of SSPs vary among plant species, tissues, and even developmental stages, as well as being influenced by SSP composition. The different trafficking routes, which affect the amount of SSPs that seeds accumulate and their composition and modifications, rely on a highly dynamic and functionally specialized endomembrane system. Although the general steps in SSP trafficking have been studied in various plants, including cereals, the detailed underlying molecular and regulatory mechanisms are still elusive. In this review, we discuss the main endomembrane routes involved in SSP trafficking to the PSV in Arabidopsis and other eudicots, and compare and contrast the SSP trafficking pathways in major cereal crops, particularly in rice and maize. In addition, we explore the challenges and strategies for analyzing the endomembrane system in cereal crops. 

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder’s toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction’s potential to a set of 482 pea ( Pisum sativum L.) accessions—genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components—for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.