Although whole genome sequencing, genetic variation mapping, and pan-genome studies have been done on a large group of cucumber nuclear genomes, organelle genome information is largely unclear. As an important component of the organelle genome, the chloroplast genome is highly conserved, which makes it a useful tool for studying plant phylogeny, crop domestication, and species adaptation. Here, we have constructed the first cucumber chloroplast pan-genome based on 121 cucumber germplasms, and investigated the genetic variations of the cucumber chloroplast genome through comparative genomic, phylogenetic, haplotype, and population genetic structure analysis. Meanwhile, we explored the changes in expression of cucumber chloroplast genes under high- and low-temperature stimulation via transcriptome analysis. As a result, a total of 50 complete chloroplast genomes were successfully assembled from 121 cucumber resequencing data, ranging in size from 156,616–157,641 bp. The 50 cucumber chloroplast genomes have typical quadripartite structures, consisting of a large single copy (LSC, 86,339–86,883 bp), a small single copy (SSC, 18,069–18,363 bp), and two inverted repeats (IRs, 25,166–25,797 bp). Comparative genomic, haplotype, and population genetic structure results showed that there is more genetic variation in Indian ecotype cucumbers compared to other cucumber cultivars, which means that many genetic resources remain to be explored in Indian ecotype cucumbers. Phylogenetic analysis showed that the 50 cucumber germplasms could be classified into 3 types: East Asian, Eurasian + Indian, and Xishuangbanna + Indian. The transcriptomic analysis showed that matK were significantly up-regulated under high- and low-temperature stresses, further demonstrating that cucumber chloroplasts respond to temperature adversity by regulating lipid metabolism and ribosome metabolism. Further, accD has higher editing efficiency under high-temperature stress, which may contribute to the heat tolerance. These studies provide useful insight into genetic variation in the chloroplast genome, and established the foundation for exploring the mechanisms of temperature-stimulated chloroplast adaptation.

IL52 is a valuable introgression line obtained from interspecific hybridization between cultivated cucumber (Cucumis sativus L., 2n = 14) and the wild relative species C. hystrix Chakr. (2n = 24). IL52 exhibits high resistance to a number of diseases, including downy mildew, powdery mildew, and angular leaf spot. However, the ovary- and fruit-related traits of IL52 have not been thoroughly investigated. Here, we conducted quantitative trait loci (QTL) mapping for 11 traits related to ovary size, fruit size, and flowering time using a previously developed 155 F_7:8 RIL population derived from a cross between CCMC and IL52. In total, 27 QTL associated with the 11 traits were detected, distributed on seven chromosomes. These QTL explained 3.61% to 43.98% of the phenotypic variance. Notably, we identified a major-effect QTL (qOHN4.1) on chromosome 4 associated with the ovary hypanthium neck width and further delimited it into a 114-kb candidate region harboring 13 candidate genes. Furthermore, the QTL qOHN4.1 is co-localized with the QTL detected for ovary length, mature fruit length, and fruit neck length, all residing within the consensus QTL FS4.1, suggesting a plausible pleiotropic effect.

Cold stress severely limits legume productivity, threatening global food security, particularly in climate-vulnerable regions. This review synthesizes advances in understanding and enhancing cold tolerance in key legumes (chickpea, soybean, lentil, and cowpea), addressing three core questions: (1) molecular/physiological foundations of cold tolerance; (2) how emerging technologies accelerate stress dissection and breeding; and (3) integration strategies and deployment challenges. Legume cold tolerance involves conserved pathways (e.g., ICE-CBF-COR, Inducer of CBF Expression, C-repeat Binding Factor, Cold-Responsive genes) and species-specific mechanisms like soybean’s GmTCF1a-mediated pathway. Multi-omics have identified critical genes (e.g., CaDREB1E in chickpea, NFR5 in pea) underlying adaptive traits (membrane stabilization, osmolyte accumulation) that reduce yield losses by 30–50% in tolerant genotypes. Technologically, AI and high-throughput phenotyping achieve >95% accuracy in early cold detection (3–7 days pre-symptoms) via hyperspectral/thermal imaging; deep learning (e.g., CNN-LSTM hybrids) improves trait prediction by 23% over linear models. Genomic selection cuts breeding cycles by 30–50% (to 3–5 years) using GEBVs (Genomic estimated breeding values) from hundreds of thousands of SNPs (Single-nucleotide polymorphisms). Advanced sensors (LIG-based, LoRaWAN) enable real-time monitoring (±0.1 °C precision, <30 s response), supporting precision irrigation that saves 15–40% water while maintaining yields. Key barriers include multi-omics data standardization and cost constraints in resource-limited regions. Integrating molecular insights with AI-driven phenomics and multi-omics is revolutionizing cold-tolerance breeding, accelerating climate-resilient variety development, and offering a blueprint for sustainable agricultural adaptation.

Thorough and precise gene structure annotations are essential for maximizing the benefits of genomic data and unveiling valuable genetic insights. The cucumber genome was first released in 2009 and updated in 2019. To increase the accuracy of the predicted gene models, 64 published RNA-seq data and 9 new strand-specific RNA-seq data from multiple tissues were used for manual comparison with the gene models. The updated annotation file (V3.1) contains an increased number (24,145) of predicted genes compared to the previous version (24,317 genes), with a higher BUSCO value of 96.9%. A total of 6231 and 1490 transcripts were adjusted and newly added, respectively, accounting for 31.99% of the overall gene tally. These newly added and adjusted genes were renamed (CsaV3.1_XGXXXXX), while genes remaining unaltered preserved their original designations. A random selection of 21 modified/added genes were validated using RT-PCR analyses. Additionally, tissue-specific patterns of gene expression were examined using the newly obtained transcriptome data with the revised gene prediction model. This improved annotation of the cucumber genome will provide essential and accurate resources for studies in cucumber.