Zygomorphic flowers, with bilateral (dorsoventral) symmetry, are considered to have evolved several times independently in flowering plants. In Antirrhinum majus , floral dorsoventral symmetry depends on the activity of two TCP-box genes, CYCLOIDEA ( CYC ) and DICHOTOMA ( DICH ). To examine whether the same molecular mechanism of floral asymmetry operates in the distantly related Rosid clade of eudicots, in which asymmetric flowers are thought to have evolved independently, we investigated the function of a CYC homologue LjCYC2 in a papilionoid legume, Lotus japonicus . We showed a role for LjCYC2 in establishing dorsal identity by altering its expression in transgenic plants and analyzing its mutant allele squared standard 1 ( squ1 ). Furthermore, we identified a lateralizing factor, Keeled wings in Lotus 1 ( Kew1 ), which plays a key role in the control of lateral petal identity, and found LjCYC2 interacted with Kew1 , resulting in a double mutant that bore all petals with ventralized identity to some extents. Thus, we demonstrate that CYC homologues have been independently recruited as determinants of petal identities along the dorsoventral axis in two distant lineages of flowering plants, suggesting a common molecular origin for the mechanisms controlling floral zygomorphy.

Floral zygomorphy (flowers with bilateral symmetry) has multiple origins and typically manifests two kinds of asymmetries, dorsoventral (DV) and organ internal (IN) asymmetries in floral and organ planes, respectively, revealing the underlying key regulators in plant genomes that generate and superimpose various mechanisms to build up complexity and different floral forms during plant development. In this study, we investigate the loci affecting these asymmetries during the development of floral zygomorphy in pea ( Pisum sativum L.). Two genes, LOBED STANDARD 1 ( LST1 ) and KEELED WINGS ( K ), were cloned that encode TCP transcription factors and have divergent functions to constitute the DV asymmetry. A previously undescribed regulator, SYMMETRIC PETALS 1 ( SYP1 ), has been isolated as controlling IN asymmetry. Genetic analysis demonstrates that DV and IN asymmetries could be controlled independently by the two kinds of regulators in pea, and their interactions help to specify the type of zygomorphy. Based on the genetic analysis in pea, we suggest that variation in both the functions and interactions of these regulators could give rise to the wide spectrum of floral symmetries among legume species and other flowering plants.

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

Understanding evolutionary change requires phenotypic differences between organisms to be placed in a genetic context. However, there are few cases where it has been possible to define an appropriate genotypic space for a range of species. Here we address this problem by defining a genetically controlled space that captures variation in shape and size between closely related species of Antirrhinum . The axes of the space are based on an allometric model of leaves from an F ₂ of an interspecific cross between Antirrhinum majus and Antirrhinum charidemi . Three principal components were found to capture most of the genetic variation in shape and size, allowing a three-dimensional allometric space to be defined. The contribution of individual genetic loci was determined from QTL analysis, allowing each locus to be represented as a vector in the allometric space. Leaf shapes and sizes of 18 different Antirrhinum taxa, encompassing a broad range of leaf morphologies, could be accurately represented as clouds within the space. Most taxa overlapped with, or were near to, at least one other species in the space, so that together they defined a largely interconnected domain of viable forms. It is likely that the pattern of evolution within this domain reflects a combination of directional selection and evolutionary tradeoffs within a high dimensional space.

Soybean has undergone extensive selection pressures for seed nutrient composition and seed color during domestication, but the major genetic loci controlling seed coat color have not been completely understood, and the transcriptional regulation relationship among the loci remains elusive. Here, two major regulators, GmMYBA2 and GmMYBR, were functionally characterized as an anthocyanin activator and repressor, respectively. Ectopic expression of GmMYBA2 in soybean hairy roots conferred the enhanced accumulation of delphinidin and cyanidin types of anthocyanins in W1t and w1T backgrounds, respectively, through activating anthocyanin biosynthetic genes in the reported loci. The seed coat pigmentation of GmMYBA2-overexpressing transgenic plants in the W1 background mimicked the imperfect black phenotype (W1/w1, i, R, t), suggesting that GmMYBA2 was responsible for the R locus. Molecular and biochemical analysis showed that GmMYBA2 interacted with GmTT8a to directly activate anthocyanin biosynthetic genes. GmMYBA2 and GmMYBR might form a feedback loop to fine-tune seed coat coloration, which was confirmed in transgenic soybeans. Both GmTT8a and GmMYBR that were activated by GmMYBA2 in turn enhanced and obstructed the formation of the GmMYBA2–GmTT8a module, respectively. The results revealed the sophisticated regulatory network underlying the soybean seed coat pigmentation loci and shed light on the understanding of the seed coat coloration and other seed inclusions.