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Drought tolerance is a complex trait controlled by many metabolic pathways and genes and identifying a solution to increase the resilience of plants to drought stress is one of the grand challenges in plant biology. This study provided compelling evidence of increased drought stress tolerance in two sugar beet genotypes when treated with exogenous putrescine (Put) at the seedling stage. Morpho-physiological and biochemical traits and gene expression were assessed in thirty-day-old sugar beet seedlings subjected to drought stress with or without Put (0.3, 0.6, and 0.9 mM) application. Sugar beet plants exposed to drought stress exhibited a significant decline in growth and development as evidenced by root and shoot growth characteristics, photosynthetic pigments, antioxidant enzyme activities, and gene expression. Drought stress resulted in a sharp increase in hydrogen peroxide (H 2 O 2 ) (89.4 and 118% in SBT-010 and BSRI Sugar beet 2, respectively) and malondialdehyde (MDA) (35.6 and 27.1% in SBT-010 and BSRI Sugar beet 2, respectively). These changes were strongly linked to growth retardation as evidenced by principal component analysis (PCA) and heatmap clustering. Importantly, Put-sprayed plants suffered from less oxidative stress as indicated by lower H 2 O 2 and MDA accumulation. They better regulated the physiological processes supporting growth, dry matter accumulation, photosynthetic pigmentation and gas exchange, relative water content; modulated biochemical changes including proline, total soluble carbohydrate, total soluble sugar, and ascorbic acid; and enhanced the activities of antioxidant enzymes and gene expression. PCA results strongly suggested that Put conferred drought tolerance mostly by enhancing antioxidant enzymes activities that regulated homeostasis of reactive oxygen species. These findings collectively provide an important illustration of the use of Put in modulating drought tolerance in sugar beet plants.

The methods utilized to analyze genotype by environment interaction (GEI) and assess the stability and adaptability of genotypes are constantly changing and developing. In this regard, often instead of depending on a single analysis, it is better to use a combination of several methods to measure the nature of the GEI from various dimensions. In this study, the GEI was investigated using different methods. For this purpose, 18 sugar beet genotypes were evaluated in randomized complete block design in five research stations over 2 years. The additive effects analysis of the additive main effects and multiplicative interaction (AMMI) model showed that the effects of genotype, environment and GEI were significant for root yield (RY), white sugar yield (WSY), sugar content (SC), and extraction coefficient of sugar (ECS). The multiplicative effect's analysis of AMMI into interaction principal components (IPCs) showed that the number of significant components varies from one to four in the studied traits. According to the biplot of the mean yield against the weighted average of absolute scores (WAAS) of the IPCs, G2 and G16 for RY, G16 and G2 for WSY, G6, G4, and G1 for SC and G8, G10 and G15 for ECS were identified as stable genotypes with optimum performance. The likelihood ratio test showed that the effects of genotype and GEI was significant for all studied traits. In terms of RY and WSY, G3 and G4 had high mean values of the best linear unbiased predictions (BLUP), so they were identified as suitable genotypes. However, in terms of SC and ECS, G15 obtained high mean values of the BLUP. The GGE biplot method classified environments into four (RY and ECS) and three (WSY and SC) mega-environments (MEs). Based on the multi-trait stability index (MTSI), G15, G10, G6, and G1 were the most ideal genotypes.

Key messageRNA-seq was used to analyze the transcriptional changes in sugar beet (Beta vulgaris L.) triggered by alkaline solution to elucidate the molecular mechanism underlying alkaline tolerance in sugar beet. Several differentially expressed genes related to stress tolerance were identified. Our results provide a valuable resource for the breeding of new germplasms with high alkaline tolerance.Alkalinity is a highly stressful environmental factor that limits plant growth and production. Sugar beet own the ability to acclimate to various abiotic stresses, especially salt and alkaline stress. Although substantial previous studies on response of sugar beet to saline stress has been conducted, the expressions of alkali-responsive genes in sugar beet have not been comprehensively investigated. In this study, we conducted transcriptome analysis of leaves in sugar beet seedlings treated with alkaline solutions for 0 day (control, C), 3 days (short-term alkaline treatment, ST) and 7 days (long-term alkaline treatment, LT). The clean reads were obtained and assembled into 25,507 unigenes. Among them, 975 and 383 differentially expressed genes (DEGs) were identified in the comparison groups ST_vs_C and LT_vs_C, respectively. Gene ontology (GO) analysis revealed that oxidation–reduction process and lipid metabolic process were the most enriched GO term among the DEGs in ST_vs_C and LT_vs_C, respectively. According to Kyoto Encyclopedia of Genes and Genomes pathway, carbon fixation in photosynthetic organisms pathway were significantly enriched under alkaline stress. Besides, expression level of genes encoding d-3-phosphoglycerate dehydrogenase 1, glutamyl-tRNA reductase 1, fatty acid hydroperoxide lyase, ethylene-insensitive protein 2, metal tolerance protein 11 and magnesium-chelatase subunit ChlI, etc., were significantly altered under alkaline stress. Additionally, among the DEGs, 136 were non-annotated genes and 24 occurred with differential alternative splicing. Our results provide a valuable resource on alkali-responsive genes and should benefit the improvement of alkaline stress tolerance in sugar beet.

The effects of foliar applications of zinc oxide nanoparticles and various biostimulants were studied to alleviate water stress in sugar beet. The experiment used a split-split-plot layout based on a Randomized Complete Block Design (RCBD) with three replications over two growing seasons (2022–2024). The main plot consisted of two irrigation levels: Irrigation after 60 and 120 mm of evaporation was considered normal irrigation (WW) and water deficit stress (WD). Zinc nanoparticle (ZnO-NPs) levels (control, 0.2, and 0.4 mg L −1 ) were assigned to subplots, and biostimulants (control, chitosan, proline, and chitosan + proline) were assigned to sub-subplots. The results shoewd under WD conditions foliar spraying of 4 mg L −1 of ZnO-NPs increased the chlorophyll b content (22.85%), carotenoid (9.58%), proline content (18.42%), beta-glycine (13.20%), stomatal conductance (33.53%), gibberellin (GA) (9.09%), cytokinin (CK) (13.07%), catalase enzyme activity (CAT) (7.86%), superoxide dismutase (SOD) (25.56%), ascorbate peroxidase (APX) (5.34%), and root yield (RY) (10.48%) and decreased abscisic acid (ABA) (11.24%), malondialdehyde (MAD) (17.33%), and hydrogen peroxide (17.18%) compared to the control. Among biostimulants treatments, application of chitosan + proline under WD conditions increased the content of chlorophyll a (37.44%), chlorophyll b (16.23%), proline (4.87%), beta-glycine (18.09%), GA (7.00%), auxin (IAA) (35.40%), CK (18.03%), CAT (11.42%), APX (19.6%), and RY (11.46%) compared to control, and decreased the content of ABA (24.16%), MAD (9.03%), and hydrogen peroxide (11.50%). In this experiment, the combination of 2 mg L -1 ZnO-NPs with chitosan and proline exhibited a synergistic effect, increasing the content of chlorophyll a and b, relative water content (RWC), SOD, and RY, while reducing ABA. The lowest IBRv2 values were recorded for control + proline, control + chitosan, 2 mg L −1 ZnO-NPs + Control, 4 mg L −1 ZnO-NPs + Control, and 4 mg L −1 ZnO-NPs + Proline treatments. 4 mg L −1 ZnO-NPs + chitosan + proline (I = 0.803) and 4 mg L −1 ZnO-NPs + proline (I = 0.809) showed the smallest increases in MAD content. In terms of RY, the least decrease was observed in the treatments of 4 mg L −1 ZnO-NPs + Chitosan and Proline (I = − 0.492) and 4 mg L −1 ZnO-NPs + Proline (I = − 1.014).

The genotype by environment interaction significantly influences plant yield, making it imperative to understand its nature for the creation of breeding programs to enhance crop production. However, this is not the only obstacle in the yield improvement process. Breeders also face the significant challenge of unfavorable and negative correlations among key traits. In this study, the stability of root yield and white sugar yield, and the association between the key traits of root yield, sugar content, nitrogen, sodium, and potassium were examined in 20 sugar beet genotypes. The study was conducted using a randomized complete block design with four replications over two consecutive years across five locations. The combined analysis of variance results revealed significant main effects of year, location, and genotype on both root yield and white sugar yield. Notably, two-way and three-way interactions between these main effects on root yield and white sugar yield resulted in a significant difference. The additive main effect and multiplicative interaction analysis revealed that the first five interaction principal components significantly impacted both the root yield and white sugar yield. The linear mixed model results for root yield and white sugar yield indicated that the genotype effect and the genotype by environment interaction were significant. The weighted average absolute scores of the best linear unbiased predictions biplot demonstrated that genotypes 20, 4, 7, 2, 16, 3, 6, 1, 14, and 15 were superior in terms of root yield. For white sugar yield, genotypes 4, 16, 3, 7, 5, 1, 10, 20, 2, and 6 stood out. These genotypes were not only stable but also had a yield value higher than the total average. All key traits, which include sugar content, sodium, potassium, and alpha amino nitrogen, demonstrated a negative correlation with root yield. Based on the genotype by yield*trait analysis results, genotypes 20, 19, and 16 demonstrated optimal performance when considering the combination of root yield with sugar content, sodium, alpha amino nitrogen, and potassium. The multi-trait stability study, genotype 13 ranked first, and genotypes 10, 8, and 9 were identified as the most ideal stable genotypes across all traits. According to the multi-trait stability index, genotype 13 emerged as the top-ranking genotype. Additionally, genotypes 10, 8, and 9 were recognized as the most stable genotypes.

A 2-year field experiment was conducted to analyze the growth conditions, physical features, yield, and nitrogen use efficiency (NUE) of sugar-beet under limited irrigation conditions in northeast of China. A cultivar H003 was used as plant materials; six treatments (C1–C6) were included: C1, no nitrogen applied, rain-fed; C2, nitrogen (120.00 kg ha −1 ), rain-fed; C3, no nitrogen applied, hole irrigation for seeding; C4, nitrogen (120.00 kg ha −1 ), hole irrigation for seeding; C5, no nitrogen applied, hole irrigation for seeding; and C6, nitrogen (120.00 kg ha −1 ), hole irrigation for seeding, and irrigation at foliage rapid growth stage. The irrigation supply was only 500 mL/plant once. Results showed C6 showed the highest chlorophyll content, dry matter accumulation, yield, etc. and had the best NUE among all the treatments. In conclusion, under the routine fertilization conditions of northeast of China, the cultivation measure of hole irrigation 500 mL/plant for seeding combined with irrigation 500 mL/plant at foliage rapid growth stage greatly improved sugar-beet yield and NUE.

Industrial processing to deliver high-quality commercial seed includes removing chemical inhibitors of germination, and is essential to produce fresh sprouts, achieve vigorous crop establishment, and high yield potential in the field. Sugar beet (Beta vulgaris subsp. vulgaris var. altissima Doell.), the main sugar source of the temperate agricultural zone, routinely undergoes several processing steps during seed production to improve germination performance and seedling growth. Germination assays and seedling phenotyping was carried out on unprocessed, and processed (polished and washed) sugar beet fruits. Pericarp-derived solutes, known to inhibit germination, were tested in germination assays and their osmolality and conductivity assessed (ions). Abscisic acid (ABA) and ABA metabolites were quantified in both the true seed and pericarp tissue using UPLC-ESI(+)-MS/MS. Physical changes in the pericarp structures were assessed using scanning electron microscopy (SEM). We found that polishing and washing of the sugar beet fruits both had a positive effect on germination performance and seedling phenotype, and when combined, this positive effect was stronger. The mechanical action of polishing removed the outer pericarp (fruit coat) tissue (parenchyma), leaving the inner tissue (sclerenchyma) unaltered, as revealed by SEM. Polishing as well as washing removed germination inhibitors from the pericarp, specifically, ABA, ABA metabolites, and ions. Understanding the biochemistry underpinning the effectiveness of these processing treatments is key to driving further innovations in commercial seed quality.

Phytoremediation is a promising means of ameliorating heavy metal pollution through the use of transgenic plants as artificial hyperaccumulators. A novel Streptococcus thermophilus γ-glutamylcysteine synthetase-glutathione synthetase (StGCS-GS) that synthesizes glutathione (GSH) with limited feedback inhibition was overexpressed in sugar beet (Beta vulgaris L.), yielding three transgenic lines (s2, s4 and s5) with enhanced tolerance to different concentrations of cadmium, zinc and copper, as indicated by their increased biomass, root length and relative growth compared with wild-type plants. Transgenic sugar beets accumulated more Cd, Zn and Cu ions in shoots than wild-type, as well as higher GSH and phytochelatin (PC) levels under different heavy metal stresses. This enhanced heavy metal tolerance and increased accumulation were likely due to the increased expression of StGCS-GS and consequent overproduction of both GSH and PC. Furthermore, when multiple heavy metal ions were present at the same time, transgenic sugar beets overexpressing StGCS-GS resisted two or three of the metal combinations (50 μM Cd-Zn, Cd-Cu, Zn-Cu and Cd-Zn-Cu), with greater absorption in shoots. Additionally, there was no obvious competition between metals. Overall, the results demonstrate the explicit role of StGCS-GS in enhancing Cd, Zn and Cu tolerance and accumulation in transgenic sugar beet, which may represent a highly promising new tool for phytoremediation.

Cercospora leaf spot, caused by the fungal pathogen Cercospora beticola, is the most destructive foliar disease of sugar beet worldwide. This review discusses C. beticola genetics, genomics, and biology and summarizes our current understanding of the molecular interactions that occur between C. beticola and its sugar beet host. We highlight the known virulence arsenal of C. beticola as well as its ability to overcome currently used disease management strategies. Finally, we discuss future prospects for the study and management of C. beticola infections in the context of newly employed molecular tools to uncover additional information regarding the biology of this pathogen. Taxonomy Cercospora beticola Sacc.; Kingdom Fungi, Phylum Ascomycota, Class Dothideomycetes, Order Capnodiales, Family Mycosphaerellaceae, Genus Cercospora. Host range Well‐known pathogen of sugar beet (Beta vulgaris subsp. vulgaris) and most species of the Beta genus. Reported as pathogenic on other members of the Chenopodiaceae (e.g., lamb's quarters, spinach) as well as members of the Acanthaceae (e.g., bear's breeches), Apiaceae (e.g., Apium), Asteraceae (e.g., chrysanthemum, lettuce, safflower), Brassicaceae (e.g., wild mustard), Malvaceae (e.g., Malva), Plumbaginaceae (e.g., Limonium), and Polygonaceae (e.g., broad‐leaved dock) families. Disease symptoms Leaves infected with C. beticola exhibit circular lesions that are coloured tan to grey in the centre and are often delimited by tan‐brown to reddish‐purple rings. As disease progresses, spots can coalesce to form larger necrotic areas, causing severely infected leaves to wither and die. At the centre of these spots are black spore‐bearing structures (pseudostromata). Older leaves often show symptoms first and younger leaves become infected as the disease progresses. Management Application of a mixture of fungicides with different modes of action is currently performed although elevated resistance has been documented in most employed fungicide classes. Breeding for high‐yielding cultivars with improved host resistance is an ongoing effort and prudent cultural practices, such as crop rotation, weed host management, and cultivation to reduce infested residue levels, are widely used to manage disease. Useful website https://www.ncbi.nlm.nih.gov/genome/11237?genome_assembly_id=352037 The hemibiotrophic fungus Cercospora beticola applies various virulence strategies to infect sugar beet and is currently only managed in‐field through integrated practices.

In this study, members of the BvDof transcription factor family were identified in the beet genome data ( Beta vulgaris L.) Through systematic analysis, 22 BvDof family genes were found in the beet genome, and they were divided into nine groups by phylogenetic analysis. Fifteen members of the BvERF family were involved in the transition to rapid root tuber growth. There was a tandem replication during the generation of the Dof gene family in sugar beet. Bv1_zfms , Bv_ofna , Bv5_racn , and Bv6_augo may be involved in the regulation of secondary cambium development in the beet root tuber. Bv9_nood , Bv1_zfms , and Bv6_cdca may be related to the growth rate of root tubers. The results provide a reference for further elucidating the molecular mechanism of the BvDof transcription factor, which regulates the development of beet root tubers.