Grafting has been widely used to improve horticultural traits. It has also served increasingly as a tool to investigate the long-distance transport of molecules that is an essential part for key biological processes. Many studies have revealed the molecular mechanisms of graft-induced phenotypic variation in anatomy, morphology and production. Here, we review the phenomena and their underlying mechanisms by which macromolecules, including RNA, protein, and even DNA, are transported between scions and rootstocks via vascular tissues. We further propose a conceptual framework that characterizes and quantifies the driving mechanisms of scion-rootstock interactions toward vascular reconnection and regeneration.

Summary Allopolyploid Brassica juncea crops in Brassicaceae are becoming increasingly revitalized as vegetables and oilseeds owing to wide adaptability and significant economic values. However, the genomic differentiation of diversified vegetables and oilseed B. juncea and the genetic basis underlying glucosinolates accumulation have yet to be elucidated. To address this knowledge gap, we report the sequencing of pairwise genomes of vegetable and oilseed B. juncea at chromosome scale. Comparative genomics analysis unveils panoramic structural variation footprints, particularly the genetic loci of HSP20 and TGA1 associated with abiotic and biotic stresses responses between oilseed and vegetable subgroups. We anchored two major loci of MYB28 (HAG1) orthologues caused by copy number variations on A02 and A09 chromosomes using scored genomic SNPs‐based GWAS that are responsible for seed oil quality‐determining glucosinolates biosynthesis. These findings will provide valuable repertories of polyploidy genomic information enabling polyploidy genome evolution studies and precise genomic selections for crucial traits like functional components of glucosinolates in B. juncea crops and beyond.

Bactrocera dorsalis is an invasive polyphagous pest causing considerable ecological and economic damage worldwide. We report a high-quality chromosome-level genome assembly and combine various transcriptome data to explore the molecular mechanisms of its rapid adaptation to new environments. The expansions of the DDE transposase superfamily and key gene families related to environmental adaptation and enrichment of the expanded and unique gene families in metabolism and defence response pathways explain its environmental adaptability. The relatively high but not significantly different expression of heat-shock proteins, regardless of the environmental conditions, suggests an intrinsic mechanism underlying its adaptation to high temperatures. The mitogen-activated protein kinase pathway plays a key role in adaptation to new environments. The prevalence of duplicated genes in its genome explains the diversity in the B. dorsalis complex. These findings provide insights into the genetic basis of the invasiveness and diversity of B. dorsalis, explaining its rapid adaptation and expansion.

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Crop modeling, a widely used tool to predict plant growth and development in heterogeneous environments, has been increasingly integrated with genetic information to improve its predictability. This integration can also shed light on the mechanistic path that connects the genotype to a particular phenotype under specific environments. We implemented a bivariate statistical procedure to map and identify quantitative trait loci (QTLs) that can predict the form of plant growth by estimating cultivar-specific growth parameters and incorporating these parameters into a mapping framework. The procedure enables the characterization of how QTLs act differently in response to developmental and environmental cues. We used this procedure to map growth parameters of leaf area and mass in a mapping population of the common bean (Phaseolus vulgaris L.). Different sets of QTLs are responsible for various aspects of growth, including the initiation time of growth, growth rate, inflection point and asymptotic growth. A major QTL of a large effect was identified to pleiotropically affect trait expression in distinct environments and different traits expressed on the same organism. The integration of crop models and QTL mapping through our statistical procedure provides a powerful means of building a more precise predictive model of genotype-phenotype relationships for crops.