Sesame is one of the oldest oil crops in the world and is widely grown in the tropical and subtropical areas of Asia, Africa and America. Upon the completion of the sesame reference genome version 1.0, we launched Sinbase 1.0 as an integrated database for genomic and bioinformatics analyses. Recently, an upgraded version (version 2.0) of the genome sequence was released. In addition, large numbers of multi-omics data have been generated on sesame, but a comprehensive database that integrates these resources for the community has been lacking until now. Here, we developed an interactive and comprehensive sesame multi-omics database, Sinbase 2.0, which provides information of the sesame updated genome containing 13 chromosomes, 3 genetic linkage maps, 5 intra- and 6 inter-species comparative genomics, 1 genomic variation analysis, 5 transcriptome data, 1 proteome, 31 functional markers, 175 putative functional genes, and 54 QTLs detected for important agronomic traits. Moreover, Sinbase 2.0 has been enriched with novel user-friendly computational tools. All datasets of Sinbase 2.0 can be downloaded online conveniently. Sinbase 2.0 will be updated regularly with new available sesame multi-omics data and can be accessed freely via Sinbase 2.—Sesame Muti-Omics Database. We expect that Sinbase 2.0, similarly to the previous version, will continue to make a major contribution to advance sesame research towards a better understanding of its biology and genetic improvement, as well as comparative genomics and evolutionary biology.

Accurate monitoring of photosynthetic phenotypes is fundamental for breeding high photosynthetic-efficiency wheat cultivars, and UAV remote sensing provides an effective approach for their large-scale identification. However, single photosynthetic parameters are limited in comprehensively evaluating crop photosynthetic efficiency. This study integrated multiple photosynthetic phenotypic parameters using principal component analysis (PCA) and the CRITIC objective weighting method (PCA-CRITIC) to construct a Comprehensive High Photosynthetic-Efficiency Phenotypic Index (CHPPI) for winter wheat. Concurrently, multispectral vegetation indices (MSVI), RGB vegetation indices (RGBVI), texture features (TF), and their combination, Comprehensive Multispectral-Visible-Texture Features (CMVTF), were extracted from UAV multimodal remote sensing data. Based on these features, four feature selection methods, namely PCC, VIP, SPA, and UVE, were evaluated in combination with five machine learning algorithms, including KNN, DT, SVR, RF, and XGBoost, to precisely estimate the CHPPI across key growth stages. The results demonstrated that the CHPPI exhibited significant cultivar variations across growth stages. Cultivars SH06144 and Liangxing19 consistently maintained high photosynthetic efficiency levels. The fused feature set CMVTF derived from multimodal remote sensing data outperformed individual feature categories, and the feature subset selected by the UVE method most significantly enhanced model accuracy. Regarding algorithms, the ensemble-based XGBoost and RF models markedly surpassed traditional machine learning algorithms. Specifically, the CMVTF-UVE-XGBoost model achieved the optimal comprehensive performance, yielding an R² of 0.862, an RMSE of 0.032, and an MAE of 0.027 on the testing set, with its spatial distribution estimations highly consistent with measured values. This study provides robust technical support for the rapid and large-scale screening of high photosynthetic-efficiency wheat breeding materials.

Cadmium (Cd) contamination poses a significant threat to wheat productivity and food safety. This study demonstrates that TaMAN7-4B, which encodes an endo-β-mannanase involved in the metabolism of cell wall mannans, is significantly upregulated in response to Cd stress, thereby enhancing Cd tolerance by increasing the root cell wall mannan content and facilitating Cd sequestration. Furthermore, we identified the transcription factor TabZIP27 as a direct regulator of TaMAN7-4B expression. TabZIP27 is phosphorylated by the kinase TaCIPK16, which stabilizes TabZIP27 by suppressing its proteasome-dependent degradation. This phosphorylation event subsequently promotes the transcription of TaMAN7-4B, resulting in enhanced Cd binding within the root cell walls and reduced translocation of Cd from roots to shoots. Our findings elucidate a novel regulatory module, TaCIPK16-TabZIP27-TaMAN7-4B, which governs Cd accumulation in wheat, thereby providing potential targets for the breeding of low-Cd cultivars.