The domestication-associated L1 gene encodes a eucomic acid synthase pleiotropically modulating pod pigmentation and shattering in soybean

GmNF-YC4 delays soybean flowering and maturation by directly repressing GmFT2a and GmFT5a expression

Flowering time and growth period are key agronomic traits which directly affect soybean (Glycine max (L.) Merr.) adaptation to diverse latitudes and farming systems. The FLOWERING LOCUS T (FT) homologs GmFT2a and GmFT5a integrate multiple flowering regulation pathways and significantly advance flowering and maturity in soybean. Pinpointing the genes responsible for regulating GmFT2a and GmFT5a will improve our understanding of the molecular mechanisms governing growth period in soybean. In this study, we identified the Nuclear Factor Y-C (NFY-C) protein GmNF-YC4 as a novel flowering suppressor in soybean under long-day (LD) conditions. GmNF-YC4 delays flowering and maturation by directly repressing the expression of GmFT2a and GmFT5a. In addition, we found that a strong selective sweep event occurred in the chromosomal region harboring the GmNF-YC4 gene during soybean domestication. The GmNF-YC4Hap3 allele was mainly found in wild soybean (Glycine soja Siebold & Zucc.) and has been eliminated from G. max landraces and improved cultivars, which predominantly contain the GmNF-YC4Hap1 allele. Furthermore, the Gmnf-yc4 mutants displayed notably accelerated flowering and maturation under LD conditions. These alleles may prove to be valuable genetic resources for enhancing soybean adaptability to higher latitudes.

Epistasis and pleiotropy-induced variation for plant breeding

Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.

Multifaceted mechanisms controlling grain disarticulation in the Poaceae

Cereal shattering and threshability, both involving disarticulation of grains from the mother plant, are important traits for cereal domestication and improvement. Recent studies highlighted diverse mechanisms influencing shattering and threshability, either through development of the disarticulation zone or floral structures enclosing or supporting the disarticulation unit. Differential lignification in the disarticulation zone is essential for rice shattering but not required for many other grasses. During shattering, the disarticulation zone undergoes either abscission leading to cell separation or cell breakage. Threshability can be affected by the morphology and toughness of the enclosing floral structures, and in some species, by the inherent weakness of the disarticulation zone. Fine-tuning shattering and threshability is essential for breeding wild and less domesticated cereals.

Domestication has altered gene expression and secondary metabolites in pea seed coat

The mature seed in legumes consists of an embryo and seed coat. In contrast to knowledge about the embryo, we know relatively little about the seed coat. We analyzed the gene expression during seed development using a panel of cultivated and wild pea genotypes. Gene co-expression analysis identified gene modules related to seed development, dormancy, and domestication. Oxidoreductase genes were found to be important components of developmental and domestication processes. Proteomic and metabolomic analysis revealed that domestication favored proteins involved in photosynthesis and protein metabolism at the expense of seed defense. Seed coats of wild peas were rich in cell wall-bound metabolites and the protective compounds predominated in their seed coats. Altogether, we have shown that domestication altered pea seed development and modified (mostly reduced) the transcripts along with the protein and metabolite composition of the seed coat, especially the content of the compounds involved in defense. We investigated dynamic profiles of selected identified phenolic and flavonoid metabolites across seed development. These compounds usually deteriorated the palatability and processing of the seeds. Our findings further provide resources to study secondary metabolism and strategies for improving the quality of legume seeds which comprise an important part of the human protein diet.

Chemical composition analysis and transcriptomics reveal the R2R3-MYB genes and phenol oxidases regulating the melanin formation in black radish

Melanins are dark-brown to black-colored biomacromolecules which have been thoroughly studied in animals and microorganisms. However, the biochemical and molecular basis of plant melanins are poorly understood. We first characterized melanin from the black radish (Raphanus sativus var. niger) 'HLB' through spectroscopic techniques. p-Coumaric acid was identified as the main precursor of radish melanin. Moreover, a joint analysis of transcriptome and coexpression network was performed for the two radish accessions with black and white cortexes, 'HLB' and '55'. A set of R2R3-type RsMYBs and enzyme-coding genes exhibited a coexpression pattern, and were strongly correlated with melanin formation in radish. Transient overexpression of two phenol oxidases RsLAC7 (laccase 7) or RsPOD22-1 (peroxidase 22-1) resulted in a deeper brown color around the infiltration sites and a significant increase in the total phenol content. Furthermore, co-injection of the transcriptional activator RsMYB48/RsMYB97 with RsLAC7 and/or RsPOD22-1, markedly increased the yield of black extracts. Spectroscopic analyses revealed that these extracts are similar to the melanin found in 'HLB'. Our findings advance the understanding of structural information and the transcriptional regulatory mechanism underlying melanin formation in radish.

Integrated metabolomic and transcriptomic dynamic profiles of endopleura coloration during fruit maturation in three walnut cultivars

BACKGROUND

The color of endopleura is a vital factor in determining the economic value and aesthetics appeal of nut. Walnuts (Juglans) are a key source of edible nuts, high in proteins, amino acids, lipids, carbohydrates. Walnut had a variety endopleura color as yellow, red, and purple. However, the regulation of walnut endopleura color remains little known.

RESULTS

To understand the process of coloration in endopleura, we performed the integrative analysis of transcriptomes and metabolomes at two developmental stages of walnut endopleura. We obtained total of 4,950 differentially expressed genes (DEGs) and 794 metabolites from walnut endopleura, which are involved in flavonoid and phenolic biosynthesis pathways. The enrichment analysis revealed that the cinnamic acid, coniferyl alcohol, naringenin, and naringenin-7-O-glucoside were important metabolites in the development process of walnut endopleura. Transcriptome and metabolome analyses revealed that the DEGs and differentially regulated metabolites (DRMs) were significantly enriched in flavonoid biosynthesis and phenolic metabolic pathways. Through co-expression analysis, CHS (chalcone synthase), CHI (chalcone isomerase), CCR (cinnamoyl CoA reductase), CAD (cinnamyl alcohol dehydrogenase), COMT (catechol-Omethyl transferase), and 4CL (4-coumaroyl: CoA-ligase) may be the key genes that potentially regulate walnut endopleura color in flavonoid biosynthesis and phenolic metabolic pathways.

CONCLUSIONS

This study illuminates the metabolic pathways and candidate genes that underlie the endopleura coloration in walnuts, lay the foundation for further study and provides insights into controlling nut's colour.

Natural and artificial selection of multiple alleles revealed through genomic analyses

Genome-to-phenome research in agriculture aims to improve crops through in silico predictions. Genome-wide association study (GWAS) is potent in identifying genomic loci that underlie important traits. As a statistical method, increasing the sample quantity, data quality, or diversity of the GWAS dataset positively impacts GWAS power. For more precise breeding, concrete candidate genes with exact functional variants must be discovered. Many post-GWAS methods have been developed to narrow down the associated genomic regions and, ideally, to predict candidate genes and causative mutations (CMs). Historical natural selection and breeding-related artificial selection both act to change the frequencies of different alleles of genes that control phenotypes. With higher diversity and more extensive GWAS datasets, there is an increased chance of multiple alleles with independent CMs in a single causal gene. This can be caused by the presence of samples from geographically isolated regions that arose during natural or artificial selection. This simple fact is a complicating factor in GWAS-driven discoveries. Currently, none of the existing association methods address this issue and need to identify multiple alleles and, more specifically, the actual CMs. Therefore, we developed a tool that computes a score for a combination of variant positions in a single candidate gene and, based on the highest score, identifies the best number and combination of CMs. The tool is publicly available as a Python package on GitHub, and we further created a web-based Multiple Alleles discovery (MADis) tool that supports soybean and is hosted in SoyKB (https://soykb.org/SoybeanMADisTool/). We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. Finally, we identified a candidate gene for the pod color L2 locus and predicted the existence of multiple alleles that potentially cause loss of pod pigmentation. In this work, we show how a genomic analysis can be employed to explore the natural and artificial selection of multiple alleles and, thus, improve and accelerate crop breeding in agriculture.