Cucumber (Cucumis sativus L.), a widely cultivated crop, has originated from Eastern Himalayas and secondary domestication regions includes highly divergent climate conditions e.g. temperate and subtropical. We wanted to uncover adaptive genome differences between the cucumber cultivars and what sort of evolutionary molecular mechanisms regulate genetic adaptation of plants to different ecosystems and organism biodiversity. Here we present the draft genome sequence of the Cucumis sativus genome of the North-European Borszczagowski cultivar (line B10) and comparative genomics studies with the known genomes of: C. sativus (Chinese cultivar – Chinese Long (line 9930)), Arabidopsis thaliana, Populus trichocarpa and Oryza sativa. Cucumber genomes show extensive chromosomal rearrangements, distinct differences in quantity of the particular genes (e.g. involved in photosynthesis, respiration, sugar metabolism, chlorophyll degradation, regulation of gene expression, photooxidative stress tolerance, higher non-optimal temperatures tolerance and ammonium ion assimilation) as well as in distributions of abscisic acid-, dehydration- and ethylene-responsive cis-regulatory elements (CREs) in promoters of orthologous group of genes, which lead to the specific adaptation features. Abscisic acid treatment of non-acclimated Arabidopsis and C. sativus seedlings induced moderate freezing tolerance in Arabidopsis but not in C. sativus. This experiment together with analysis of abscisic acid-specific CRE distributions give a clue why C. sativus is much more susceptible to moderate freezing stresses than A. thaliana. Comparative analysis of all the five genomes showed that, each species and/or cultivars has a specific profile of CRE content in promoters of orthologous genes. Our results constitute the substantial and original resource for the basic and applied research on environmental adaptations of plants, which could facilitate creation of new crops with improved growth and yield in divergent conditions.

Despite the importance of plant sex determination in genetic diversity, there is no clear picture of the regulation of this process. Cucumber has recently emerged as an excellent model system to explore sex determination. In this review, we describe the current state of sex determination in cucumber and propose a model to explain the regulation of the molecular processes controlling this phenomenon. Transcriptional profiling of hormonally controlled genes involved in sex-specific patterns of floral development has progressed from genetic-based studies to direct RNA analysis and from gene-by-gene examinations to genomic-scale analyses. Floral development comprises an orchestrated series of processes that are ordered in accordance with plant genetic background. Moreover, they are regulated at multiple levels by endogenous and environmental factors. Recent findings have contributed to a better understanding of sex expression and will undoubtedly inspire new applications and research into mechanisms underlying the development, function, and evolution of cucumber sex determination and floral morphogenesis. Intensive investigation of potential sex determination genes in model species is anticipated to aid the elucidation of the sex determination gene network and should have a significant impact on plant breeding.

Genetic variation is expressed by the presence of polymorphisms in compared genomes of individuals that can be transferred to next generations. The aim of this work was to reveal genome dynamics by predicting polymorphisms among the genomes of three individuals of the highly inbred B10 cucumber (Cucumis sativusL.) line. In this study, bioinformatic comparative genomics was used to uncover cucumber genome dynamics (also called real-time evolution). We obtained a new genome draft assembly from long single molecule real-time (SMRT) sequencing reads and used short paired-end read data from three individuals to analyse the polymorphisms. Using this approach, we uncovered differentiation aspects in the genomes of the inbred B10 line. The newly assembled genome sequence (B10v3) has the highest contiguity and quality characteristics among the currently available cucumber genome draft sequences. Standard and newly designed approaches were used to predict single nucleotide and structural variants that were unique among the three individual genomes. Some of the variant predictions spanned protein-coding genes and their promoters, and some were in the neighbourhood of annotated interspersed repetitive elements, indicating that the highly inbred homozygous plants remained genetically dynamic. This is the first bioinformatic comparative genomics study of a single highly inbred plant line. For this project, we developed a polymorphism prediction method with optimized precision parameters, which allowed the effective detection of small nucleotide variants (SNVs). This methodology could significantly improve bioinformatic pipelines for comparative genomics and thus has great practical potential in genomic metadata handling.

Background/Objectives: In the context of accelerating climate change and growing food insecurity, improving crop resilience to abiotic stresses such as drought, salinity, heat, and cold is a critical agricultural and scientific challenge. Understanding the biochemical mechanisms that underlie plant stress responses is essential for developing resilient crop varieties This review aims to provide an integrative overview of how metabolomics can elucidate biochemical mechanisms underlying stress tolerance and guide the development of stress-resilient crops. Methods: We reviewed the recent literature on metabolomic studies addressing abiotic stress responses in various crop species, focusing on both targeted and untargeted approaches using platforms such as nuclear magnetic resonance (NMR), liquid chromatography–mass spectrometry (LC-MS), and gas chromatography–mass spectrometry (GC-MS). We also included emerging techniques such as capillary electrophoresis–mass spectrometry (CE-MS), ion mobility spectrometry (IMS-MS), Fourier transform infrared spectroscopy (FT-IR), and data-independent acquisition (DIA). Additionally, we discuss the integration of metabolomics with transcriptomics and physiological data to support system-level insights. Results: The reviewed studies identify common stress-responsive metabolites, including osmoprotectants, antioxidants, and signaling compounds, which are consistently linked to enhanced tolerance. Novel metabolic biomarkers and putative regulatory hubs are highlighted as potential targets for molecular breeding and bioengineering. We also address ongoing challenges related to data standardization and reproducibility across analytical platforms. Conclusions: Metabolomics is a valuable tool for advancing our understanding of plant abiotic stress responses. Its integration with other omics approaches and phenotypic analyses offers promising avenues for improving crop resilience and developing climate-adaptive agricultural strategies.

Artificial intelligence (AI) can revolutionize agriculture by enhancing genomic research and promoting sustainable crop improvement. AI systems integrate machine learning (ML) and deep learning (DL) with big data to identify complex patterns and relationships by analyzing vast genomic, phenotypic, and environmental datasets. This capability accelerates breeding cycles, improves predictive accuracy, and supports the development of climate-resilient, high-yielding crop varieties. Applications such as precision agriculture, automated phenotyping, predictive analytics, and early pest and disease detection demonstrate AI’s ability to optimize agricultural practices while promoting sustainability. Despite these advancements, challenges remain, including fragmented data sources, variability in phenotyping protocols, and data ownership concerns. Addressing these issues through standardized data integration frameworks, advanced analytical tools, and ethical AI practices will be critical for realizing AI’s full agricultural potential. This review provides a comprehensive overview of AI-powered genomic research, highlights the role of big data in training robust AI models, and explores ethical and technological considerations for sustainable agricultural practices.

In terms of the process of air purification, a lot of attention has been devoted to trees and shrubs. Little attention has been paid to herbaceous vegetation from the lower forest layers. Urban forests are often located on the outskirts of cities and surround exit roads where there is heavy traffic, generating particulate matter (PM) pollution. The aim of this study was to investigate the spread of PM from the road traffic in the air and to investigate how individual layers of urban forests accumulate PM. We conducted comparative analyses of PM accumulation on plants in five zones away from the road, into the forest, in the air, and in four vegetation layers: mosses, herbaceous plants, shrubs and trees. The results show that all forest layers accumulate PM. We show that PM is very efficiently accumulated by herbaceous plants growing along roadsides, and that the PM that was not deposited on herbaceous plants was accumulated by trees and shrubs. With increasing distance from the road into the forest, the PM content on herbaceous plants decreased and the accumulation on trees and shrubs increased. We estimated that PM concentration in the air dropped significantly in the front line of the trees, but it was still detectable up to 50 m into the forest. The results presented herein show that meadow vegetation and urban forests play a very important role in air purification. Our results provide a better understanding of the complexity of urban forest interactions and provide the basis for better planning of urban greenery.