Henan Academy of Agricultural Sciences

BackgroundFlower color plays a crucial role in plant identification, evaluation, and evolution, and it is directly or indirectly associated with the agronomic characteristics of plants. Alfalfa (Medicago sativa L.) is an important leguminous forage grass, it exhibits a continuous gradient of purple flowers, but the regulatory mechanisms underlying this quantitative variation remain poorly understood.MethodsWe integrated metabolomics and transcriptomics analyses to investigate five alfalfa flower types: white (BH) and four shades of purple (ZH1-ZH4, from light to dark). Anthocyanin and carotenoid contents were quantified by LC-ESI-MS/MS. Differentially accumulated pigments were subjected to KEGG enrichment and statistical analysis. RNA-seq reads were mapped to the Medicago reference genome, and differentially expressed genes (DEGs) were identified using DESeq2. Common significant DEGs (CSDEGs) among the four purple-versus-white comparisons were clustered to identify expression trends. Quantitative Real-time PCR (qRT-PCR) was performed to validate the transcriptome data, and an O2PLS model was used to integrate metabolomic and transcriptomic datasets.ResultsMetabolomics analysis revealed that delphinidin-3, 5-O-diglucoside and malvidin-3, 5-O-diglucoside were the two most abundant anthocyanins, and their contents increased steadily from ZH1 to ZH4. The ratio of blue to red pigments decreased along the same gradient. Transcriptomics identified 580 CSDEGs, of which 108 showed a regular trend across the five flower types, and 24 were transcription factors, with MYB and bHLH being the most represented. CSDEGs are involved in multiple processes such as stimulation of the internal and external cellular environment, pigment synthesis and regulation, pigment transport and deposition. Notably, 29 genes in CSDEGs were definitively involved in the anthocyanin biosynthesis chain, and some important candidate genes regulating the formation of purple color in alfalfa flowers were identified. Joint analysis of the two datasets revealed that the relative expression levels of two novel genes (MSTRG.59017, MSTRG.14861) were positively correlated with the contents changes of delphinidin-3, 5-O-diglucoside or malvidin-3, 5-O-diglucoside in BH, ZH1, ZH2, ZH3, and ZH4.ConclusionAnthocyanins are the dominant pigments responsible for the purple coloration of alfalfa flowers. Delphinidin and malvidin derivatives, especially delphinidin-3, 5-O-diglucoside and malvidin-3, 5-O-diglucoside, are the key molecules driving the purple gradient, as well as the proportional relationship between blue and red pigments. Genes responding to the intracellular and extracellular environments influencing the purple formation of alfalfa flowers, involved in transcriptional, translational and post-translational regulation of anthocyanin biosynthesis, and involved in synthesis, transport and deposition of anthocyanin were screened out. We confirmed that there should be key genes specific to the synthesis of each anthocyanin glycoside, which will be the focus of future research. This study has established a theoretical foundation for explaining the different purple color of alfalfa flowers. This study enriches the theory of plant flower color formation and provides a theoretical basis for alfalfa flower color improvement and molecular breeding.

The role of platelet-derived growth factor D (PDGFD) in mesenchymal cells is well-established, but its specific function in skeletal muscle generation remains unknown. This study reveals for the first time PDGFD's dual regulatory role in myogenesis: it acts both as a "guardian" maintaining the myoblast pool and as an "initiator" driving myogenic differentiation. Through single-cell RNA sequencing analysis of skeletal muscle from Jiangquan Black pigs, we identified PDGFD as a common candidate gene for both muscle and fat development. In the C2C12 cell model, PDGFD knockdown significantly inhibited cell proliferation and promoted apoptosis, while overexpression enhanced viability and inhibited apoptosis, indicating its critical role in maintaining myoprogenic precursor cell homeostasis. Further studies revealed that PDGFD interference downregulated key myogenic differentiation markers MyoD and MyoG, inhibiting differentiation. Its expression peaked during mid-differentiation (D5), suggesting temporal regulation of differentiation. Interestingly, although PDGFD primarily acts through the PI3K/Akt pathway downstream of PDGFR-β, PDGFD knockdown did not show significant synergistic effects with PI3K/Akt pathway activation in inhibiting differentiation. This suggests PDGFD may specifically regulate myogenic differentiation via an independent or parallel signaling axis. This study not only expands the known functions of PDGFD in muscle biology but also provides new insights into the mechanisms by which growth factors coordinate cell fate decisions.

To optimize the structural parameters of a peanut digging shovel and enhance its operational performance, the forces exerted on the digging shovel were examined through a graphical mechanics approach. This analysis identified the primary structural and operational parameters of the shovel’s design. A numerical simulation model for the working resistance of the shovel was established adopting EDEM (2018) discrete element analysis software and subsequently validated through comparative analysis with field experiment results. Employing the Box–Behnken response surface method, quadratic regression models were constructed with digging resistance and soil non-breakage ratio as the response variables, while forward speed, soil entry angle, and blade tilt angle were taken as the influencing factors. Optimization analysis of these parameters was conducted. The optimization results indicate that with a forward speed of 0.8 m/s, a soil entry angle of 20°, and a blade tilt angle of 40°, the working resistance of the shovel is 1667 N, and the soil non-breakage ratio is 20.56%. The error between the field test results and the predictions from the optimized model was less than 2%, illustrating the feasibility of the model and the optimization outcomes. This study offers a technical reference for future simulation-based optimization of peanut digging shovels.