Nowadays, there are few reports about regulatory genes implicated in peel color of longan. The basic genetic research of longan has been in stagnation for a long time as a lack of transcriptomic and genetic information. To predict candidate genes associated with peel color, Gene Functional Annotation and Coding Sequence prediction were used to perform functional annotation for our assembled unigenes and investigate differentially expressed genes (DEGs) of fruitlet peels from Longli (Dimocarpus confinis). Finally, a total of 24,044 (44.19 %) unigenes were annotated at least in one database after BLAST search to NCBI non-redundant protein sequence, NCBI non-redundant nucleotide sequences, Kyoto Encyclopedia of Genes and Genomes (KEGG) Ortholog, manually annotated and reviewed protein sequence database (Swiss-Prot), Protein family, Gene Ontology, euKaryotic Ortholog Groups databases. After searching against the KEGG-GENE protein database, a result of 6228 (11.45 %) unigenes were assigned to 245 KEGG pathways. Via comparing the distributions of expression value of all corresponding unigenes from red peel and green peel fruit, it could be intuitively concluded that high similarity was existed in the two distributions; however, on the whole, between two distributions of log RPKM expression value, some differences indicated that expression level in green-peel fruit group is slightly higher than values in red-peel fruit group. Finally, a total of 1349 unigenes were identified as DEGs after blasting the DEGs to public sequence databases, and 32 peel-color-related genes were identified in longan. Our results suggest that a number of unigenes involved in longan metabolic process, including anthocyanin biosynthesis. In addition, DRF, F3H, ANS, CYP75A1 and C1 may be the key ones. The study on key genes related to peel color will be contributed to revealing the molecular mechanisms of regulating peel color in woody plants.

Domestication has been proven to significantly impact the biosynthesis of plant secondary metabolites. Cultivated green jujube (Ziziphus mauritiana Lam.), as an important autotetraploid fruit crop widely planted in tropical regions, exhibits differential physicochemical traits compared with its wild progenitor. To assess the traits lost in cultivated green jujube during domestication, the study performed comprehensive genomic, transcriptomic and metabolomic investigations of flavonoid pathways in wild and cultivated green jujube. Based on the four haplotype genomes of wild and cultivated green jujube, for the first time, the study bulk-identified 16 key gene families associated with flavonoid biosynthesis. Collinearity analysis revealed that tandem duplication was the predominant event in flavonoid-related genes rather than WGD. Through the expression profiles in different tissues, the distinct member of these gene families was classified as “redundant” or “functional”. Transcriptomic analyses illustrated the significant differential expressions (p < 0.05) of 13 flavonoid-related gene families in fruits of six cultivated and three wild green jujube accessions, except for FLS, LAR and PPO. The wild green jujube fruits accumulated more abundance of flavonoid metabolites than in cultivated fruits (p < 0.0001), as evidenced by upregulated chalcones, dihydroflavonol, isoflavones and flavonoid carbonoside. Gene–metabolite co-expression modules further validated the potential transcription regulators, such as BBX21, WRI1 and bZIP44. Together, the study suggested a genomic, transcriptomic and metabolic perspective for domestication regarding fruit flavonoid pathways in green jujube, which provides a valuable genetic resource for fruit quality improvement in cultivated green jujube.

A framework beyond single‐reference genomes is needed to understand transcription factor evolution. This study employed an integrated haplotype‑resolved genomes–transcriptome atlas–machine learning to characterize the transcription factors of autotetraploid green jujube ( Ziziphus mauritiana ). The first haplotype‑resolved comparison of transcription factor superfamilies from eight haplotype genomes (HapGenome) representing a specific wild and a specific cultivated green jujube accession, encompassing 42 superfamilies and 12,123 gene copies. Evolutionary analyses revealed high structural conservation with minimal copy number variation, gene presence/absence variations, and strong purifying selection (Ka/Ks < 1). Dispersed duplication (47.24%), not whole‐genome duplication (36.10%), was the most frequently observed duplication event in the expansion of transcription factor superfamily. A haplotype‑resolved transcriptome atlas demonstrated that tissue‐specific expression divergence occurred at the superfamily level and between the core/dispensable genes. Integrating transcriptomic and metabolomic data, support vector machine classification with leave‑one‑out cross‑validated distinguished three wild fruits from six cultivated fruits with the accuracy of 89% using orthologous gene groups (OGGs) expression profiles. The eXtreme gradient boosting was employed as an exploratory tool to prioritize OGGs related to metabolite changes. Finally, OGG‐95, a Lesion Simulating Disease Zn finger transcription factor, was screened out, which was significantly upregulated in cultivated fruits, and its expression was significantly correlated with differential accumulation of nucleotides and organic acids that need further functional validation. This integrative study provided novel insights into the genomic architecture and regulatory evolution of transcription factors in a polyploid fruit crop, highlighting the power of multi‐dimensional analyses for gene discovery.

Green jujube (Ziziphus mauritiana) is an economically important fruit tree with exceptional nutritional and medicinal values, particularly in tropical unde

This study investigated the effects of solid-state fermentation with a compound microbial inoculant on the nutritional composition, microbial community structure, and metabolic products of green jujube (Ziziphus mauritiana Lam.) straw. The results demonstrated that solid-state fermentation significantly enhanced the nutritional value of the straw, as evidenced by a marked increase in crude protein content and significant reductions in neutral detergent fiber (NDF), acid detergent fiber (ADF), and cellulose content. Metagenomic analysis revealed that fermentation substantially altered the microbial community structure of the straw, with a pronounced increase in the relative abundance of bacteria from the phylum Pseudomonadota (particularly genera such as Klebsiella and Enterobacter), and an upward trend in the abundance of fungi from the phylum Basidiomycota (Astraeus). Functional annotation indicated that fermentation enhanced the potential of the straw microbiota in genetic information processing, ABC transporters, and starch and sucrose metabolism, while attenuating the oxidative phosphorylation pathway. Metabolomic analysis identified 1176 differential metabolites, with significant increases in bioactive compounds such as peptides, amino acids, polyunsaturated fatty acids, and flavonoids following fermentation. Correlation analysis further revealed significant associations between specific microorganisms (Klebsiella, Enterobacter, and Aureobasidium) and key metabolites (amino acids, peptides, and flavonoids) in the fermented green jujube straw. This study confirms that solid-state fermentation can effectively improve the nutritional value and functional properties of the agricultural by-product green jujube straw by reshaping its microbial ecosystem and metabolic network.

The grading of green jujube is a key factor in improving production efficiency and market competitiveness. However, traditional grading methods are inefficient, imprecise, and struggle to detect minor damages. This study proposes an improved BCW-YOLO deep learning model specifically designed for automated grading of green jujube. The model integrates a Bidirectional Feature Pyramid Network (BiFPN) and a Contextual Transformer Attention (COT) mechanism to enhance feature fusion accuracy and capture fine-grained details. In addition, the WIoU v3 loss function is introduced to optimize object localization performance. By constructing a multi-angle green jujube dataset and applying data augmentation techniques, the model’s generalization capability was significantly improved. The results of the experiement indicate that the improved BCW-YOLO achieves precision, recall, mAP, and F1 score of 90.87%, 92.12%, 95.66%, and 91.49%, respectively, representing increases of 1.93%, 2.77%, 1.58%, and 2.34% compared to the original YOLO model. Through comprehensive validation using confusion matrices, PR curves, heatmap analyses, and ablation studies, the model’s performance was thoroughly verified. Compared with other YOLO series models, BCW-YOLO performs exceptionally well in detecting minor damages, demonstrating its potential in practical agricultural grading. The findings provide a new technical approach for precise grading and automated sorting of green jujube, showing promising application prospects.