research study about cucumber

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Home > Books > Cucumber Economic Values and Its Cultivation and Breeding

Introductory Chapter: Studies on Cucumber

Submitted: 09 March 2021 Published: 08 May 2021

DOI: 10.5772/intechopen.97360

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Cucumber Economic Values and Its Cultivation and Breeding

Edited by Haiping Wang

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Huixia jia *.

• Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China

Haiping Wang *

*Address all correspondence to: [email protected] and [email protected]

1. Introduction

Cucumber ( Cucumis sativus L.) belongs to Cucumis genus in Cucurbitaceae family and is an economically important fruit vegetable. There are three wild or semi-wild varieties of cucumber: C. sativus L. var. hardwickii, C. sativus L. var. sikkimensis, C. sativus L. var. xishuangbannanesis. Cucumber is indigenous to India and likely originated from the foothills of the Himalayan Mountain [ 1 , 2 ]. Cucumber was cultivated ~3000 years ago in India, and it seems to spread rapidly to Western Asia, and then to Southern Europe [ 2 ]. Cucumber was introduced respectively to North China through the Silk Route and to South China from Burma and India-China border, and subsequently spread to East Asia [ 2 ]. Genome variation analysis showed cucumber core germplasms were divided into four geographic groups including India, Eurasia, East Asia, and Xishuangbanna [ 3 ]. Nowadays, cucumber is widely cultivated in temperate and tropical regions throughout the world [ 4 ]. The total production of cucumber was 87,805,086 tons worldwide, and Asia was the largest producer accounting for 84.9% of the world’s total production in 2019 ( www.fao.org/faostat/en/ ). With abundant water, nutrients and phytochemical composition, cucumber has versatile uses in culinary, therapeutic and cosmetic purposes [ 5 , 6 ]. Cucumber has multiple advantages such as diploid, small genome, short life cycle and self-compatible mating system, so it is suitable for genetic studies. Moreover, cucumber has been identified as a model plant for studying sex determination and plant vascular biology [ 7 ]. Consequently, numerous studies have been conducted to discover the miracle of cucumber. The book will cover the extensive benefits, production and market, cultivation and management, pests and diseases, breeding progress of cucumber.

2. Biological characteristics

Cucumber is an annual climbing herbaceous plant. The root system is shallow and mainly distributes in the cultivated land layer of 30 cm. The stem is vine with different degree of apical dominance. The cross section of the stem is rhombus, and the epidermis of the stem has burrs. The axillae on the stem have the ability of branching, and the number of branching varies greatly among varieties. The cotyledons of cucumber are opposite and long elliptic; euphylla are alternate, simple, pentagonal palmate or cordate in outline, and the blades are 3–7 lobed. The flower is axillary, unisexual and occasionally hermaphrodite. The calyx is green with bristles, and the corolla is yellow. The colour of young fruit changes from white to pale green, while mature fruit is yellow or brown when ripened. The shape of the fruit is diverse, such as clublike, cylindrical and spherical. Each fruit has 100–400 seeds. The weight of 1000 seeds is about 20–40 g.

3. Culinary, therapeutic and cosmetic uses of cucumber

At present, cucumber is the fourth most widely cultivated vegetable after tomato, cabbage and onion [ 8 ]. Cucumber has versatile uses in culinary, therapeutic and cosmetic purposes [ 5 , 6 ]. Nutritional and epidemiological researches have shown various benefits of cucumber. For example, cucumber contains abundant nutrients and has crunchy texture and unique flavor, so it is a quintessential vegetable used for a variety of dishes, and it is also indispensable for salad, soup and smoothie. Cucumber is rich in superior hydration and phytochemicals, which have diverse health benefits including weight loss, anti-inflammation, remedy for multiple diseases of eczema, constipation, hypertension, atherosclerosis, cancer, etc. [ 9 ]. Recent studies found that the presence of kaempferol in cucumber is an important antidiabetic agent [ 10 ]. Furthermore, cucumber is popularly used for natural beautification and for skin treatments [ 11 ].

4. Influence factors and solutions of cucumber fermentation

Cucumber pickles are most commonly fermented vegetable and widely consumed throughout the world [ 12 ]. Good fermentation depends on the proper combinations and interactions of multiple physical, chemical and microbiological factors [ 13 ]. Brine storage and process operations are susceptible to oxidation reactions during the fermentation process, and this has adverse influence on the quality property of cucumber pickles. To control the influence factors of cucumber fermentation, researchers have done many efforts on modern and advanced technologies, such as reducing the concentration of brining sodium chloride, developing the brining properties using lactic acid bacteria cultures, developing an anaerobic tank system, preventing cucumber gaseous deterioration by pouring of CO2 from fermentation brines [ 13 ]. After storing the brine, excess salt and organic wastes need to be leached to complete the product processing, and these wastes are sources of serious environmental concern. Thus, the waste disposal needs to be solved in the cucumber pickle industry.

5. Performance, structure and constraints of cucumber market

Marketing is vital for linking production and consumption and facilitating agricultural productivity and employment [ 14 ]. Market performance is the ultimate result of various market activities, and market structure is the organization characteristics of the market that influence the nature of competition and pricing [ 15 ]. Both male and female participate in cucumber marketing, and the male–female rate has great differences in different regions. The wholesalers are older than the retailers. In Ibadan, most of the retailers were within 31–40 years age, whereas most of the wholesalers were within 41–50 years age. It’s gratifying that cucumber marketing is usually profitable for the retailers and wholesalers at both peak and lean seasons of cucumber production. However, the cucumber market is competitive, and inequality exists in the market. Commodity perishability is an important constraint in cucumber market. Thus, it is indispensable to reduce perishable degree and prolong storage time after post-harvest.

6. Soil moisture and fertilizer management of cucumber

Inappropriate farming systems and poor agronomic management are responsible for low yield of cucumber. The quality/fertility status of soils is essential for growth and development for cucumber [ 16 ]. With good moisture and fertilizer management, optimum yield of cucumber might be attained. The conventional irrigation methods including flooding irrigation, furrow irrigation and drip irrigation have been widely applied for a long time in cucumber cultivation because of their low cost or simple operation [ 17 , 18 ]. However, these irrigation methods are surface irrigation and are driven by positive pressure, which may cause low water use efficiency, water wastage and nutrient loss [ 16 , 19 ]. To solve these problems, new irrigation technique such as negative pressure irrigation that controls automatically water release based on the soil water potential difference should be encouraged [ 16 ]. Inadequate fertilizer use causes low soil fertility that cannot provide sufficient nutrients for the normal growth of cucumber. The integration application of inorganic and organic fertilizer is more beneficial than the sole use of inorganic fertilizer or organic manures in cucumber production [ 20 ]. Moreover, fertilizer sources need to dissolve or decompose to make nutrients available for cucumber plants, so soil fertility also depends on soil water, temperature and density. Consequently, the soil management strategies such as negative pressure irrigation, seasonable fertilization, application of organic mulches and conservation tillage should be appropriately applied for sustainable production of cucumber.

7. Biostimulators promote growth of cucumber under soilless cultivated condition

Soilless cultivation in substrate culture is an important cultivation pattern for cucumber in greenhouses. The substrates should have specific physical properties including pore volume, air and water capacity, and density of substrates. Studies indicate that biostimulators can stabilize the production process to enhance plant growth under stress conditions. For instance, humate can increase vitality and growth of plants, improve seed germination, promote nutrient uptake, enhance transport and availability of micronutrients, and increase ion-exchange capacity. Lactates can produce bioregulatory effects to improve nutrient balance and plant vitality [ 21 ]. Bacillus subtilis , as a microorganism from the rhizosphere, can accelerate plant growth, stimulate the process of formation of plant organs, and enhance the resistance of biotic and abiotic stresses [ 21 ]. Application of biostimulators mixture (humate, lactate, and Bacillus subtilis ) prevent growth reduction of cucumber under pH and temperature stresses through enhancing the root growth, whereas the growth is markedly reduced under stresses if no biostimulator is applied.

8. Pests and diseases during cucumber cultivation and production

During growth process, cucumber might be affected by multiple insect pests and diseases, resulting in decrease of yield and quality. The major insect pests in cucumber including Diabrotica undecimpunctata , Acalymma vitatum , Bactrocera cucurbitae , Raphidopalpa foveicollis , Epilachna implicate , Myzus persicae , Aphis gossypii , Anasa tristis , Trialeurodes vaparariorus , Bemisia tabaci and B. argentifolii [ 22 , 23 ]. Currently, the pest management mainly relies on chemical pesticides that cause environmental pollution, pest resistance, and disturbance of balance between the pests and natural enemies. Moreover, this control strategy is harmful to human health. Therefore, an integrated pest management including pest monitoring, cultural method, host resistance, botanicals, biological control, and judicious use of chemicals is recommended for controlling these pests [ 24 , 25 ] Many diseases caused by viral, bacterial, fungal and nematode pathogens severely affect the cultivation and production of cucumber. Viruses infecting cucumber belong to three genera: Potyvirus , Cucumovirus and Crinivirus [ 26 ]. Especially, the CMV, ZYMV, WMV, MWMV, PRSV and BPYV are major viruses that cause severe symptoms to cucumber. Downy mildew, powdery mildew and anthracnose also cause substantial losses of cucumber production [ 27 ]. Some pathogenic fungi including Alternaria tenuis , Fusarium equisett , Phytophthora capsici , Botrytis cinerea and Cladosporium tenuissimum cause rotting and high post-harvest losses of cucumber [ 28 ]. Furthermore, root-knot nematodes are prevalent destructive pathogens of cucumber [ 29 ]. Though a series of chemicals have been evaluated and screened to control these diseases, the biological control strategy and high-resistant varieties of cucumber need be developed and created to resist diseases in efficient and environmental ways.

9. Polyphenols act as antioxidants in cucumber to defense stresses

Plant secondary metabolites play important roles in adapting to various environments and defensing against biotic and abiotic stresses. Cucumber is a rich source of phenolic compounds that are important secondary metabolites [ 30 , 31 ]. The antioxidant capacity of cucumber seems to be attributing to polyphenols that scavenge singlet oxygen, hydroxyl and lipid peroxyl radicals to prevent lipid oxidation. Better understanding of the molecular regulation of polyphenols biosynthesis is crucial to increase the production of polyphenols. Polyphenols are derivatives of phenylpropanoid pathway which involves an array of enzymes. Among these, phenylalanine ammonia lyase, chalcone synthase, cinnamate 4-hydroxylase and dihydroflavonol reductase play important roles [ 32 ]. In-depth study of these key enzymes in cucumber will facilitate to reveal the molecular mechanism of polyphenol synthesis, which is helpful for advancement in biotechnological and industrial applications.

10. Progress of traditional breeding and molecular breeding in cucumber

In the past decades, traditional breeding has played essential roles in cultivar innovation of cucumber. Some superior varieties with early maturity, high yield and high resistance have been developed through hybridization and mutagenesis [ 33 ]. However, this progress is slow because of the long cycle and difficulty in selection of stable genetic characters or genotypes. To overcome the obstacle of traditional breeding, molecular breeding technologies including molecular marker assisted breeding, genome-wide design breeding and genetic engineering have been applied in cucumber to accelerate the breeding cycle and select desirable traits. Molecular breeding of cucumber has made some progress and achievements on completion of genomics, genetic architecture and molecular mechanism underlying important traits, and creation of high quality and multi-resistant varieties [ 7 , 34 , 35 , 36 ]. With increasing consumption demand of cucumber, more new varieties with excellent comprehensive properties are in need, and we might make some efforts from the following aspects: (i) expanding collection and utilization of cucumber germplasm resources; (ii) establishing highly efficient gene editing and genetic transformation technologies in cucumber; (iii) identifying new loci or genes associated with key agronomic traits of cucumber and combining multiple molecular markers of excellent traits into one variety; (iv) realizing rapid accumulation of omics genotypes and phenomics [ 37 ].

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• Published: 01 October 2018

The USDA cucumber ( Cucumis sativus L.) collection: genetic diversity, population structure, genome-wide association studies, and core collection development

• Xin Wang 1 ,
• Kan Bao 1 ,
• Umesh K. Reddy 2 ,
• Yang Bai 1 ,
• Sue A. Hammar 3 ,
• Chen Jiao 1 ,
• Todd C. Wehner 4 ,
• Axel O. Ramírez-Madera   ORCID: orcid.org/0000-0001-6296-1059 5 ,
• Yiqun Weng 5 , 6 ,
• Rebecca Grumet 3 &
• Zhangjun Fei   ORCID: orcid.org/0000-0001-9684-1450 1 , 7  

Horticulture Research volume  5 , Article number:  64 ( 2018 ) Cite this article

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• Genetic variation
• Plant genetics

Germplasm collections are a crucial resource to conserve natural genetic diversity and provide a source of novel traits essential for sustained crop improvement. Optimal collection, preservation and utilization of these materials depends upon knowledge of the genetic variation present within the collection. Here we use the high-throughput genotyping-by-sequencing (GBS) technology to characterize the United States National Plant Germplasm System (NPGS) collection of cucumber ( Cucumis sativus L.). The GBS data, derived from 1234 cucumber accessions, provided more than 23 K high-quality single-nucleotide polymorphisms (SNPs) that are well distributed at high density in the genome (~1 SNP/10.6 kb). The SNP markers were used to characterize genetic diversity, population structure, phylogenetic relationships, linkage disequilibrium, and population differentiation of the NPGS cucumber collection. These results, providing detailed genetic analysis of the U.S. cucumber collection, complement NPGS descriptive information regarding geographic origin and phenotypic characterization. We also identified genome regions significantly associated with 13 horticulturally important traits through genome-wide association studies (GWAS). Finally, we developed a molecularly informed, publicly accessible core collection of 395 accessions that represents at least 96% of the genetic variation present in the NPGS. Collectively, the information obtained from the GBS data enabled deep insight into the diversity present and genetic relationships among accessions within the collection, and will provide a valuable resource for genetic analyses, gene discovery, crop improvement, and germplasm preservation.

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Introduction.

Improvements in crop yield, ability to withstand abiotic and biotic stresses, and superior product quality all depend on genetic variation for key agronomic and horticultural traits. In search of such variation, breeders frequently turn to germplasm collections to find new sources of valuable characteristics, especially resistances to diseases, insects, and environmental stresses such as heat, drought, salt, or cold. To facilitate these breeding efforts and maintain critical diversity for future generations, many national and international institutions have developed extensive germplasm collections to provide repositories of genetic variation. More than 1750 gene banks have been established worldwide 1 . Collections are typically made from locations throughout the globe, with particular emphasis on centers of crop diversity. The importance of such collections as a critical first step to conserve biological variation, especially in light of genetic erosion resulting from habitat loss, adoption of modern varieties, and climate change, is increasingly recognized as a critical global good, both in scientific and broader public spheres 2 , 3 . While creation and maintenance of these valuable collections is essential, questions arise as to how to catalog, unlock, manage, and preserve the valuable diversity they contain. How do we evaluate the extent and nature of variation that exists within the collection? How can we access that variation for crop improvement? Fortunately, the past decade has ushered in powerful genomic tools that allow for high throughput, high resolution, genetic characterization, while also providing breeders more efficient access to, and use of, the diversity available within collections.

Collections for the Cucurbitaceae family, which includes many high-value crops consumed as vegetables and fruits throughout the world, face the above-mentioned challenges for germplasm preservation and utilization 4 . Cucumber ( Cucumis sativus L.), a member of the Cucurbitaceae family with origins in India, China, Burma, Thailand, is thought to have been domesticated ~3000 years ago 5 , 6 . The primary and secondary centers of diversity for the species are located in India and Southeast Asia, respectively 7 , 8 . Genomic analysis of cultivated cucumber ( C. s . var. sativus) divided it into four geographic groups: India; Eurasia and the West; East Asia and China; and Xishuangbanna from Southwestern China 9 , 10 . The Indian group, which is thought to form the basal group, maintains a large proportion of the genetic diversity and also includes the wild cucumber, C. s . var. hardwickii , a feral form of var. sativus 9 , 10 , 11 . Deep resequencing of a core collection of 115 cucumber lines, sampled from 3342 accessions worldwide, suggests that the domestication process led to a severe genetic bottleneck, resulting in reduction in diversity relative to wild accessions 10 . More than 100 putative selective sweeps appear to be associated with domestication, including extended linkage disequilibrium in regions surrounding loci associated with key fruit traits such as size and bitterness. Results of the genomic analyses, including assignment of a basal role of the Indian group and separation of the orange-endocarp Xishuangbanna group, complement prior genetic and morphological assessments 12 , 13 , 14 , 15 , 16 . These analyses have allowed for evolutionary insight into the relationships and domestication trajectories among cucumber accessions.

The cucumber collection in the United States is maintained at the Ames, Iowa facility of the USDA Agriculture Research Service National Plant Germplasm System (NPGS; https://npgsweb.ars-grin.gov/gringlobal/site.aspx?id=16 ). The NPGS collection comprises 1314 cucumber accessions representing the primary cucumber gene pool ( C. s . var. sativus and C. s . var. hardwickii ). This collection, which is primarily composed of cultivars, land races, and varieties collected from around the world, has been extensively utilized by breeders searching for a variety of traits, including resistance to downy mildew 17 (causal agent: Pseudoperonospora cubensis ), powdery mildew 18 (causal agent: Podosphaera xanthii ), Phytophthora fruit rot 19 , 20 (causal agent: Phytophthora capsici ), belly rot 21 , 22 (causal agent: Rhizoctonia solani ), and root knot nematodes ( Meloidogyne spp.) 23 , as well as variations for fruit yield, fruit quality 24 , and above-ground and below-ground plant architecture 25 , 26 . However, to date, there have been very limited efforts to genetically characterize the US cucumber collection. Meglic et al. 27 examined 757 accessions using seven isozyme loci, and Horejsi et al. 28 characterized 118 accessions with 71 RAPD loci. Lv et al. 9 included 883 accessions from the U.S. collection, which were characterized using a set of 23 SSR markers and 316 alleles. Current genomic technologies allow for much higher throughput and full genome analyses. The dramatically reduced cost of sequencing, high-throughput sample preparation, and efficient bioinformatics now make it feasible to perform genomic analysis on increasingly large numbers of samples for plant germplasm research 29 , 30 . In this study, we have performed genotyping on 1234 cucumber accessions from the NPGS, using genotyping-by-sequencing 30 (GBS). The resultant high-throughput single-nucleotide polymorphism (SNP) markers provided high-definition genetic characterization of the US cucumber germplasm collection, allowing for assessment of genetic diversity and population structure, identification of markers that are highly associated with important agronomic traits through genome-wide association studies (GWAS), and development of a molecularly informed publicly accessible core population to facilitate breeding and preservation efforts.

Materials and methods

Plant materials and dna extraction.

Tissue samples (50–100 mg fresh weight) were collected from young (not fully expanded) leaves, freeze-dried, and ground to a fine powder using 5/32” stainless steel balls (AbbottBall, West Hartford, CT) in a Retsch Mixer Mill (Retsch, Newtown, PA). DNA was isolated using the Omega Mag-Bind Plant DNA DS Kit (M1130, Omega Bio-Tek, Norcross, GA) on a Kingfisher Flex Magnetic Particle Processor (Thermo Scientific, Waltham, MA). The kit protocol was followed except that the initial 56 °C incubation was extended to 60 min instead of 30 min. The DNA was quantified using the Quant-iT PicoGreen dsDNA Kit (Invitrogen, Carlsbad, CA) in a 384-well format on a CFX384 C1000 Real-Time thermal cycler (BioRad, Hercules, CA). Normalization to 30–100 ng/ul was done using a GBFit Arise Pipetting System (Pacgen Inc., Irvine, CA). Quality checks were performed on 10% of the genomic DNA samples from each batch of 96 samples by agarose gel observation of 300 ng of undigested and Hind III digested DNA per sample.

GBS and SNP calling

Genotyping of the cucumber accessions was performed following the GBS protocol 30 , using ApeK I as the restriction enzyme. The resulting 96-plex or 384-plex libraries were sequenced on a HiSeq 2500 system (Illumina Inc., USA) with the single-end mode and read length of 101 bp.

SNP identification was performed using TASSEL 5.0 GBS Discovery Pipeline 31 , using the cucumber Gy14 draft genome (v2; http://cucurbitgenomics.org ) as the reference. Briefly, the raw reads were first processed to retain reads possessing a barcode and a restriction enzyme cut site using GBSSeqToTagDBPlugin with the parameters “-kmerLength 90-minKemrL 30-mnQS 10-c 100-maKmerNum 200000000”. The resulting reads were then concatenated into distinct tags using the FastqToTagCount plug-in in TASSEL, and tags supported by at least ten reads were kept and mapped to the cucumber reference genome sequence using BWA (version 0.7.16a) with default parameters 32 . Based on the alignments, positions of aligned tags were determined using SAMtoGBSdbPlugin, and SNPs were identified from the aligned tags using DiscoverySNPCallerPluginV2 with default parameters. The identified SNPs were scored according to the coverage, depth, and genotypic statistics for a given set of samples using SNPQualityProfilerPlugin. SNPs were filtered based on their missing data rate and minor allele frequencies (MAF) using VCFtools 33 .

Phylogenetic and population genomic analyses

SNPs with MAF ≥ 1% and missing data rate ≤ 50% were used for phylogenetic and population structure analyses. The maximum-likelihood (ML) phylogenetic tree was constructed using SNPhylo 34 with parameters “-r -M 0.5 -m 0.01 -l 0.1 -B 100” and visualized using the ggtree package 35 . PI 618817 ( Cucumis myriocarpus ) and PI 282446 ( C. heptadactylus ) were used as the outgroup. Principal component analysis (PCA) was performed using Plink-1.9 (ref. 36 ). Population structure analysis was performed using the STRUCTURE program 37 . A total of 11,745 SNPs with linkage disequilibrium (LD) decay ( r 2 ) < 0.4 were used for the analysis. To determine the most likely group number, STRUCTURE was run 20 times using 8000 SNPs randomly selected from the 11,745 SNPs, for each K ( K  = 2–20). The highest ∆K , which indicates the most likely number of clusters in the population, was obtained. After determining the best K ( K  = 3), we then ran STRUCTURE using all 11,745 SNPs with 10,000 iterations for each K ( K  = 2–4).

LD decay was measured by correlation coefficients ( r 2 ) for all pairs of SNPs within 500 kb that were calculated using PopLDdecay v3.27 ( https://github.com/BGI-shenzhen/PopLDdecay ) with the following parameters: -MaxDist 500 -MAF 0.05 -Het 0.88 -Miss 0.999. The maximum value of r 2 was calculated using all pairs of SNPs within 500 bp. The nucleotide diversity (π) and population fixation index ( F ST ) were calculated using Bio:PopGen implemented in the BioPerl package 38 . To visualize the pairwise F ST values among different groups, multidimensional scaling (MDS) was conducted using the cmdscale function in R to transform F ST values into two-dimensional values, which were used for plotting.

The USDA-GRIN database archives phenotypic data for Cucumis ( https://npgsweb.ars-grin.gov/gringlobal/cropdetail.aspx?type=descriptor&id=123 ). The phenotypic data of 13 important traits for cucumber, including three related to disease resistance (anthracnose, downy mildew, and gummy stem blight (GSB) resistance), three related to root knot nematode resistance (resistance to Meloidogyne hapla race 1, M. arenaria race 2, or M. incognita race 3), three related to fruit shelf life (weight loss, firmness loss, and shriveling), and four other traits (chilling tolerance, days to flower, root size, and fruit yield), were downloaded from the GRIN database. The phenotypic data were collected over the last 30 years by the Cucurbit Breeding program of North Carolina State University. Data sets for each trait were collected over multiple years and locations ( http://cucurbitbreeding.com ) for 750–950 cultigens per trait. Description of the data collection is available at  Supplementary Note . Phenotypic data from accessions genotyped in the present study were used for GWAS.

We used a total of 72,982 biallelic SNPs without any filtering to construct the kinship (K) matrix, which was used to correct for population structure and kinship in the GWAS analyses. For GWAS, the missing genotypes in the raw biallelic SNP dataset were imputed using the k-nearest neighbor (KNN) algorithm implemented in the fillGenotype software 39 . In order to obtain the optimal imputation accuracy and filling rate, three accessions with few missing genotypes (Amex 7735, NSL 32744, and PI 167052) were selected and 10%, 20%, and 30% SNP sites were randomly masked as missing genotypes for imputing. The imputation was performed using the fillGenotype with the following parameters: w (20, 30, 50, 65, 80), p (−3, −5, −7, −9), k (3, 5, 7, 9), and r (0.65, 0.7, 0.75, 0.8). The optimal combination of parameters (w = 30, k = 9, p  = −9, r  = 0.8) was selected after comparing the filling rate and imputation accuracy of each combination of parameters, to impute the missing genotypes in the raw dataset. Only biallelic imputed SNPs with minor allele frequency ≥ 1% and missing data rate ≤ 20% (a total of 28,650 SNPs) were used for GWAS. GWAS were performed using the linear mixed model (LMM) implemented in Fast-LMM 40 . The genome-wide significance thresholds of the GWAS were determined using the Bonferroni correction at α = 0.05 for significant and α  = 0.01 for extremely significant associations as described in Li et al. 41 . In this study, the significance thresholds of α  = 0.05 and α  = 0.01 corresponded to raw P values of 1.75 × 10 –6 and 3.49 × 10 −7 , or −log10( P ) values of 5.76 and 6.46, respectively.

Core collection selection

GenoCore 42 was used to select a subset of accessions that captured the majority of the allelic diversity of the 1234 cucumber accessions, with the following parameters: -d 0.01%, -cv 100%. Combined with phenotypic analysis, we obtained the final core collection containing 395 cucumber accessions, of which 354 were genotyped in the current study. The percentage of the allelic diversity captured by the 354 accessions in this core collection was determined using GenoCore. The core collection was further evaluated by PCA, using the same methods described above for the entire collection.

Genotyping of cucumber germplasm collection and variation identification

Seed was successfully germinated for 1234 cucumber accessions from the NPGS, which represents 94% of the collection (1314 accessions for which seed was available). Based on their geographic distribution (countries of origins), we classified these accessions mainly into seven groups, 216 from India/South Asia, 293 from East Asia, 113 from Central/West Asia, 161 from Turkey, 314 from Europe, 33 from Africa, 97 from North America, as well as 7 from other regions (Fig.  1 and Supplementary Table S 1 ). The accessions from India (184) along with 32 plant introductions (PIs) from the surrounding regions of Bhutan, Malaysia, Nepal, Myanmar, Pakistan, Sri Lanka, and Thailand were classified separately from accessions from other Asian countries, since India and the surrounding regions are considered as the center of origin of cultivated cucumber 6 , 43 . The Indian/South Asia group also included three accessions of C. s . var. hardwickii . In addition, since Turkey is a country straddling Asia and Europe, we put accessions from Turkey as an independent group. We genotyped these cucumber accessions, as well as two non-cucumber but closely related accessions PI 618817 ( C. myriocarpus ) and PI 282446 ( C. heptadactylus ), using the GBS technology, which generated a total of ~1.35 billion reads of 101 bp in length. The numbers of reads for each sample ranged from ~176 K to 4.8 million, with a median of 677 K reads (Supplementary Table S 1 ). A total of 554 K unique tags with at least 10 read counts, which corresponded to ~1.23 billion reads, were obtained and used for SNP calling. The 1.23 billion GBS reads were aligned to the reference Gy14 genome (version 2; http://cucurbitgenomics.org ), with 55.5% (0.69 billion corresponding to 279 K tags) aligned to unique positions and 17.3% (0.21 billion corresponding to 50 K tags) to multiple locations; the remaining 27.2% (0.33 billion corresponding to 225 K tags) unaligned reads were mainly from mitochondrion and chloroplast, as well as genome regions that were absent in the reference genome. Approximately 3.7% (9.5 Mb out of 258.6 Mb) of the Gy14 genome was covered by the aligned GBS reads, which is typical for reduced complexity GBS data 30 .

Size of the circles indicates the relative sampling size in each country

Based on the alignments, we identified a total of 114,760 variation sites, of which 113,854 were SNPs and 906 were small insertions/deletions (InDels). After retaining SNPs with ≤ 50% missing data (representing at least 617 accessions) and MAF ≥ 0.01 (i.e., SNPs present in at least 7 accessions), we obtained a total of 24,319 SNPs distributed across the cucumber Gy14 genome with an average of one SNP per 10.6 kb (Table  1 ). Only eight regions > 500 kb in the Gy14 genome were not covered by SNPs, and all these eight regions were centromeric or pericentromeric (Fig.  2a ). The distribution of MAF of these SNPs is shown in Fig.  2b . The average MAF was 0.13; nearly half (11,798; 48.5%) of the SNPs had MAF between 0.01 and 0.05. Among the 24,319 SNPs with ≤ 50% missing data and MAF ≥ 0.01, only those that were biallelic were retained as the final SNP dataset (23,552 SNPs) used in the downstream analyses, unless otherwise specified.

a SNP density across the seven cucumber chromosomes. Number of GBS-SNPs in each 500 kb non-overlapping window are shown. b Distribution of minor allele frequency (MAF) for the filtered SNPs

Phylogenetic relationships and population structure of the cucumber accessions

Using the final SNP dataset, we constructed a rooted ML tree to infer phylogenetic relationships among the cucumber accessions, using PI 618817 ( C. myriocarpus ) and PI 282446 ( C. heptadactylus ) as the outgroup (Fig.  3a and Supplementary File  1 ). Three major clades were identified. Consistent with India as the center of origin for cucumber, the clade with the deepest branches was the India/South Asia group. The remaining accessions were separated into two major clades. One mainly contained accessions from East Asia, while the second encompassed accessions from Central/West Asia, Turkey, Europe, Africa, and North America.

a Rooted maximum-likelihood phylogenetic tree of the 1234 cucumber accessions constructed using GBS-SNPs. PI 618817, C. myriocarpus , and PI 282446, C. heptadactylus were used as the outgroup. An enlarged version of the tree with searchable accession names is provided as Supplementary File  1 in pdf format. b Principal component analysis (PCA) of the 1234 cucumber accessions. The first two PCs explain about 20% of variance, with PC1 and PC2 explaining 10.80% and 8.99%, respectively. c Plot of ΔK values with K from 2 to 19 in the STRUCTURE analysis. d Population structure analysis of cucumber accessions with K from 2 and 4. Each accession is represented by a vertical bar. Each color represents one ancestral population, and the length of each colored segment in each vertical bar represents the proportion contributed by ancestral populations

PCA of these cucumber accessions illustrated a similar pattern of their phylogenetic relationships (Fig.  3b ). Our results are consistent with those reported in Qi et al. 10 , which also classified cucumbers into three primary groups, with the exception of the Xishuangbanna group, for which no accessions were included in our GBS set.

To investigate the population structure of cucumber, the Bayesian clustering algorithm implemented in the STUCTURE program 37 was first used to estimate ancestry proportions for each cucumber accession. ∆ K analysis showed that three populations ( K  = 3) represented the best number of clusters for these 1234 cucumber accessions (Fig.  3c ). As shown in Fig.  3d , at K  = 2, accessions from East Asia and India were clearly separated from other accessions. At K  = 3 (optimal), the India/South Asia group was clearly separated from the East Asia group. The population structure result at this optimal K was consistent with the phylogenetic tree and PCA results; all suggested three primary clusters in the cucumber accessions collected from NPGS. At K  = 4, a new subpopulation emerged mainly in accessions from Europe and North America. A large portion of accessions from Europe, North America, Africa, Turkey, and Central/West Asia showed genetic admixture, while most of the East Asia accessions had a homogeneous genetic background.

Within the India/South Asia clade were several subclades (Fig.  3a and Supplementary Fig.  S1 ). The Indian accessions within the U.S. NPGS were collected in two time periods: a first set of materials was entered into the system prior to 1972, and a second set collected in 1992. The accessions collected in 1992 were primarily from the states of Rajasthan, Uttar Pradesh, and Madhya Pradesh, representing regions in North and Central India that were largely missed in the prior collection 44 . The Indian accessions were differentially distributed among the different subclades, especially those from Rajasthan that were primarily associated with subclade 2, suggesting that the subclades, in part, reflect geographic distribution within India. Accessions from prior collections from South or Southwest India (Maharashtra, Karnataka, and Kerala) clustered in subclade 3. Subclade 1 primarily contained accessions from Madhya Pradesh in central India. The great majority of the East Asian accessions were collected from China. Those from Japan and South Korea largely clustered with each other; the remaining subclades were almost exclusively composed of accessions from China (Supplementary Fig.  S1 ). For accessions from Turkey, two subclades were identified, one clustered with accessions from Central/West Asia group, and the other clustered with accessions from Europe (Supplementary Fig.  S1 ). The North American accessions, also showed division into two distinct subclades. One group was largely comprised of pickling (processing) cultigens and the other of slicing (fresh market) cultigens (Supplementary Fig.  S1 ), reflecting the two predominant market classes produced in the US.

LD patterns, genetic diversity, and population differentiation in cucumber

The LD decay ( r 2 ) with increasing physical distance between SNPs was calculated for each group (Supplementary Fig.  S2 ). When the entire population was analyzed, the average physical distance over which LD decayed to half of its maximum value was around 24 kb ( r 2  = 0.0930; maximum r 2  = 0.1830). Variable LD decays were detected in different groups. The Africa group and the North American group had the longest physical distances over which LD decayed to half of its maximum value, 64 kb and 96 kb, respectively, while the India group had the shortest, 16 kb. The Europe, Turkey, East Asia, and the Central/West Asia groups showed comparable LD decay patterns and physical distances (48 kb, 40 kb, and 32 kb for Europe, East Asia, and Central/West Asia, respectively).

We then evaluated the genetic diversity within different groups. The average values of genome-wide nucleotide diversity (π) for Central/West Asia, Europe, North America, Africa, Turkey, East Asia, and India/South Asia groups were 0.87 × 10 −3 , 0.90 × 10 −3 , 0.93 × 10 −3 , 0.98 × 10 −3 , 0.81 × 10 −3 , 0.74 × 10 −3 , and 1.22 × 10 −3 , respectively. The π value of the India/South Asia group was higher than those of other groups, consistent with India being the center of origin of cultivated cucumber where cucumber accessions are expected to be more genetically diverse.

We further investigated population divergence among different groups by calculating pairwise fixation index ( F ST ) values. Pairwise weighted F ST values among North America, Central/West Asia, Africa, Turkey, and Europe groups ranged from 0.042 to 0.14, while the values between East Asia and other six groups ranged from 0.284 to 0.413, and between India/South Asia and other six groups from 0.176 to 0.284 (Supplementary Table S 2 ). Visualization of pairwise weighted F ST values using MDS showed a clear distinction between the East Asia group and other groups. F ST between East Asia vs. India/South Asia was 0.284 and between East Asia vs. the Western group (North America, Europe, Africa, and Central/West Asia) was 0.269. There was much less divergence among the North America, Europe, Turkey, Africa, and Central/West Asia groups, and between the Western group and India/South Asia (0.15) (Fig.  4 ). Collectively, both π and F ST values suggested that domestication and improvement of cultivated cucumbers from Indian cucumbers occurred independently in East Asia compared to other regions.

Multidimensional scaling of pairwise F ST values between different cucumber groups

Genome-wide association studies in cucumber

The high-density SNP markers combined with GWAS provide a powerful resource to identify quantitative trait loci (QTL) and possible candidate genes for important horticultural traits. We collected historical phenotypic data of cucumber accessions from the NPGS for 13 agronomic traits, which included three traits related to disease resistance (anthracnose, downy mildew, and GSB resistance), three related to root knot nematode resistance (resistance to Meloidogyne hapla race 1, M. arenaria race 2, or M. incognita race 3), three related to fruit shelf life (weight loss, firmness loss, and shriveling), and four other traits (cold tolerance, days to flower, root size, and fruit yield). For each trait, data were available for around 600–700 accessions that were genotyped using GBS in this study (Supplementary Table S 3 ). The phenotypic data largely followed normal distribution without significant skewness except for resistance to M. hapla race 1 (Supplementary Fig.  S3 ). GWAS were performed for these traits with the imputed SNPs, which had an imputation accuracy of > 99% and missing data filling ratio of > 96.5% (Supplementary Table S 4 ), using the LMM accounting for population structure and kinship. Significantly associated SNPs could be identified except for resistance to M. incognita race 3 and root size (Supplementary Table S 5 ).

GWAS for disease and nematode resistance

For anthracnose resistance, two regions on chromosome 7 were identified (Fig.  5a ). A total of 11 SNPs spanning one region (from 1.0 to 1.1 Mb) and a total of five SNPs spanning another region (from 12.52 to 12.55 Mb) were found to be significantly associated with anthracnose resistance (Supplementary Table S 5 ). Other significantly associated SNPs were identified at 33.1 Mb of chromosome 3 and 10.06 Mb of chromosome 5.

a GWAS for disease resistance traits including resistance to downy mildew, anthracnose, or gummy stem blight. b GWAS for root knot nematode resistance traits including resistance to Meloidogyne hapla race 1, M. arenaria race 2, or M. incognita race 3

For downy mildew resistance, a region on chromosome 5 spanning from 29.38 to 32.46 Mb was identified to contain 27 significantly associated SNPs (Fig.  5a and Supplementary Table S 5 ). Eight other SNPs significantly associated with downy mildew resistance were identified, with one on chromosome 3 (40.78 Mb), four on chromosome 5 (4.26, 6.54, 14.64, and 22.49 Mb), and three on chromosome 7 (9.55, 10.96, and 19.73 Mb).

For GSB resistance, three regions, one on chromosome 2, one on chromosome 5 and one on chromosome 7 were identified (Fig.  5a ). The region on chromosome 2 spanned from 30.67 to 31.83 Mb and contained four significantly associated SNPs; the region on chromosome 5 spanned from 28.68 to 31.34 Mb and contained 25 significantly associated SNPs (Supplementary Table S 5 ). Another two SNPs, on chromosome 3 (13.05 Mb) and 5 (23.05 Mb), respectively, were identified to be significantly associated with GSB resistance.

For root knot nematode resistance, no regions were identified to be significantly associated with resistance to M. incognita race 3; while a SNP on chromosome 1 (3.18 Mb) was identified to be significantly associated with resistance to M. arenaria race 2 (Fig.  5b ). Six SNPs, one on chromosome 3 (26.48 Mb), one on chromosome 5 (19.67 Mb), two on chromosome 6 (7.37 and 28.88 Mb), and two on chromosome 7 (1.35 and 16.72 Mb) were significantly associated with resistance to M. hapla race 1 (Supplementary Table S 5 ).

GWAS for fruit yield and physiological traits

Fruit yield trait in the cucumber accessions was investigated at two locations, Iowa and North Carolina. GWAS for fruit yield using data from each of the two locations as well as combined identified a total of nine significantly associated SNPs, one on chromosome 2 (30.07 Mb), two on chromosome 3 in a region at 27.86 Mb, three on chromosome 4 (27.00, 28.27 and 29.83 Mb), and three on chromosome 5 in a region spanning from 2.847 to 2.864 Mb (Supplementary Fig.  S4a and Supplementary Table S 5 ).

GWAS were performed for three traits related to fruit shelf life, weight loss, loss of firmness, and shriveling. Five SNPs, three on chromosome 4 (2.22 Mb and two at 28.78 Mb) and two on chromosome 7 (696 and 978 kb) were identified for weight loss, one SNP on chromosome 3 (27.23 Mb) was identified for loss of firmness, and one SNP on chromosome 2 (3.18 Mb) was identified for shriveling (Supplementary Fig.  S4b and Supplementary Table S 5 ).

For chilling tolerance, eighteen SNPs were identified, with ten on chromosome 1, one on chromosome 2, two on chromosome 4 and five on chromosome 7. For days to flower, six SNPs, one on chromosome 1, one on chromosome 3, two on chromosome 4, and two on chromosome 6 were identified (Supplementary Fig.  S5 and Supplementary Table S 5 ). No significant associations were found for root size.

Development of a publicly accessible core cucumber germplasm collection

Our main objective of developing a core collection from the cucumber accessions in the NPGS is to provide the community with a subset of representative cucumber accessions that can be used for future GWAS, QTL mapping, marker development, and gene cloning studies. The selected core collection would have a reasonable size (~400 accessions) and capture largely the allelic diversity of the entire collection, and also include accessions with some unique and important agronomic traits.

To develop this core collection, we first analyzed the 1234 cucumber accessions using the GenoCore program 42 . The results showed that the 720 top-ranked accessions captured 100% of the allelic diversity of the whole set, and the top 100, 200, 300, and 400 top-ranked accessions captured 93.87%, 97.09%, 98.47%, and 99.20% of the allelic diversity, respectively (Fig.  6a ). According to this analysis, we first selected 354 accessions which captured 95.9% of the allele diversity in the collection of the 1234 cucumber accessions. Of the 354 accessions, 70 (19.8%) were from India/South Asia, 35 (9.9%) from Central/West Asia, 94 (26.6%) from East Asia, 10 (2.8%) from Africa, 20 (6.0%) from North America, 74 (20.9%) from Europe, 48 (13.6%) from Turkey, and 3 from other regions (Supplementary Table S 6 ). PCA analysis of these 354 accessions in the core collection (Fig.  6b ) showed the nearly identical patterns to those of the 1234 accessions in the entire collection.

a Coverage of allelic diversity versus number of selected accessions analyzed by GenoCore. b Principle component analysis (PCA) of cucumber accessions. Red dots: accessions in the core collection; gray dots: accessions not in the core collection

An additional 41 historical varieties with important horticultural and disease resistance traits were added to this core collection, making the final core collection containing a total of 395 accessions. The additional accessions included some cucumber cultivars or germplasm that have played important roles in cucumber breeding in the US for the processing and fresh markets (Supplementary Table S 6 ).

Genetic characterization of the US NPGS cucumber collection

The genetic composition of the U.S. cucumber germplasm collection was characterized using high-throughput GBS analysis. A total of 1234 accessions, predominantly collected from India and South Asia, East Asia, Central and West Asia, Europe, North America, and Africa were genotyped, providing 279 K uniquely aligned sequence tags. From these data ~23.5 K biallelic SNPs representing minor alleles present in at least seven accessions (frequency > 0.01 and missing data rate < 0.5) were identified. With the exception of highly methylated centromeric or pericentromeric regions, the SNPs were well distributed at high density in the genome with an approximate frequency of 1 SNP per 10.6 kb. These data allowed for comprehensive analysis of phylogentic relationships, population structure, and LD patterns of accessions in the collection and provide a resource for genetic analysis and gene discovery.

Consistent with our current understanding about the geographic origin of cucumber 6 and prior phylogenetic analyses 9 , 10 , the U.S. cucumber PI collection comprised three major clades. The basal clade predominantly comprised accessions from India/South Asia, the presumed center of domestication for C. sativus . While this clade had the deepest branches suggesting greater divergence among members of this clade, the overall diversity of accessions from this region was reduced relative to the study of Qi et al. 10 . This is likely due to sampling. The 216 accessions from India/South Asia in the U.S. collection included only three (1.4%) accessions of C. s . var. hardwickii , a highly diverse wild botanical variety believed to be either a progenitor or a feral relative of the cultivated cucumber, C. s . var. sativus 6 , 11 , 44 . In contrast, 13 of 30 accessions (43.3%) from the Indian accessions studied by Qi et al. 10 were var. hardwickii .

From its origins in India and initial domestication ~3000 years ago, it appears that cucumber moved both East (to East Asia) and West (to Central and West Asia, Europe, Africa, and North America), following distinct trajectories in each case 27 , 45 . The strong differentiation between the East and West groups likely reflects a long period of divergent domestication (written Chinese records mentioning cucumber date as early as 164 BCE) as well as geographical isolation due to the Himalayan mountains 10 , 28 , 46 . Patterns of LD decay were consistent with the phylogenetic and population structure analyses. The Indian group had the shortest physical distance to reach half-maximal value (16 kb), vs. 32–48 kb for East Asia, Central/West Asia, and Europe. The reduced rate of LD for North American and Africa accessions (96 and 88 kb) may reflect the greater genetic relatedness of the samples in this collection, or the migration route for cucumber, which is thought to have been introduced into these regions comparatively recently from Europe 47 .

Several studies have indicated that the overall level of genetic diversity within cultivated cucumber is quite narrow, and that most of the genetic differentiation was observed between geographic regions or market classes 9 , 10 , 28 , 44 , 46 , 48 . Our phylogenetic analyses also reflected these sources of divergence. In addition to the separation observed among the three primary clades, we saw examples of differentiation within clades as evidenced by countries of origin, regions of collection within India, subgroups from Turkey, and between processing and fresh market cucumbers in North America. Among the Indian PIs, accessions from Madhya Pradesh, Uttar Pradesh, and Rajasthan were preferentially, but not exclusively, distributed in different subclades, suggesting diversity both within and between regions. Separation of accessions from Rajasthan relative to other regions in India was previously observed based on isozyme analysis performed following initial collection 44 . The current SNP-based analysis allowed for more nuanced assessment of relationships among the accessions. The Turkish germplasm also was associated with several subclades. For the two largest subclades containing Turkish accessions, one was extensively mixed with accessions from Central/West Asia, while the second was extensively mixed with accessions from Europe. Examination of collection locations within Turkey showed predominance of samples from the European-mixed subclade from western Turkey and samples from the Asian-mixed subclade from Eastern Turkey. There were some exceptions, however, possibly reflecting seed exchange across different regions of the country. Separation among the North American accessions reflected market class. As public and commercial breeding efforts have largely catered to either pickling or slicing cucumber, with delineated breeding efforts, it is not surprising to observe genetic divergence. Differentiation between pickling and slicing cucumbers also has been observed with RFLP markers and metabolomic analyses of cucumber fruit peels 28 , 49 .

Development of genomic breeding tools

An important value of genetic characterization of the collection is the development of genomic tools for breeders. QTL analyses of key traits of economic importance can allow for the development of markers for marker assisted selection, focusing phenotypic selection on population subsets containing desired markers and facilitating gene pyramiding for complex traits. The GWAS presented here using the high-density SNP markers and historical phenotyping data for several disease resistance and physiological traits show the identification of significantly associated genomic regions. At this time QTL have been mapped for a limited number of traits in cucumber. Of the traits examined here, recent studies have reported QTL for downy mildew, GSB, and flowering time 50 , 51 , 52 , 53 , 54 , 55 , 56 .

Recent QTL mapping studies for downy mildew resistances in two PI lines (PI 330628 or WI 7120, and PI 197088) identified eight resistance QTL, dm2.1, dm3.1, dm3.2, dm4.1, dm5.1, dm5.2, dm5.3 , and dm6.1 . Among them, dm2.1, dm4.1, dm5.2 and dm6.1 seem to be shared by the two PI lines 50 , 51 . Another cucumber accession, PI 197087, possesses multiple resistances to downy mildew (pre-2004 strain, by the dm1 locus), anthracnose (by the cla locus) and angular leaf spot (by the psl locus). Pan et al. 52 and Wang et al. 54 showed that dm1/cla/psl locus for the triple disease resistances in this PI line was controlled by the same staygreen gene ( CsSGR ), which was located in the short arm of chromosome 5 (~5 Mb in Gy14 V2.0). The several peaks on chromosome 5 detected from GWAS in this study (Supplementary Table S 5 ) seem to correspond well to dm1 , dm5.1 , dm5.2, and dm5.3 detected in Wang et al. 50 , 51 , 54 . In addition, the peak on chromosome 5 detected in GWAS for anthracnose resistance is likely the same as the dm1/cla/psl locus (CsSGR) originated from PI 197087 (ref. 52 , 54 ). However, no downy mildew QTL was detected on chromosome 4 in the natural population, whereas no QTL on chromosome 7 were detected from bi-parental mapping populations, which was identified in GWAS. These differences may reflect the power of QTL detection with different approaches. The different virulence structure of field downy mildew pathogens may also contribute to the observed differences.

QTL for GSB resistance have been recently identified in cucumber and melon. Two QTL mapping studies on GSB resistances from the wild cucumber ( C.s . var. hardwickii ) accession PI 183967 have been reported with some contradictory results 53 , 55 . Liu et al. 55 identified six QTL on chromosomes 3, 4, 5, and 6 ( gsb3.1, gsb3.2, gsb3.3, gsb4.1, gsb5.1, and gsb6.1 ) with gsb5.1 as a major QTL. On the other hand, Zhang et al. 53 identified five QTL ( gsb-s1.1, gsb-s2.1, gsb-s6.1, gsb-s6.2 , and gsb-s6.3 ) for resistance to GSB in PI 183967 with gsb-s6.2 having the largest effect. While not directly overlapping, gsb-s2.1 (ref. 53 ) and gsb5.1 (ref. 55 ) seem to be at nearby regions of the two peaks we identified from GWAS in this study on chromosomes 2 and 5, respectively, which obviously need further investigation to confirm. In addition, a recent report also identified a candidate gene for GSB resistance in melon located on chromosome 4 around 4.0 Mb 57 ; however, it does not appear to reside in syntenic regions 58 with the QTL identified this study.

QTL for flowering time was previously mapped on chromosomes 1, 2, 5, and 6 in recombinant inbred lines derived from a cross between an American pickling type and little leaf (ll) line H-19 (ref. 56 ), and on chromosomes 1, 5, and 6 in a cross between American pickling cucumber and semi-wild var. Xishuangbanna 16 . There appears to be potential overlap among the identified QTL in those studies and the current GWAS. In all three studies significant regions were located on the distal end of chromosome 1 and on the central region of chromosome 6, suggesting potentially robust loci influencing flowering time over a range of genetic backgrounds.

Genetic characterization of accessions within a germplasm collection and knowledge of their genetic relationships also enables definition of a core population, i.e., a subset of the full collection that captures the majority of diversity of the species 29 , 59 . Core collections can greatly facilitate breeding and preservation efforts by providing a common starting point for screening the population for traits of importance for crop improvement. By allowing for reduced numbers in the initial screening stages, they can be especially helpful when phenotyping a trait of interest that is particularly expensive or labor-intensive. A defined core population also can allow for more focused management of seed supplies for distribution. While core populations can be defined using geographic or phenotypic characteristics, establishment of maximally valuable core populations, relies on effective measures of genetic diversity among the accessions 29 .

A prior core of 147 accessions from the NPGS cucumber collection was proposed based on isozyme analysis of 970 PIs, along with data regarding disease resistance (angular leaf spot, anthracnose, downy mildew, rhizoctonia fruit rot, and target leaf spot), water and heat stress tolerance, and morphological characteristics 45 . Current next-generation sequencing technology allows for more robust genotypic assessment. From the analyses performed here, we have designed a core collection of 354 accessions that represent 96% of the genetic variation present in the NPGS. Approximately half (76) of the PIs from the prior core 45 were included in the current core collection. It has also been recommended that germplasm collections include important breeding materials where key traits have been introgressed into cultivated inbred lines 4 , 29 . To this end, the proposed core also includes 41 accessions, including historical cultivars, widely used breeding lines and individuals with identified traits of interest. To make the core maximally valuable for future breeding efforts and genetic studies, we are in the process of deep resequencing of the genomes, and creating seed stocks of the selected accessions in the final core collection, under the current USDA CucCAP project ( https://cuccap.org/ ). Both the genotype data and seeds of the core collection will be accessible to the public.

Conclusions

This work has provided detailed genetic analysis of the cucumber germplasm collection maintained by the US NPGS, which includes more than 1200 accessions collected throughout the world. The information provided by the GBS data has provided deep insight into the diversity present within the collection and genetic relationships among the accessions. These data can be used for genetic analyses such as GWAS to identify potential genomic regions associated with valuable traits, and for informed management of the collection to conserve genetic resources. Development of the genetically informed core collection will enable more efficient genetic analyses that can be coupled with sophisticated genomic tools to facilitate crop improvement. While it is clear that a great deal of valuable diversity is represented among the materials in the NPGS collection, these observations also illustrate the importance of careful and extensive germplasm collection to ensure that our collections reflect the extant diversity available worldwide.

Data availability

Raw GBS reads for all individual cucumber accessions have been deposited in the NCBI sequence read archive (SRA) under accession numbers SRP149275 and SRP149431. Raw and filtered SNPs in VCF format are available at ftp://cucurbitgenomics.org/pub/cucurbit/GBS_SNP/cucumber.

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Acknowledgements

We thank Dr. Jim Smith (MSU) for helpful advice regarding phylogenetic analyses and Dr. Marivi Colle (MSU) for assistance in developing the high throughput DNA extraction methods. This research was supported by grants from USDA National Institute of Food and Agriculture Specialty Crop Research Initiative (2015-51181-24285).

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Z.F., R.G., and Y.W. designed and managed the project. S.A.H., C.J., A.O.R-M., and Y.W. prepared and handled samples. T.C.W. collected phenotypic data. X.W., K.B., U.K.R., and Y.B. performed data analyses. X.W., Z.F., and R.G. wrote the paper.

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Wang, X., Bao, K., Reddy, U.K. et al. The USDA cucumber ( Cucumis sativus L.) collection: genetic diversity, population structure, genome-wide association studies, and core collection development. Hortic Res 5 , 64 (2018). https://doi.org/10.1038/s41438-018-0080-8

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REVIEW article

Research advances in genetic mechanisms of major cucumber diseases resistance.

• 1 Key Laboratory for Quality and Safety Control of Subtropical Fruits and Vegetables, Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, Ministry of Agriculture and Rural Affairs, College of Horticulture Science, Zhejiang Agriculture and Forestry University, Hangzhou, China
• 2 Ministry of Agriculture Key Laboratory of Biology and Genetic Resource Utilization of Rubber Tree, State Key Laboratory Breeding Base of Cultivation and Physiology for Tropical Crops, Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Danzhou, China
• 3 College of Jiyang, Zhejiang Agriculture and Forestry University, Zhuji, China
• 4 Institute of Ecological Civilization, Zhejiang Agriculture and Forestry University, Hangzhou, China

Cucumber ( Cucumis sativus L.) is an important economic vegetable crop worldwide that is susceptible to various common pathogens, including powdery mildew (PM), downy mildew (DM), and Fusarium wilt (FM). In cucumber breeding programs, identifying disease resistance and related molecular markers is generally a top priority. PM, DM, and FW are the major diseases of cucumber in China that cause severe yield losses and the genetic-based cucumber resistance against these diseases has been developed over the last decade. Still, the molecular mechanisms of cucumber disease resistance remain unclear. In this review, we summarize recent findings on the inheritance, molecular markers, and quantitative trait locus mapping of cucumber PM, DM, and FM resistance. In addition, several candidate genes, such as PM, DM, and FM resistance genes, with or without functional verification are reviewed. The data help to reveal the molecular mechanisms of cucumber disease resistance and provide exciting new opportunities for further resistance breeding.

Introduction

Cucumber ( Cucumis sativus L.) is a popular vegetable grown on a large scale worldwide. It has an edible fruit with refreshing tastes and is enriched with vitamin E. In the recent years, with the increasing cultivation area of cucumber, it has gradually moved towards a large-scale planting model. However, because cucumber is susceptible to horticultural diseases, including powdery mildew (PM), downy mildew (DM), Fusarium wilt (FW), Verticillium wilt, Cladosporium cucumerinum , Corynespora leaf spot, green mottle mosaic virus, and bacterial soft rot, it does not help for industrialized production, which results in substantial economic losses to cucumber producers. Among the diseases, PM, DM, and FW are the serious main fungal diseases of cucumber that result in severe production and quality losses ( Block and Reitsma, 2005 ; Zhang et al., 2016 ; Vakalounakis and Lamprou, 2018 ). Several effective approaches have been widely used to control these diseases, such as various fungicides, biofungicides, and grafting. However, the variable adaptability of pathogens, fungicides residues on plants and in the environment, and higher production costs associated with these approaches indicate that better methods are required ( Mahmood et al., 2016 ; Chen et al., 2021 ). Therefore, breeding more resistant cultivars is an efficient approach to control cucumber diseases and understanding the genetic and molecular mechanisms of cucumber disease resistance is a crucial focus of cucumber breeding programs.

There is no conclusive genetic data on cucumber disease resistance at present. Some studies have shown that PM, DM, and FW resistance are quantitative traits controlled by multiple genes, respectively. For instance, the resistance to PM is controlled by a recessive single gene, and susceptibility is controlled by partial dominant genes ( Nie et al., 2015a ). In cucumber, the DM resistance was controlled by multiple recessive genes and has the duplicate recessive epistasis and the additive effects data confirmed the detected 14 quantitative trait loci (QTLs) for DM resistance ( Innark et al., 2020 ). Dong et al. (2019) found that the inheritance of FW resistance in cucumber is a quantitative resistance trait controlled by multiple genes, including two pairs of additive dominance-epistatic major genes and an additive-dominance polygene. However, resistance to PM, DM, and FW is also controlled by a single gene. For example, a single recessive gene, pm , for PM resistance in leaves, has been mapped to an approximately 468 kb region on chromosome 5 in IL52 ( Zhang et al., 2018 ). A recessive resistance gene, dm-1 , has been identified in many DM-resistant plant introduction (PI) lines, including PI 197087, Gy4, Chipper, and the Market more series ( Barnes and Epps, 1954 ; Wehner and Shetty, 1997 ; Call et al., 2012a ). Foc has been incorporated in the Dutch-type cucumber hybrids and has widely controlled FW of cucumber for 40 years ( Vakalounakis and Fragkiadakis, 2003 ). Variety is a major factor in the inheritance of cucumber disease resistance. At present, the mapping population of cucumber PM-resistance genes has been mainly constructed using PI 197088, S06, WI 2757, H136, K8, and IL52. In the constructed segregation population, 19 possible PM resistance QTLs were identified ( Wang et al., 2020 ). In the population generated using the high-resistance variety PI 197088, pm1.1 , pm1.3 , pm4.3 , pm5.1 , pm5.3 , pm5.4 , pm6.2 , pm6.3 , and pm7.1 are the main resistance QTLs and pm4.1 and pm6.3 are two major QTLs in the population constructed using high-resistance variety S06. Additionally, pm5.3 is the most important QTL for PM resistance in the population constructed using the high-resistance cultivar IL52 ( Sakata et al., 2006 ; Liu et al., 2008 ; Fukino et al., 2013 ; Yoshioka et al., 2014 ; Zhang et al., 2018 ). Most PM-related genes are closely linked to DM-related genes; therefore, they may also play equally important roles in DM resistance ( Wang et al., 2018 ; Zhang et al., 2018 ). The materials used for mapping genes associated with PM are also used for selecting genes associated with DM. At present, the DM-resistance gene mapping population has mainly been constructed using PI 197085, PI 197088, WI 7120 (PI 330628), WI 2757, S94, TH118FLM, IL52, and K8. In total, 16, 5, and 2 QTLs have been identified in PI 197085, PI 330628, and WI 2757, respectively ( Wang et al., 2020 ). For example, Wang et al. (2018) developed 55 microsatellite markers and found that dm5.1 , dm5.2 , and dm5.3 are the main resistance QTLs in the highly resistant variety PI 197088 and dm1.1 , dm2.1 , and dm6.2 are the main sensitivity-related QTLs in the highly susceptible variety Coolgreen. Li et al. (2018) used 141 simple sequence repeat (SSR) markers to identify 5 QTLs, namely, dm1.1 , dm3.1 , dm4.1 , and dm5.1/dm5.2 , among which, dm4.1 is a major resistance QTL in the cross-population derived from PI 197088 and Changchunmici. The development of resistance mechanisms against FW occurred more slowly than against PM and DM and few materials resistant to FW have been identified. Dong et al. (2019) detected a major effect QTL, fw2.1 , in a 1.91-Mb region on chromosome 2 using F2 segregating populations derived from Superina (P1) and Rijiecheng (P2). Additionally, different identification and evaluation methods, mapping population, infection site, and pathogen races also influence the inheritance of cucumber disease resistance ( Vakalounakis and Lamprou, 2018 ; Innark et al., 2020 ; Wang et al., 2020 ). The epigenetic variations also play important roles in crop disease resistance and are affected by environmental factors ( Zhi and Chang, 2021 ), but the epigenetic regulation in cucumber disease resistance has not been found yet.

Many candidate genes for PM, DM, and FW disease resistance in cucumber have been identified using genetic mapping as well as transcriptomic and proteomic analyses and several genes have been cloned for functional verification. The candidate genes for these diseases are all involved in plant hormone signal transduction, cell redox homeostasis, and transcriptional regulation. In PM disease, the Mildew Resistance Locus O ( MLO ) -like genes, including CsaMLO1–13 , but especially CsMLO1 , −8 , and −11 , are the most studied PM genes in cucumber ( Schouten et al., 2014 ; Nie et al., 2015b ; Berg et al., 2017 ) and MLO-based PM resistance caused by the formation of cell wall depositions (papillae) by the plant cell directly beneath the site of PM penetration ( Wolter et al., 1993 ), but the function is not yet unraveled. The candidate genes for DM resistance are involved in various metabolic pathways. For example, STAYGREEN ( CsSGR ), which is involved in the chlorophyll degradation pathway, plays important roles in DM disease resistance ( Wang et al., 2019 ) and the transient expression of CsLRK10L2 , which is a Damage-associated Molecular Pattern Molecule (DAMP) oligogalacturonan receptor and is involved in the breakdown of pectin, in Nicotiana benthamiana ( N. benthamiana ) leaves causes necrosis and results in high DM resistance ( Berg et al., 2020 ). Several candidate genes of both DM and PM resistance have also been identified. The gene Csa5M622830.1 , a GATA transcriptional factor gene, may prevent supplement nutrition from reaching DM and PM pathogens ( Zhang et al., 2018 ). Compared with PM and DM, fewer candidate genes resist to FW have been identified. However, resistance to FW is enhanced in transgenic cucumber harboring Ginkbilobin2-1 ( GNK2-1 ) ( Liu et al., 2010 ). Additionally, a great number of candidate microRNAs (miRNAs), long non-coding RNAs (lncRNAs), proteins, and metabolites related to DM, PM, and FW in cucumber have also been identified ( Li et al., 2011 ; Xu et al., 2019 , 2021 ; Nie et al., 2021 ; Sun et al., 2021 ).

Although a number of molecular markers, QTLs, and candidate genes have been identified, the genetic mechanisms of cucumber disease resistance are not well understood. Here, we independently review the genetic mechanisms of cucumber resistance to PM, FW, and DM and also provide new insights into future management strategies.

Inheritance, Quantitative Trait Loci Mapping, and Candidate Genes of Cucumber Resistance to Powdery Mildew

Powdery mildew mainly invades cotyledons, leaves, and stems, resulting in yellow, crisp dry leaves in which photosynthesis is seriously affected, thereby reducing cucumber yield. PM in cucumber is commonly caused by Podosphaera xanthii ( Sphaerotheca fuliginea ) and Golovinomyces cichoracearum ( Erysiphe cichoracearum ) ( Block and Reitsma, 2005 ), which share the characteristics of frequent infection, short incubation period, and strong transmission. They also can occur annually during cucumber production.

Inheritance of Powdery Mildew Resistance in Cucumber

A classical genetic analysis demonstrated that cucumber PM resistance is a quantitative trait controlled by multiple recessive genes in different germplasms ( Smith, 1948 ; Kooistra, 1968 ; Morishita et al., 2003 ; He et al., 2013 ). Early in 1948, Smith (1948) suggested that PM resistance in Puerto Rico 37 was controlled by recessive genes and then, associated recessive genes were identified in the PI 2008151 and Natsufushinari varieties ( Kooistra, 1968 ). The two recessively inherited genes linked to the QTL in chromosome 5 are responsible for PM in WI2757 ( He et al., 2013 ). Additionally, studies have shown that PM resistance in cucumber is controlled by a single recessive gene. A single recessive gene pm for PM resistance in leaves has been mapped to an approximately 468 kb region on chromosome 5 in IL52 ( Zhang et al., 2018 ). The resistance to PM in the stem of NCG-12 is also controlled by a single recessive nuclear gene ( pm-s ) ( Liu et al., 2017 ). The recessive inheritance of PM is not convenient to use in cucumber breeding ( Xu X. et al., 2016 ). The temperature-dependent PM resistance in PI 197088-5 is due to one recessive gene and another incompletely dominant gene ( Morishita et al., 2003 ). Shen et al. (2011) found that cucumber PM traits are determined by the interaction of major genes and polygenes in the JIN 5-508 variety and the inheritance of major genes dominates. Xu X. et al. (2016) first reported the dominantly inherited major-effect QTL ( Pm1.1 ) for PM in the Jin5-508-derived SSSL0.7 line. However, quantitative resistance under polygenic control is generally more durable than that conferred by a single dominant gene ( Kelly and Vallejo, 2006 ). The inheritance of cucumber disease resistance is dependent on the variety and material ( Wang et al., 2020 ) and the genetic laws governing cucumber PM resistance are still not well understood.

Molecular Markers and Quantitative Trait Loci of Powdery Mildew Resistance in Cucumber

Effective molecular markers and QTLs controlling resistance to PM in cucumber have also been reported in recent years (de Ruiter et al., 2008 ; Fukino et al., 2013 ; He et al., 2013 ; Nie et al., 2015a ; Wang et al., 2019 ). Various molecular markers have been used for mapping PM-associated loci in different cucumber species. In total, 140 PM-associated Specific-locus Amplified Fragment Sequencing (SLAFs) and two hot regions ( pm5.3 and pm6.1 ) have been identified on chromosomes 1 and 6 using an F2 segregating population derived from H136 as the susceptible parent and BK2 as the resistance donor ( Zhang et al., 2015 ). In total, 17 SSR markers have been discovered to be linked to the pm-s gene, which maps to chromosome 5 between the pmSSR27 and pmSSR17 markers ( Liu et al., 2017 ). The introgression of the 6.8-Mb segment that contains 3,016 single nucleotide polymorphisms (SNPs) causes the phenotypic variation in PM resistance between SSL508-28 and D8 ( Xu et al., 2017 ) and this region, pm5.1 , is consistent with major loci for PM resistance found in many studies ( Nie et al., 2015a ; Xu Q. et al., 2016 ; Wang et al., 2018 ). In total, 113 SNP and InDel markers significantly associated with PM resistance have been identified on chromosomes 4 and 5 using a genome-wide association analysis (GWAS) ( Tan, 2021 ). Additionally, four QTLs ( pm1.1 , pm2.1 , pm5.1 , and pm6.1 ) have been identified on chromosomes 1, 2, 5, and 6 using the recombinant inbred line (RIL) population derived from a cross between PI 197088 and the susceptible line Coolgreen. Among them, pm5.1 is the major-effect QTL, explaining 32.4% phenotypic variance, whereas the minor-effect QTL, pm6.1 , contributed to disease susceptibility ( Wang et al., 2018 ). Recently, pm5.2 (30% R2 at LOD 11) and pm6.1 (11% R2 at LOD 3.2) conferred PM resistance in an F2 population derived from a cross between PM-R (resistant) and PM-S (susceptible) ( Zhang C. et al., 2021 ). After further studies on the segregation populations constructed from PI 197088, S06, WI 2757, H136, K8, and IL52, 19 possible QTLs for PM resistance were mapped ( Wang et al., 2020 ). Moreover, PM resistance QTLs are also organ-dependent in cucumber. The disease indices of the hypocotyl, cotyledon, and true leaf of WI2757 were analyzed by multiple QTL mapping. pm5.1 was the major QTL for cotyledon resistance, pm5.2 controlled hypocotyl resistance, pm1.1 and pm1.2 controlled leaf resistance and both the minor QTLs, pm3.1 and pm4.1 , caused leaves or hypocotyls to have an increased PM susceptibility ( He et al., 2013 ). Liu et al. (2017) showed that pm-s , located on chromosome 5, controls PM resistance in cucumber stem and the gene Csa5G623470 , encoding an MLO protein, is closely related to the PM resistance of stem. Environmental factors also play important roles in the resistance to PM. Sakata et al. (2006) constructed a cucumber RIL using the PI197088-1 variety, resistant to PM, and the Santou variety, susceptible to PM, under both the high and low temperatures. Only one QTL played a role at high (26°C) and low (20°C) temperatures, which suggested that resistance was related to temperature. This was the first study on the QTL mapping of PM resistance genes at different temperatures. Like PM, DM is also an important disease in cucumber production. Many PM QTLs or genes are closely linked to DM QTLs or genes; consequently, they may also play equally important roles in DM resistance ( Wang et al., 2018 ; Zhang et al., 2018 ). For example, pm2.1 , pm5.1 , and pm6.1 associated with PM QTLs are colocalized with the DM QTLs dm2.1 , dm5.2 , and dm6.1 , respectively ( Wang et al., 2018 ). These studies showed inconsistent results regarding the number and locations of QTLs underlying PM and this may be due to differences in the germplasms, genetic maps, analysis methods, and environmental conditions.

Candidate Genes or Proteins Involved in the Powdery Mildew Resistance of Cucumber

In the recent years, candidate genes or proteins associated with PM resistance have been identified using transcriptomic and proteomic analyses and genetic mapping. Differentially expressed genes (DEGs) have been identified between PM-resistant species and susceptible species, such as SSL508-28 and D8, XY09-118 and Q10, BK2 and H136, and NILs of S1003 and Near Iso-genic Lines (NIL) ( pm5.1 ), using transcriptomes ( Xu et al., 2017 ; Nie et al., 2021 ; Zhang P. et al., 2021 ; Zheng et al., 2021 ). These DEGs function in plant hormone signal transduction, phenylpropanoid biosynthesis, phenylalanine metabolism, ubiquinone and other terpenoid-quinone biosynthesis, endocytosis, plant–pathogen interaction, and Mitogen-activated Protein Kinases (MAPKS). In particular, genes encoding the transcriptome factors (WRKY, NAC, and TCP), peroxidase, nucleotide-binding site (NBS), glucanase, and chitinase have been analyzed ( Zhang P. et al., 2021 ; Zheng et al., 2021 ). The miRNAs Csa-miR172c-3p and Csa-miR395a-3p are upregulated in PM-resistant D8 and Csa-miR395d-3p and Csa-miR398b-3p are downregulated in PM-susceptible SSSL508-28, suggesting that their target genes AP2 , bHLH , Dof , UGT , and LASPO may play important roles in PM-inoculated cucumber leaves ( Xu et al., 2020 ). Nie et al. (2021) showed that 49 differentially expressed lncRNAs may function as target mimics for 106 miRNAs during cucumber_PM interaction, including miR156 , miR159 , miR164 , miR166 , miR169 , miR171 , miR172 , miR6173, miR319 , miR390, miR393 , miR396 , and miR5658 . Moreover, differentially regulated processes, proteins, and accumulated metabolites between different PM-resistant materials have also been detected, including flavonoid, hormones, fatty acid, diterpenoid metabolism, tetrapyrrole biosynthetic process, sulfur metabolic process, and cell redox homeostasis ( Xu et al., 2019 ; Zhang P. et al., 2021 ).

A larger number of potential genes related to PM in cucumber have been identified using genetic mapping. Nie et al. (2015a) delimited the recessive major QTL pm5.1 for PM resistance in an approximately 1.7-kb region between markers UW065021 and UW065094 and they identified an MLO-like gene CsMLO1 , which encodes a cell membrane protein, as a candidate gene for PM resistance ( Nie et al., 2015b ). Schouten et al. (2014) obtained 13 MLO homologs, CsaMLO1-13 , in cucumber. Among them, the ectopic expression of CsMLO1 in the PM-resistant Atmlo2-Atmlo12 double-mutant results in PM sensitivity recovery. The overexpression of CsaMLO1 or CsaMLO8 completely restores PM susceptibility in a tomato mlo mutant, whereas the overexpression of CsaMLO11 only partially restores PM susceptibility ( Nie et al., 2015b ; Berg et al., 2017 ). To date, only MLO genes in cucumber have been functionally verified as being involved in PM resistance. In addition to MLO genes, other candidate PM resistance genes have been identified. Csa1M064780 and Csa1M064790 , encoding a cysteine-rich receptor-like protein kinase, are the most likely candidate PM resistance genes ( Xu Q. et al., 2016 ). The single recessive gene Csa5M622830 , which encodes a GATA transcriptional factor, is likely the gene for the complete PM resistance introgressed from Cucumis hystrix ( Zhang et al., 2018 ). CsGy5G015660 , which encodes a putative leucine-rich repeat receptor-like serine/threonine-protein kinase, is currently considered a strong candidate gene for PM resistance in cucumber ( Liu et al., 2021 ; Zhang C. et al., 2021 ). Moreover, proteins related to PM resistance have also been identified and functionally verified. Two NBS-Leucine-rich Repeat (LRR) proteins (CsRSF1 and CsRSF2), closely correlated with Abscisic Acid (ABA) and Gibberellin (GA) signals in cucumber, are predicted to have a similar domain sequence with the Arabidopsis PM-resistance protein RESISTANCE TO POWDERY MILDEW8 (RPW8) ( Xiao et al., 2001 ). The transient silencing of CsRSF1 and CsRSF2 reduces the resistance of cucumber to PM, whereas the transient overexpression of CsRSF1 and CsRSF2 improves the resistance of cucumber to PM ( Wang et al., 2021 ). Transcription factors, such as GRAS, DNA-binding with One Finger (DoF), Eukaryotic Initiation Factor 2 (eIF2α), Polygalacturonase (PG), UDP-Glycosyltransferase (UGT), and Serine/threonine Protein Kinases (STPKs) and their target genes, are also differentially expressed after PM inoculation ( Zhong, 2020 ). Translationally Controlled Tumor Protein (TCTP) is a highly conserved and multifunctional protein and CsTCTP1 may regulate the defense responses of cucumber or ABA signaling to control PM disease in cucumber. CsTCTP2 may regulate the Target of Rapamycin (TOR) signal in response to PM stress ( Meng et al., 2018 ). These studies provide new insights into cucumber responses to PM and the potential genes related to PM will be highly helpful in breeding cucumber varieties with enhanced PM resistance.

Inheritance, Quantitative Trait Loci Mapping, and Candidate Genes of Cucumber Resistance to Downy Mildew

Downy mildew of cucumber is caused by the obligate biotrophic oomycete Pseudoperonospora cubensis . It mainly infects leaves, but can also harm stems and inflorescences. It can occur from seedling to adult stage, but is particularly prevalent when cucumber enters the harvest stage. During the period of seedling infection, irregular chlorotic and withered yellow spots are produced on the reverse sides of cotyledons. A gray-black mold layer is produced when the plant becomes wet and cotyledons die when the infection is serious. During the adult stage, the disease gradually spreads upward from the lower leaves. At the beginning of the disease, light green water-immersion spots appear on the backs of the leaves. At the middle stage of the disease, the leaf spots fade from green to light yellow and the leaf backs become yellowish-brown. At the later stage, the disease spots converge and shrink upward from the leaf edges and finally, the whole leaf withers. In serious cases, all the leaves on the plant die ( Zhang et al., 2016 ).

Inheritance of Downy Mildew Resistance in Cucumber

Researchers have studied the inheritance of cucumber DM resistance. However, due to different resistance germplasms and inconsistent identification methods, there is no consensus on the genetic laws governing cucumber DM resistance. As early as 1942, DM-resistant lines were screened and DM resistance is controlled by a recessive resistance gene, dm-1 , in many resistant PI lines, including PI 197087, Gy4, Chipper, and the Marketmore series ( Jenkins, 1942 ; Barnes and Epps, 1954 ; Wehner and Shetty, 1997 ; Call et al., 2012a ). Simultaneously, multiple recessive genes are also involved in the regulation of cucumber DM resistance in resistant germplasms, including cucumber varieties WI4783, Wisconsin SMR18, K8 and K18, PI19708, CSL0067, and CSL0139 ( Doruchowski and Lakowska-Ryk, 1992 ; Zhang et al., 2013 ; Szczechura et al., 2015 ; Wang et al., 2016 ). Call et al. (2012b) identified three highly DM-resistant materials, PI 197088, PI 330628, and PI 605996, from 1,300 cucumber collections. Among them, PI 197088 is the most studied for DM resistance, with multiple genes being controlled in breeding programs ( Li et al., 2018 ; Liu et al., 2021 ). PI 197088 also has high resistance to PM. There are different genetic bases of DM-resistant germplasms. Therefore, the identification of DM-associated molecular markers and QTLs in various resistant materials may help to increase the inheritance of DM through breeding programs.

Molecular Markers and Quantitative Trait Loci of Downy Mildew Resistance in Cucumber

A variety of DM-associated QTLs has been identified in different varieties using Sequence Characterized Amplified Regions (SCAR), SSR, and SNP markers in recent years. The genetic linkage map was constructed using 66 polymorphic SSR markers and using this linkage map, 14 QTLs have been detected by evaluating DM in cotyledons as well as first and second true leaves after inoculation. LG5.1, located between the SSR03943 and SSR19172 markers, was detected at all the leaf stages ( Innark et al., 2020 ). Based on the linkage map having 328 SSR and SNP markers, dm4.1 , and dm5.1 , compared with dm2.1 and dm6.1 , were determined to be the major effect of QTL ( R 2 = 15–30%) with additive effects and this has been reproducibly detected in four environments (US2013, US2014, IT2013, and NL2013) ( Wang et al., 2016 ). In total, five QTLs associated with DM resistance have been identified on chromosomes 1, 3, 4, and 5 in seven independent experiments and dm4.1 , explaining 27% of the phenotypic variance, has been reliably detected in all the indoor experiments ( Li et al., 2018 ). The DM candidate QTLs related to DM have been detected using diverse evaluation methods that consist of different plant organs (cotyledons and true leaves), developmental stages (seedlings and adult plants), and evaluation criteria (lesion expansion and sporulation extent) and the dm1.1 QTL has the largest effect on resistance among the nine QTLs detected ( Yoshioka et al., 2014 ). In addition to QTL mapping methods, bulked segregant analyses (BSAs), next-generation sequencing (NGS), and GWASs have been the most rapid and effective ways of studying the genetic inheritance of DM resistant in cucumber. In total, five QTLs ( dm2.2 , dm4.1 , dm5.1 , dm5.2 , and dm6.1 ) have been identified and dm2.2 has the largest effect on DM resistance as assessed by combining BSA and NGS methods based on SNP markers ( Win et al., 2017 ). Additionally, 18 QTLs have been detected through the GWAS of a core database of 97 cucumber lines, but only six QTLs ( dmG1.4 , dmG4.1 , dmG4.3 , dmG5.2 , dmG7.1 , and dmG7.2 ) are associated with stable DM resistance ( Liu et al., 2021 ). To date, PI 197085, PI 197088, WI 7120 (PI 330628), WI 2757, S94, TH118FLM, IL52, and K8 have been used for mapping QTLs associated with DM resistance. Different cucumber germplasm resources may show stable genetic bases and QTLs for DM. For example, dm5.1 and dm5.2 have been detected in five resistance sources ( Wang et al., 2020 ). New QTLs have also been detected in commonly used disease-resistant materials. In PI 197087, Berg et al. (2020) focused on a QTL on chromosome 4-DM4.1 in the NILs produced by PI 197087 and a susceptible cucumber line (HS279) and this contained three sub-QTLs: DM4.1.1 that affects pathogen-induced necrosis, DM4.1.2 that has additive effects on sporulation, and DM4.1.3 that has recessive effects on chlorosis and sporulation. In general, the DM-associated QTLs varied depending on the germplasm and plant tissue as well as the developmental stage used in these analyses.

Candidate Genes or Proteins Involved in the Downy Mildew Resistance of Cucumber

A series of candidate genes or proteins related to DM resistance have been identified in cucumber through transcriptome profiling, proteomic analysis, and fine mapping. A large number of DEGs between DM-resistant and susceptible materials were identified by transcriptome analyses and these DEGs are involved in multiple defense response-related functions, including response: hormone signaling, regulation of nutrient supply, pathogen-associated molecular pattern recognition, signal transduction, reactive oxygen species and lignin accumulation, cell cycle, protein binding and metabolism, and transcriptional regulation ( Li et al., 2011 ; Burkhardt and Day, 2016 ; Gao et al., 2021 ). For example, five genes play important roles in the cucumber DM defense pathway: Csa5G139760 encodes an acidic chitin endonuclease, Csa6G080320 encodes a kinase having an LRR domain and transmembrane domain, Csa5G471600 is a retroviral receptor-like protein, and Csa5G544050 and Csa5G564290 encode the RNA-dependent RNA polymerase gene ( Gao et al., 2021 ). Consistently, differentially expressed proteins between the resistant and susceptible cucumber lines have also been identified and most of these proteins focus on cell rescue, defense, and energy metabolism ( Sun et al., 2021 ). Zinc finger-homeodomain (ZHD) proteins encode a family of plant-specific transcription factors that are responsive to DM in cucumber, such as CsZHD1–3 , CsZHD6 , CsZHD8 , and CsZHD10 ( Lai et al., 2021 ). Many novel QTLs for DM resistance in different cucumber species have been detected, such as dm2.1 , dm4.1 , dm4.1.2 , dm4.1.3 , dmG2.1 , and dmG7.1 ( Win et al., 2017 ; Berg et al., 2020 ; Liu et al., 2021 ), and these precise molecular markers and QTLs for DM resistance are helpful for the consequent fine mapping and positional cloning of QTLs. Liu et al. (2021) identified seven DM-resistance candidate genes using GWAS, including Csa1G575030 for dmG1.4 , Csa2G060360 for dmG2.1 , Csa4G064680 for dmG4.1 , Csa5G606470 for dmG5.2 , and Csa7G004020 for dmG7.1 . Among them, Csa5G606470 is a WRKY transcription factor and it was also identified within the DM-associated QTL dm5.2 using a Bulked Sergeant Analysis with Whole-genome Resequencing (BSA-seq) analysis ( Zhang et al., 2018 ). Cucumber CsSGR encodes a magnesium dechelatase and plays critical regulatory roles in the chlorophyll degradation pathway and a loss-of-susceptibility mutation of CsSGR results in durable broad-spectrum DM disease resistance ( Wang et al., 2019 ). CsLRK10L2 acts as a DAMP oligogalacturonan receptor and is involved in the breakdown of pectin, which is involved in the production of plant cell walls. This gene has been identified as a likely candidate for the sub-QTL DM4.1.2 because the transient expression of its loss-of-function mutation CsLRK10L2 from the DM-susceptible parent HS279 in N. benthamiana leaves causes necrosis ( Berg et al., 2020 ). A series of DM- and PM-associated QTLs were also colocalized in typical Northern Chinese type cucumber K8, PI 197088, and PI 197088-derived line CS-PMR1. For example, dm2.1/pm2.1 , dm5.3/pm5.1 , and dm6.2/pm6.1 have been colocated in PI 197088 ( Wang et al., 2018 ). Several candidate genes for both the DM and PM resistance have also been identified, including Csa5M622800.1 , Csa5M622830.1 , and Csa5M623490.1 . The gene Csa5M622830.1 is a GATA transcriptional factor gene and it may prevent the nutrition from reaching DM and PM pathogens ( Zhang et al., 2018 ). In addition, Cucumis sativus Irregular Vasculature Patterning ( CsIVP )-RNA interference ( RNAi ) plants having higher salicylic acid levels show higher resistance to DM than wild type (WT) and it was proposed that CsIVP may interact with CsNIMIN1 , which is a negative regulator in the salicylic acid-signaling pathway, to improve DM resistance in cucumber ( Yan et al., 2020 ). At present, the candidate genes for DM resistance in cucumber identified by forward genetic analysis methods need to be verified by overexpression or knockout experiments in cucumber.

Inheritance, Quantitative Trait Loci Mapping, and Candidate Genes of Cucumber Resistance to Fusarium Wilt

Cucumber FW, caused by Fusarium oxysporum f. sp. cucumerinum Owen (FOC), is a systemic soil-borne fungal disease and the hyphae of this pathogen penetrate cucumber roots, which causes vascular wilt. The disease causes necrotic lesions on the stem bases, foliar wilting, and eventually whole-plant wilt and even death and it occurs throughout cucumber development ( Vakalounakis and Lamprou, 2018 ). The main factor affecting the incidence of FW is the number of FOC in the soil, which is positively correlated.

Inheritance of Fusarium Wilt Resistance in Cucumber

To understand the genetic inheritance of FW resistance, it is important to develop resistance breeding resources and breed-resistant varieties. The inheritance of FW resistance in cucumber has been studied for a long time, but with different conclusions ( Toshimitsu and Noguchi, 1975 ; Netzer et al., 1977 ; Vakalounakis, 1993 , 1995 ; Zhang et al., 2014 ; Vakalounakis and Lamprou, 2018 ; Dong et al., 2019 ; Jaber et al., 2020 ). Dong et al. (2019) found that the inheritance of FW resistance in cucumber is a quantitative trait controlled by multiple genes using an F2 population derived from a cross between the susceptible line Superina and the resistant line Rijiecheng and several studies agreed with this inheritance of FW resistance in cucumber ( Toshimitsu and Noguchi, 1975 ; Zhang et al., 2014 ). Other researchers have reported that the FW resistance in cucumber is a qualitative trait controlled by a single Foc gene ( Netzer et al., 1977 ; Vakalounakis, 1993 , 1995 ; Vakalounakis and Lamprou, 2018 ; Jaber et al., 2020 ). The Foc gene has been incorporated in the Dutch-type cucumber hybrids and has widely controlled FW in cucumber for 40 years ( Vakalounakis and Fragkiadakis, 2003 ). The different patterns of FW inheritance in cucumber are also influenced by pathogen races, including races 1–3 from America, Israel, and Japan, respectively, and race 4 from China ( Zhang et al., 2014 ). Vakalounakis and Lamprou (2018) found that Foc (syn. Fcu -1), which has been identified as a dominant FW resistance gene in the cultivars SMR-18 and WIS2757, controls FW resistance to races 1, 2, and 3, which indicates that FW resistance is not related to different pathogen races. Additionally, the Foc gene was found to be linked to the Ccu gene, which controls resistance to scab in cucumber inbred line 9110Gt, possible due to the FW and scab resistance in cucumber both being controlled by an NBS-type R gene ( Vakalounakis, 1993 ; Mao et al., 2008 ). In the future, the availability of more natural FW-resistant resources aids in revealing the inheritance pattern of FW resistance in cucumber.

Molecular Markers and Quantitative Trait Loci of Fusarium Wilt Resistance in Cucumber

Compared with PM and DM, there are limited reports on molecular linkage markers and QTL mapping related to the inheritance of FW resistance in cucumber. Wang (2005) identified an Amplified Fragment Length Polymorphisms (AFLP) marker E25M70 and an SSR marker CSWCT06A linked to cucumber Foc2.1 at genetic distances of 8.12 and 5.98 cM, respectively. One major QTL, Foc2.1 , has been screened from the F9 RILs derived from the cross between 9110Gt and 9930 and it is located between SSR03084 and SSR17631 on chromosome 2. The marker SSR17631 has been validated with an 87.88% accuracy among 46 cucumber germplasms ( Zhang et al., 2014 ). Moreover, Zhou et al. (2015) mapped the QTL of Foc4 resistance to FW in the region of SSR17631 and SSR00684 on chromosome 2. Another major QTL, fw2.1 , located on chromosome 2, has also been detected and fine-mapped, with a physical distance of 0.60 Mb (InDel1248093–InDel1817308) and it contains 80 candidate genes ( Dong et al., 2019 ). One AFLP marker of FW resistance in cucumber has been identified at a distance of 6.0 cm from the Foc gene and it was converted into SCE12M50 B and SCE12M50 A codominant markers ( Jaber et al., 2020 ). The SCE12M50 B marker is located 7.0 cm away from SSR03084 and is linked to the Ccu locus that controls resistance to scab in cultivar SMR-18 ( Mao et al., 2008 ; Jaber et al., 2020 ). Owing to the complexity of FW symptoms and the defects of related research techniques, the mechanisms and functions of these loci have not been determined and require further exploration.

Candidate Genes or Proteins Involved in the Fusarium Wilt Resistance of Cucumber

Some FW candidate proteins and genes in cucumber have been identified using proteomic and transcriptomic analyses in different FW-resistant varieties. A comparative proteomic analysis of root proteins isolated from infected highly susceptible 995 and highly resistant F9 revealed that 15 overaccumulated proteins are mainly involved in defense and stress responses, oxidation-reduction, metabolism, transport and other processes, and jasmonic acid and redox signaling components. LRR family- and stress-related proteins may be crucial in the defense responses to FW in cucumber ( Zhang et al., 2016 ). Moreover, defense mechanisms against oxidation and detoxification as well as carbohydrate metabolism may also be necessary for FW resistance in cucumber ( Du et al., 2016 ). Xu et al. (2021) identified 210 and 243 differentially regulated proteins in the FW resistance Rijiecheng and high-susceptibility Superina after Foc infection. Additionally, four genes, TMEM115 ( CsaV3_5G025750 ), which encodes a transmembrane protein, TET8 ( CsaV3_2G007840 ), which functions as a tetraspanin, TPS10 ( CsaV3_2G017980 ), which encodes a terpene synthase, and MGT2 ( CsaV3_7G006660 ), which encodes a glycosyltransferase, are remarkably upregulated in both the cultivars after Foc inoculation, but with higher expression levels in Superina. In total, 14 chitinase defense-related genes have higher expression levels in FW susceptible and resistant lines and CsChi23 may play an important role in activating a rapid immune reaction against FW ( Bartholomew et al., 2019 ). Furthermore, other defense-related genes are activated to regulate the defense responses of cucumber to a Foc inoculation, including several genes related to ABA and ethylene ( Zhou and Wu, 2009 ; Dong et al., 2020 ). miR319a-JRL3 , miR6300-BEE1 , miR6300-DAHP1 , and miR6300-PERK2 also regulate cucumber defenses against FW ( Xu et al., 2021 ). Dong et al. (2019) identified five candidate FW-resistance genes in fw2.1 by combining genetic mapping and a transcriptome analysis, Csa2G007990 , which encodes calmodulin, Csa2G009430 , which encodes a transmembrane protein, Csa2G009440 , which encodes a serine-rich protein, and Csa2G008780 and Csa2G009330 , which are novel genes. This is the only report of mapping FW candidate genes in cucumber, but the functions of the candidate genes have not been verified.

Future Prospects for Enhancing Cucumber Disease Resistance

In summary, the inheritance of PM, DM, and FW resistance in cucumber has been widely investigated and cucumber resistance traits are generally considered as quantitative traits controlled by more one gene. Because of the complicated inheritance of resistance to cucumber diseases, the results are not unified. Simultaneously, several molecular markers and QTLs for PM, DM, and FW resistance in cucumber have been identified ( Figure 1 and Supplementary Table 1 ). Many factors affect cucumber resistance to these three diseases, including pathogen species, plant materials, pathogen invasion site, environment, and genetic linkage, resulting in a variety of effective molecular markers and QTLs for cucumber disease. Large numbers of candidate genes and immune proteins associated with DM, PM, and FW have been identified using mapping, GWAS, RNA sequencing (RNA-seq), and proteomic assay technology, but only a few have been functionally verified ( Figure 1 and Supplementary Table 1 ). For example, only the functions of MLO-like genes that are important in PM resistance have been verified in cucumber. The transient silencing of the two NBS-LRR genes ( CsRSF1 and CsRSF2 ) reduces cucumber resistance to PM. Among the DM-resistant candidate genes, CsSGR , CsLRK10L2 , and CsIVP have been functionally verified through mutation, transient expression, or RNAi. Additionally, the resistance of GNK2-1 transgenic cucumber to FW is enhanced compared with WT ( Liu et al., 2010 ). Because of a lack of cucumber FW-resistant germplasms in China, the susceptibility of most cultivars, and the relatively narrow genetic variation among cucumber FW, the breeding of cucumber FW-resistant cultivars has been restricted to a certain extent and research on the molecular mechanisms of FW has not progressed as far as research on PM and DW.

Figure 1. The symptoms, QTL mapping and candidate genes or proteins related to cucumber PM, DM, and FW, respectively.

To better effectively prevent cucumber diseases and explore the genetic and molecular mechanisms of cucumber resistance to PM, DM, and FW, respectively, we propose five aspects of work that need to be performed in the future: (1) collect more disease-resistant cucumber germplasms, especially materials that are resistant to multiple pathogens, including wild germplasm resources, cultivars, and mutants; (2) identify more effective molecular markers and QTLs associated with PM, DM, and FW to be used in selecting germplasms and accelerating resistance breeding; (3) analyze more differently expressed DNA, RNA, miRNAs, lncRNAs, or metabolic related to PM, DM, or FW, respectively, through omics or multiomics and bioinformatics tools would provide considerable experimental information for mechanistic investigations and understand the regulatory network for cucumber diseases, such as transcriptomics, proteomics, metabolomics, epigenomics, and interactomics. Additionally, the data-driven interface through a user-friendly web interface would also be helpful for the mechanism of cucumber diseases, such as plant regulomics ( Ran et al., 2020 ); (4) improve the efficiency and stability of genetic transformation in cucumber. There are now effective methods for gene functional verification that use biotechnology, such as transgenes, RNAi, Transcription Activator-like Effector Nucleases (TALENs), and CRISPR-Cas; (5) develop persistent and safe preventive measures, including chemical, biological, and physical controls. For example, maintaining an optimization of blue light in the growth light before nighttime UV is important for the management of PW in cucumber ( Palma et al., 2021 ). A balance between effective defense and crops yield should be established through these preventive measures. The plant immunity engineering toolbox that integrates genetics, technology, and engineering is required for enhancing disease resistance in crops in the future and the molecular mechanisms of cucumber resistance to PM, DM, and FW need to be further studied.

Author Contributions

YH, MW, and YY drafted the manuscript. CY, SC, and YS modified the manuscript. LM, HW, XZ, and LW designed the project and gave suggestions on the revision of the manuscript. All the authors approved the final version of the manuscript.

This study was supported by “Pioneer” and “Leading Goose” R&D Program of Zhejiang (No. 2022C02051), the Zhejiang Natural Science Foundation of China (Grant No. LY19C150008), Opening Project Fund of Key Laboratory of Biology and Genetic Resources of Rubber Tree, Ministry of Agriculture and Rural Affairs, PR China/State Key Laboratory Breeding Base of Cultivation and Physiology for Tropical Crops/Danzhou Investigation and Experiment Station of Tropical Crops, Ministry of Agriculture and Rural Affairs, PR China (No. RRI-KLOF202102), the Natural Science Foundation of Zhejiang province (Grant Nos. LY21C150002 and LQY19C150001), the National Natural Science Foundation of China (Grant Nos. 31872105, 31972221, 32002048, 31801862, and 32172595), the National College Students Innovation and Entrepreneurship Training Program in 2019 and 2021 (Nos. 202110341043 and 201910341005), and the Student Scientific research training program of Zhejiang Agriculture and Forestry University (Nos. 2021KX0196 and 2021KX019), Ministry of Agriculture, and the National Key Research and Development Program of China (Nos. 2018YFD1000800 and 2019YFD1000300).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

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Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.862486/full#supplementary-material

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Keywords : cucumber, powdery mildew, downy mildew, Fusarium wilt, genetic mechanism

Citation: He Y, Wei M, Yan Y, Yu C, Cheng S, Sun Y, Zhu X, Wei L, Wang H and Miao L (2022) Research Advances in Genetic Mechanisms of Major Cucumber Diseases Resistance. Front. Plant Sci. 13:862486. doi: 10.3389/fpls.2022.862486

Received: 26 January 2022; Accepted: 22 February 2022; Published: 19 May 2022.

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Copyright © 2022 He, Wei, Yan, Yu, Cheng, Sun, Zhu, Wei, Wang and Miao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xiangtao Zhu, [email protected] ; Lingling Wei, [email protected] ; Huasen Wang, [email protected] ; Li Miao, [email protected]

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Cucumber ( Cucumis sativus L . ): Genetic Improvement for Nutraceutical Traits

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• Ashutosh Rai 2 ,
• Vishal Chugh 2 &
• Sudhakar Pandey 3  

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Among the cucurbits, cucumber ( Cucumis sativus L.) is a valuable vegetable crop cultivated for its immature fruits. The cucumber is one of the oldest cultivated vegetable crops. It has been known in history for over 5,000 years and probably originated in India. A lot of diversity exists with respect to shape, size, and color of the fruits which contain 0.4% protein, 2.5% carbohydrates, 1.5 mg iron, and 2 mg of vitamin C per 100 g of fresh weight. Fruits are good for people suffering from constipation, jaundice, and indigestion. The quality of cucumber depends on its total soluble solids, fruit firmness, desired fruit size, and other phytochemicals like antioxidant capacity, phenols, and vitamin C content. As Ayurvedic and conventional remedy, cucumber is used for various skin-related problems, including inflammation under the eyes, and sunburn, and is assumed to have cooling, curative, comforting, emollient, lenitive, anti-itching effect to irritated skin, and extended cosmetic effects. Due to the presence of numerous active constituents distributed throughout the plant parts, including vitamins, minerals, amino acids, phytosterols, phenolic acids, fatty acids, and curcurbitacin, cucumber exhibits a variety of pharmacological properties. Cucumber fruit that have an orange colored endocarp or mesocarp have high β-carotene content. Carotenoids play vital roles in human nutrition as potent precursor for various vitamins for biosynthetic pathways. Cucurbitacins, the bitter triterpenoid compounds found in cucurbits, are toxic to most of the organisms but are able to attract specialized insects. The expression of cucrbitacin depends on a Mendelian gene known as “ Bi. ” The enzyme oxidosqualene cyclase (OSC) catalyzes the biosynthesis of responsible triterpene carbon skeleton in fruits and plants. An oxidosqualene cyclase (OSC) gene in squash ( Cucurbita pepo L.) known as cucurbitadienol synthase (CPQ) is the first enzyme of biosynthetic pathway of cucurbitacin. The mapping of fruit quality-related quantitative trait loci and metabolic pathway studies are enabling researchers to enhance quality traits.

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Rai, A., Chugh, V., Pandey, S. (2023). Cucumber ( Cucumis sativus L . ): Genetic Improvement for Nutraceutical Traits. In: Kole, C. (eds) Compendium of Crop Genome Designing for Nutraceuticals. Springer, Singapore. https://doi.org/10.1007/978-981-19-3627-2_57-1

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DOI : https://doi.org/10.1007/978-981-19-3627-2_57-1

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Research Advances in Genetic Mechanisms of Major Cucumber Diseases Resistance

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• 1 Key Laboratory for Quality and Safety Control of Subtropical Fruits and Vegetables, Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, Ministry of Agriculture and Rural Affairs, College of Horticulture Science, Zhejiang Agriculture and Forestry University, Hangzhou, China.
• 2 Ministry of Agriculture Key Laboratory of Biology and Genetic Resource Utilization of Rubber Tree, State Key Laboratory Breeding Base of Cultivation and Physiology for Tropical Crops, Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Danzhou, China.
• 3 College of Jiyang, Zhejiang Agriculture and Forestry University, Zhuji, China.
• 4 Institute of Ecological Civilization, Zhejiang Agriculture and Forestry University, Hangzhou, China.
• PMID: 35665153
• PMCID: PMC9161162
• DOI: 10.3389/fpls.2022.862486

Cucumber ( Cucumis sativus L.) is an important economic vegetable crop worldwide that is susceptible to various common pathogens, including powdery mildew (PM), downy mildew (DM), and Fusarium wilt (FM). In cucumber breeding programs, identifying disease resistance and related molecular markers is generally a top priority. PM, DM, and FW are the major diseases of cucumber in China that cause severe yield losses and the genetic-based cucumber resistance against these diseases has been developed over the last decade. Still, the molecular mechanisms of cucumber disease resistance remain unclear. In this review, we summarize recent findings on the inheritance, molecular markers, and quantitative trait locus mapping of cucumber PM, DM, and FM resistance. In addition, several candidate genes, such as PM, DM, and FM resistance genes, with or without functional verification are reviewed. The data help to reveal the molecular mechanisms of cucumber disease resistance and provide exciting new opportunities for further resistance breeding.

Keywords: Fusarium wilt; cucumber; downy mildew; genetic mechanism; powdery mildew.

Copyright © 2022 He, Wei, Yan, Yu, Cheng, Sun, Zhu, Wei, Wang and Miao.

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Number of published articles about cucumber research from the horticulture category based on the Web of Science from 1946 to 2021.

Network visualization maps of cited journals about cucumber research from the horticulture category of the Web of Science with 32 nodes and six clusters.

Network visualization maps of authors of cucumber research from the horticulture category of the Web of Science.

The country coauthorship network of cucumber research from the horticulture category of the Web of Science with 38 nodes and seven clusters.

The organization coauthorship network of cucumber research from the horticulture category of the Web of Science.

VOSviewer co-occurrence network visualization mapping of the most frequent keywords (minimum of five occurrences) for cucumber research from the horticulture category of the Web of Science.

VOSviewer co-occurrence overlay visualization mapping of most frequent keywords (minimum of five occurrences) for cucumber research from the horticulture category of the Web of Science.

Comparison of the citations per year of the eight most cited articles and their initial publications until 29 Mar. 2021.

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Bibliometric Analysis of Cucumber ( Cucumis sativus L.) Research Publications from Horticulture Category Based on the Web of Science

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Cucumber ( Cucumis sativus L.) is an economically important vegetable crop that is cultivated worldwide. The current study aimed to identify and analyze the 2030 articles and review article about cucumber research from the horticulture category of the VOS viewer Web of Science. Bibliometric data were analyzed by bibliometric science mapping and visualization tools. Articles mainly written in English (1884; 92.81%) were from 5630 authors, 80 countries or territories, and 1094 organizations; they were published in 46 journals and book series. The top five core journals are Scientia Horticulturae (337; 16.60%), HortScience (265; 13.05%), Journal of the American Society for Horticultural Science (239; 11.77%), European Journal of Plant Pathology (195; 9.61%), and Horticulture Journal (Journal of the Japanese Society for Horticultural Science) (157; 7.73%). These journals each published more than 157 articles. The top five countries and regions were the United States, People’s Republic of China, Japan, South Korea, and India. The top five organizations were the University of Wisconsin, North Carolina State University, U.S. Department of Agriculture–Agricultural Research Service, Michigan State University, and Nanjing Agricultural University. The top five authors are Todd C. Wehner (Wehner, TC), Jack E. Staub (Staub, JE), Yiqun Weng, R.L. Lower, and S. Tachibana; each published more than 24 articles. All keywords used for cucumber research in the horticulture category were separated into eight clusters for different research topics. Visualizations offer exploratory information regarding the current state in a scientific field or discipline as well as indicate possible developments in the future. This review could be a valuable guide for designing future studies.

The cucumber ( Cucumis sativus L.), which belongs to the Cucurbitaceae family, is a commonly consumed vegetable. It is an economically important crop that is widely cultivated throughout the world. Cucumber plants often experience biotic and abiotic stresses during its developmental life cycle, thus leading to reductions in yield and quality ( Wang et al., 2015 ). Cucumber plants suffer from several diseases, such as downy mildew, powdery mildew (PM), anthracnose, and cucumber mosaic virus, which limit crop production. Developing resistance to these diseases is a major subject of cucumber plant breeding. Cucumber is a potential model plant candidate for the Cucurbitaceae family and would provide new scientific knowledge through molecular analysis ( Nanasato and Tabei, 2020 ). PM is a destructive foliar disease with wide distribution and rapid spread that causes substantial yield losses. The long-term planting and variable adaptability of pathogens have led to the gradual decline in plant resistance. Consequently, the cultivation of durable and resistant cultivars is clearly the most economical and environmentally friendly method of controlling PM diseases during cucumber breeding ( Chen et al., 2021 ). High yield can be achieved by improving cultural practices and by developing genetically superior cultivars. High-yielding cultivars, precision farming systems, increased use of chemicals for fertilization and weed and disease control, and proper training of the local farmers allowed for significant changes in agriculture ( Gusmini and Wehner, 2008 ).

Cucumber is a popular vegetable worldwide. The total cucumber production (including gherkins) in 2018 was 75.2 million tons from 1.984 million cultivated hectares ( FAOSTAT, 2020 ). China is one of the main countries producing cucumbers. The total cucumber production (including gherkins) in China in 2018 was 56.24 million tons from 1.044 million hectares, which accounted for 52.7% and 74.8% of the corresponding world totals, respectively. Cucumber ( Cucumis sativus , L.) is widely grown in greenhouses in China as an offseason vegetable because of its high yield and economic benefits ( Liu et al., 2021 ).

Cucumber ( Cucumis sativus L.) is a major vegetable crop with important economic and biological value, and it serves as a model plant for studying several important biological processes ( Che and Zhang, 2019 ). Cucumber was the first among major horticulture crops with a publicly available draft genome. Its relatively small, diploid genome, short life cycle, and self-compatible mating system offer advantages for genetic studies ( Wang et al., 2020 ). The hypocotyl is an important agronomic trait of cucumber, and transplanting seedlings with a short hypocotyl is an effective method of increasing cucumber yield ( Zhang et al., 2021 ). Cucumber is a potential model plant candidate for the Cucurbitaceae family and would provide new scientific knowledge through molecular analysis ( Nanasato and Tabei, 2020 ).

The category of horticulture includes resources concerning the cultivation of flowers, fruits, vegetables, or ornamental plants in gardens, orchards, or nurseries (InCites Journal Citation Reports; Clarivate, 2021 ). Searching the publications by the topic keywords of “Cucumber” OR “ Cucumis sativus L.” and all the years published for article or review article types in the Web of Science, the top three Web of Science categories are Plant Sciences, Horticulture, and Agronomy. In this paper, publications that focused on the horticulture category were collected.

Bibliometric indicators have been frequently used to analyze scientific and technological production in different fields of knowledge. Bibliometric techniques have been adopted in some research, such as essential oil-bearing plants exposed to water stress ( Kulak et al., 2019 ), grafting in horticultural plants ( Belmonte-Ureña et al., 2020 ), scientific research about fungus Phakopsora pachyrhizi Sydow and Sydow affecting soybean [ Glycine max (L.) Merrill] ( Meira et al., 2020 ), highly cited articles in the Science Citation Index Expanded (subject category: horticulture) ( Kolle et al., 2017 ), the research, innovation, and development of Corylus avellana ( Raparelli and Lolletti, 2020 ), the berries on the top ( Yeung et al., 2019 ), bibliometric analysis of French National Institute for Agricultural Research (INRA) publications about fruits and vegetables produced between 2002 and 2006 ( Tatry et al., 2011 ), tree fruit growing in Germany ( Dalla Via and Baric, 2012 ), trends in mango research ( Kolle et al., 2018 ), and wine research and its relationship with wine production ( Jamali et al., 2020 ). Others have researched soil and water conservation in the Loess Tableland-Gully region of China ( Wang et al., 2019 ), advances in water use efficiency in agriculture, and sustainable water use in agriculture ( Velasco-Muñoz et al., 2018a , 2018b ). Rice with fertilizer has been analyzed based on Citespace ( Sun and Yuan, 2019 ), the top articles about world rice research ( Sun and Yuan, 2020a ), library and information science ( Sun and Yuan, 2020b ), water resources ( Sun and Yuan, 2020c ), the agronomy category ( Sun and Yuan, 2021 ), green and sustainable science and technology ( Yuan and Sun, 2019 ), scientific research of maize or corn ( Yuan and Sun, 2020a , 2020b ), and global research of muskmelon ( Cucumis melo L.) ( Yuan et al., 2021 ).

Research is the way to the truth; therefore, innovations are important to finding something new or a new understanding to approach the truth. It is not helpful for researchers to duplicate the same problem without improving their research. It is the responsibility of the authors, reviewers, and editors to stop publishing such problems in journals. The aim of this study was to assess publications of research and review articles about cucumber ( Cucumis sativus L.) from the horticulture category during all years using bibliometric science mapping and visualization tools. We assess the scattering of publications in citation databases, classification of topics, and progress over the years. Country input and author collaboration (coauthorship) are addressed. Special attention is dedicated to research topics and research fronts.

Web of Science.

The Clarivate Analytics Web of Science (WoS) is the world’s leading scientific citation search and analytical information platform and one of the world’s largest and most comprehensive academic information resources covering more than 12,000 core academic journals. The numbers of publications in the WoS Core Collection were derived from the following databases: The Science Citation Index–Expanded (SCIE), 1900 to present; Social Science Citation Index (SSCI), 1900 to present; Conference Proceeding Citation Index-Science (CPCI-S), 2015 to present; Conference Proceedings Citation Index–Social Science & Humanities (CPCI-SSH), 2015 to present; Current Chemical Reactions (CCR-EXPANDED), 1985 to present; and Index Chemicus (IC), 1993 to present.

Data collection and analysis.

This study surveyed articles from the WoS Core Collection (1900–present) (data retrieval last performed on 29 Mar. 2021). We used the following keyword query:

Topic: (“Cucumber” or “ Cucumis sativus L.”)

Then, the results were refined by the document type (article or review) and Web of Science category (horticulture).

There were 2030 articles from WoS Core Collection. Full records and cited references of the included articles were extracted using other reference software file formats and imported into VOSviewer (version 1.6.16; 2020; Leiden University, Leiden, the Netherlands) for further citation analysis. The impact factors (IFs; IF 2020 and IF 5-year) were taken from the Journal Citation Report (JCR) 2020, which was published in 2021 and had the latest data available.

Visualizations (network and overlay) using VOSviewer were conducted for WoS data to determine the co-occurrence and clusters of connected publications, country input, and author collaboration (coauthorship), as well as clusters of inter-related research topics. We used VOSviewer to show the international collaboration between the authors, organizations, and countries, and the research trends through all keywords ( Van Eck and Waltman, 2010 ). Default parameter values of the VOSviewer were usually used in the analysis. Items were represented by a label and a circle. The sizes of the circles reflect the weight of an item. Some items were not displayed to avoid overlapping. The colors in the network visualization represented clusters of similar items as calculated by the program. The distance between the items indicated the strength of the relationships.

Document types and language of publication.

Based on Clarivate Analytics WoS Index, the 2030 articles were from SCIE (2030), and some papers also belong to CPCI-S (18), SSCI (6), and Book Citation Index–Science (1). The document types and languages are displayed in the Table 1 . Among the document types, there were 1999 articles (98.47%) and 31 reviews (1.53%), including 18 proceedings articles (0.89%), 4 early access articles (0.20%), and 1 book chapter (0.05%). The first article titled “The development of downy mildew-resistant cucumbers” ( Barnes et al., 1946 ) was published in Proceedings of the American Society for Horticultural Science (47:357–360).

Document type and language of the publication about cucumber research from the horticulture category of the Web of Science.

Almost all of the articles were published in English (1884; 92.81%); others were published in Japanese (58; 2.86%), Korean (24; 1.18%), German (23; 1.13%), Russian (17; 0.84%), Portuguese (14; 0.69%), Spanish (6; 0.30%), Chinese (2; 0.10%), and Swedish (2; 0.10%). English was the dominating language in the WoS. Scholars tend to publish their articles in English because they want them to be widely accepted. Most of the published documents were in the form of original research articles, and English was the most common language used ( Khan et al., 2020 ).

Publication output.

To determine the trend in cucumber research in the horticulture category of the WoS, a total of 2030 articles and reviews were obtained from the online version of the WoS database from 1946 to 2021 ( Fig. 1 ). The most articles (91) were published in 2018. In the 1980s, 1990s, 2000s, and 2010s, the article publication rates were 93.15%, 82.86%, 63.30%, and 41.53%, respectively. The h -index was initially proposed as a measure of a researcher’s scientific output based on counting the number of publications (N) by that researcher cited N or more times ( Hirsch, 2005 ). For the 2030 articles, the h -index was 66, and the average citation per item was 15.69.

Citation: HortScience 56, 11; 10.21273/HORTSCI16083-21

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Web of Science categories and research areas.

For cucumber research from the horticulture category of the WoS, there are nine WoS subject categories in the science edition (total of 254 categories) and eight research areas. Table 2 shows the WoS categories and research areas regarding the subject of cucumber research from the horticulture category of the WoS. The top five WoS categories include Horticulture (2030; 100%), Agronomy (566; 27.88%), Plant Sciences (544; 26.80%), Genetics Heredity (164; 8.08%) and Food Science Technology (69; 3.40%). The top five research areas include Agriculture (2030; 100%), Plant Sciences (544; 26.80%), Genetics Heredity (164; 8.08%), Food Science Technology (69; 3.40%), and Biotechnology Applied Microbiology (1; 0.05%). The journals or articles may be classified as two or more categories in the WoS, which shows the multidisciplinary character of this research field ( Elango and Ho, 2017 , 2018 ). In the WoS, publications are also mapped to WoS categories, which are more detailed than areas ( Stopar et al., 2021 ).

All Web of Science (WoS) categories and research areas for cucumber research from the horticulture category.

Core journals.

Based on JCR 2020 data (published in 2021), there are 45 journals and one book series regarding cucumber research from the horticulture category of the WoS. The one book series is Advances in Virus Research . The top 20 core journals are displayed in Table 3 . Based on the JCR 2020, there are 37 journals in the horticulture category.

Top 20 journals about cucumber research from the horticulture category of the Web of Science.

The top 5, top 10, top 15, and top 20 journals published 59.66%, 80.20%, 89.80%, and 95.81% of the total articles, respectively. Scientia Horticulturae was the most productive journal, with 337 articles (16.60%), followed by HortScience (265; 13.05%), Journal of the American Society for Horticultural Science ( Proceedings of the American Society for Horticultural Science ; 257; 12.66%), European Journal of Plant Pathology (195; 9.61%), and Horticulture Journal ( Journal of the Japanese Society for Horticultural Science ; 157; 7.73%). These journals each published more than 157 articles. Some journal names were changed; therefore, we combined these titles as the same journal; however, the total number of journals was calculated separately. Of the top 20 journals, there were 5 journals in quartile 1, 6 journals in quartile 2, 5 journals in quartile 3, and 4 journals in quartile 4 ( Table 3 ). White-Gibson et al. (2019) demonstrated the importance of publishing in the English language and in a journal with a high IF. A citation analysis is not a measurement of scientific quality, but it is reflective of importance ( White-Gibson et al., 2019 ).

According to the publication data regarding the citation of 45 journals, 33 journals met the thresholds of 5 publications and 32 journals were connected to each other. The network of citations in the field of cucumber research from the horticulture category of the WoS is shown as six clusters with different colors in Fig. 2 . The sizes of the circles reflect the total number of journal publication records. If journals were in the same cluster, then it was suggested that they published articles with similar content and had close relations with each other.

Author coauthorship analysis.

In general, internationally collaborative articles had the highest visibility and scientific impact, followed by interinstitutional collaborative articles, single-country articles, and single-author articles ( Wambu and Ho, 2016 ). According to the publication data, a total of 5630 authors published 2030 publications. There were 141 authors who met the thresholds of five publications. However, only 57 authors were connected to each other. The network of authorship in the field of network visualization maps of authors of cucumber research from the horticulture category of the WoS is shown in Fig. 3 . The sizes of the circles reflect the total number of records. If authors were in the same cluster, then it was suggested that they studied a similar field and had close cooperation with each other.

Details regarding the author information published in articles about cucumber from 1946 to 2021, along with citations, average citations, organization, and countries are provided in Table 4 . Table 4 shows the top 19 authors who published more than 12 articles, the total citations, average citations, organizations, and countries. Although we combined the same authors with different spellings, the total number of authors was calculated separately. The top five authors were Todd C. Wehner (Wehner, TC), Jack E. Staub (Staub, JE), Yiqun Weng, R.L. Lower, and S. Tachibana; each published more than 24 articles. The top five authors with a high number of citations per article were Yiqun Weng, Michael J. Havey (Havey, MJ), Jack E. Staub (Staub, JE), Yuhong Li, and Toshiki Asao (Asao, T). Their average citations per article totaled more than 20.75. Among the top 19 authors, there were nine from the United States, six from Japan, three from China, and one from India.

Top 19 most prolific authors who published articles in the field of cucumber research from the horticulture category of the Web of Science.

Country/region coauthorship analysis.

There were 80 countries or regions contributing to the 2030 articles in this study. Table 5 lists the top 20 countries or regions where more than 17 publications originated. The top five countries that published articles were the United States, People’s Republic of China, Japan, South Korea, and India. Italy, Israel, Netherlands, France, and Canada provided the highest number of citations per article.

Top 20 countries/regions publishing articles in the field of cucumber research from the horticulture category of the Web of Science.

We developed the international country coauthorship network map using VOSviewer software. We set the threshold as 5. There were 39 countries/regions meeting the requirements and 38 countries/regions connected to each other ( Fig. 4 ). The VOSviewer software divided these 38 countries into seven clusters with different colors. The sizes of circles reflect the total number of records, and the distance between the countries indicates the strength of relationships. The different color groups and different clusters were formed based on countries.

The United States, People’s Republic of China, Japan, South Korea, and India had the five biggest circles ( Fig. 4 ). The first cluster consisted of 10 countries and regions (red color): India, Brazil, France, England, Belgium, Thailand, Taiwan, Hungary, Czech Republic, and Bangladesh. The second cluster consisted of seven countries or regions (green): Japan, South Korea, Iran, Norway, Finland, Sweden, and Denmark. The third cluster consisted of seven countries (blue): Poland, Israel, the Netherlands, Italy, Serbia, Lebanon, and Slovenia. The fourth cluster consisted of five countries and regions (yellow): Germany, Greece, Egypt, Nigeria, and Saudi Arabia. The fifth cluster consisted of four countries (violet): People’s Republic of China, Canada, Turkey, and Pakistan. The sixth cluster consisted of three countries (light blue): Spain, Mexico, and Australia. The seventh cluster consisted of two countries (orange): United States and South Africa. Taiwan, which is a region of China, showed stronger research and collaboration in the field of cucumber research. It seems that greater cooperation brings more advanced achievements in scientific research. Therefore, geographical location is an important factor that determines international cooperation. The increasing international exchanges have promoted academic communications ( Tang et al., 2018 ).

Organization coauthorship analysis.

According to the publication data, a total of 1094 organizations published 2030 publications. The organization coauthorship analysis reflected the degree of communication between institutions as well as the influential institutions in this field ( Reyes-Gonzalez et al., 2016 ). Table 6 lists the top 22 organizations and institutions that had more than 16 publications. These organizations are mainly focused in China (eight organizations), the United States (seven organizations), Japan (two organizations), Israel (one organization), Canada (one organization), India (one organization), South Korea (one organization), and Greece (one organization). The University of Wisconsin, North Carolina State University, U.S. Department of Agriculture–Agricultural Research Service (USDA–ARS), Michigan State University, and Nanjing Agricultural University are the top five organizations that have had articles published. The organizations of Agriculture Research Organization, Shandong Agricultural University, Agricultural University of Athens, University of Wisconsin, and Cornell University had the highest average number of citations per article.

Top 22 organizations publishing articles in the field of cucumber research from the horticulture category of the Web of Science.

Of the 1094 organizations, there were 119 organizations that met the minimum threshold of five; however, 88 organizations were connected to each other ( Fig. 5 ). The VOSviewer software divided these 88 institutions into 11 clusters using different colors. Geographical localization is an important factor for partnership and joint ventures.

All keywords co-occurrence analysis.

Of the 6672 keywords, there were only 621 keywords that met the threshold more than five times. These were separated into eight main cluster viewpoints of cucumber research from the horticulture category of the WoS ( Fig. 6 ). The top 20 co-occurrence keywords (occurring more than 55 times) were cucumber, cucumis sativus , growth, plants, tomato, yield, resistance, quality, expression, identification, photosynthesis, temperature, tolerance, leaves, disease resistance, stress, inheritance, fruit, Arabidopsis, and genome.

The same data were arranged by the period of cucumber research from the horticulture category of the WoS ( Fig. 7 ). According to the manual for VOSviewer version 1.6.16 ( Van Eck and Waltman, 2020 ), blue colors indicate older research topics, and yellow and green colors indicate more recent topics of interest. When blue is used for topics, it does not mean that the topic is no longer researched; instead, blue usually indicates that, on average, the topic was intensely investigated previously, but now more attention has shifted toward other topics.

Visualizations of large datasets (big data) offer exploratory information regarding the current state of a scientific field or discipline as well as indicate possible developments in the future. The top 20 keywords are listed and ranked in each cluster in Fig. 6 .

The first cluster (red) focused on inheritance analyses and gene expression. It included keywords such as expression, identification, inheritance, arabidopsis, genome, gene, cucurbitaceae, markers, genes, quantitative trait loci, sativus, sativus L., map, QTL analysis, loci, DNA, evolution, linkage, diversity, and genetic diversity.

The second cluster (green) focused on the metabolism and tolerance to stress of the plant. The keywords were ranked as tolerance, leaves, stress, fruit, cucumis sativus L., accumulation, light, seedlings, oxidative stress, storage, arabidopsis-thaliana, CI, gene-expression, injury, metabolism, acid, ethylene, hydrogen-peroxide, chlorophyll fluorescence, and abscisic-acid.

The third cluster (blue) focused on plant growth and fruit quality. It included keywords such as growth, tomato, yield, quality, photosynthesis, temperature, responses, salinity, soil, greenhouse, management, nitrogen, crops, grafting, lettuce, leaf, salt tolerance, soilless culture, fruit-quality, and efficiency.

The fourth cluster (yellow) focused on cucumber disease resistance and biocontrol management. The keywords included cucumber, disease resistance, induction, powdery mildew, biological-control, disease, salicylic-acid, vegetable breeding, plant growth, infection, biological control, biocontrol, damping-off, induced resistance, cucurbits, root, systemic acquired-resistance, systemic resistance, downy mildew, and induced systemic resistance.

The fifth cluster (violet) focused on cucumber grafting cultivars. It included keywords such as cucumis sativus , cucumis-sativus , watermelon, sativus L, cucumis-sativus L, cultivars, rootstock, water, vegetables, citrullus lanatus, maize, transformation, culture, pickling cucumber, plant-regeneration, fruits, toxicity, cytokinins, higher plants, pollination, and pumpkin.

The sixth cluster (light blue) focused on cucumber mosaic virus and disease resistance. It included keywords such as resistance, melon, protein, tobacco, cucumber mosaic virus, pepper, cucumis melo, plant, cmv, cucumber mosaic-virus, muskmelon, sequence, squash, field, lycopersicon-esculentum, potyvirus, virus resistance, coat protein, cucumber-mosaic-virus, capsicum annuum, and diseases.

The seventh cluster (orange) focused on cucumber nutrient solution and cucumber allelopathy. The keywords included hydroponics, rice, cucumber cucumis-sativus, nutrient solution, germination, vegetable crops, proteins, autotoxicity, systems, root exudates, allelopathy, cucumber ( cucumis sativus L.), corn substances, activated-charcoal, bell pepper, hydroponic culture, activated charcoal, allelochemicals, and bioassay.

The eighth cluster (brown) focused on plants nutrition. The keywords included plants, nutrition, nitrate, susceptibility, assimilation, nitrate reductase, amino-acids, ammonium, greenhouses, nutrient, podosphaera xanthii, zinc, availability, biomass, elevated CO 2 , pH, and podosphaera-xanthii.

Top articles based on Essential Science Indicators.

The top articles are the sum of hot articles and highly cited articles based on the Clarivate Analytics Essential Science Indicators (ESI). A highly cited article is an article that belongs to the top 1% of articles in a research field published during a specified year. A hot article is an article published within the past 2 years that was cited a number of times during the most recent 2-month period, thus placing it in the top 0.1% of articles in the same field. The Essential Science Indicators database includes a period of more than 11 years (1 Jan. 2010–31 Dec. 2020).

Based on the ESI database, these top articles are six highly cited articles ( Table 7 ) that have been published in Scientia Horticulturae ( Ahammed et al., 2020 ; Schwarz et al., 2010 ; Z.W. Zhang et al., 2020 ; T.G. Zhang et al., 2020 ) and Theoretical and Applied Genetics ( Lu et al., 2014 ; Pan et al., 2020 ) ( Table 7 ).

Top 6 highly cited articles according to the ESI.

The most frequently cited articles.

A citation analysis has been performed to provide a supplementary index to determine the impact of scientific studies and identify studies, researchers, and the most renowned institutions focusing on the theme. Although several articles have been published, a relatively small number of individuals account for a large proportion of the citations within the period. The total number of citations of the eight most frequently cited articles is more than 150 ( Fig. 8 ). These eight articles have been published in the following four journals: HortScience ( Lee, 1994 ), Scientia Horticulturae ( Carmen Martinez-Ballesta et al., 2010 ; Colla et al., 2010 ; Feng et al., 2010 ; Schwarz et al., 2010 ), Postharvest Biology and Technology ( Qin and Lu, 2008 ), and European Journal of Plant Pathology ( De Meyer et al., 1998 ; Zehnder et al., 2001 ). The citations of these articles increased every year until 2020, but their rates of increase are different ( Fig. 8 ). The most cited article (blue line) was published in HortScience ( Lee, 1994 ). An article published in Scientia Horticulturae ( Schwarz et al., 2010 ) had highest number of citations per year (22.08; red line); it was also the most highly cited article based on ESI ( Table 7 ). The number of times that an article is cited is considered a good quantitative measure of the impact of an article.

This study analyzed 2030 articles and review articles of cucumber research from the horticulture category of the WoS. These articles were mainly written in English (92.81%) and were from 5630 authors, 80 countries/territories, and 1094 organizations, and they were published in 46 journals and book series. The top five core journals were ranked as Scientia Horticulturae , HortScience , Journal of the American Society for Horticultural Science , European Journal of Plant Pathology , and Horticulture Journal (Journal of the Japanese Society for Horticultural Science) . The top five countries and regions were the United States, People’s Republic of China, Japan, South Korea, and India. The top five organizations were the University of Wisconsin, North Carolina State University, USDA–ARS, Michigan State University, and Nanjing Agricultural University. The top five authors were Todd C. Wehner (Wehner, TC), Jack E. Staub (Staub, JE), Yiqun Weng, R.L. Lower, and S. Tachibana. All keywords of the cucumber research from the horticulture category were separated into eight clusters of different research topics. The analyses and visualizations reported herein offer exploratory information regarding the current status of a scientific field or discipline as well as indicate possible developments in the future.

Ahammed, G.J. , Wu, M.J. , Wang, Y.Q. , Yan, Y.R. , Mao, Q. , Ren, J.J. , Ma, R.H. , Liu, A.R. & Chen, S.C. 2020 Melatonin alleviates iron stress by improving iron homeostasis, antioxidant defense and secondary metabolism in cucumber Scientia Hort. 265 109205 doi: 10.1016/j.scienta.2020.109205

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Contributor Notes

This work was supported by Hubei Provincial Natural Science Foundation of China (2019CFA017).

B.-Z.Y. and Z.-L.B. are the corresponding authors. E-mail: [email protected] or [email protected] .

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Reddish sea creature — with over 70 feet — found by a submarine. It’s a new species

S cientists onboard a submarine piloted themselves through the depths of the South China Sea. As they searched the murky blue waters, they spotted a reddish sea creature covered in dozens of appendages.

It turned out to be a new species.

Researchers explored the South China Sea during a series of five dives on a “manned submersible vehicle” between 2018 and 2023, according to a study published March 20 in the peer-reviewed journal ZooKeys.

During one of these deep-sea dives, the researchers found an unfamiliar-looking reddish sea cucumber, the study said. They carefully collected the unusual animal and preserved it.

Back on the surface, the researchers looked more closely at the sea cucumber and realized they’d discovered a new species: Oneirophanta brunneannulata, or the brown-ringed sea cucumber.

The brown-ringed sea cucumber can reach about 8 inches in length and about 2 inches in width, the study said. It has an “elongated,” tube-shaped body with 20 tentacles and over 70 tube feet.

Sea cucumbers use these feet-like projections to suction onto surfaces and move around, according to the University of Hawai‘i.

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A photo shows the reddish coloring of the brown-ringed sea cucumber. From above, it looks fleshy and squishy with worm-like extensions branching from its back. From underneath, it has several rows of shorter and stubbier feet with dark brown tips.

Researchers said they named the new species after the Latin word “brunneannulata,” meaning “brown rings,” because of the “distinctive brown rings around the (sea cucumber’s) tube feet.”

The brown-ringed sea cucumber was found on a sea slope at a depth of about 4,400 feet, the study said.

So far, the brown-ringed sea cucumber is known from one specimen found in the South China Sea, the study said. The South China Sea is a contested body of water in southeastern Asia that borders Brunei, China, Indonesia, Malaysia, the Philippines, Taiwan and Vietnam.

The new species was identified by its tube feet, tentacles and other subtle physical features, the study said. DNA analysis found the new species had at least 8% genetic divergence from other Oneirophanta sea cucumbers.

The research team included Yunlu Xiao and Haibin Zhang. The team also discovered two more new species of sea cucumber: a yellow-white one and an orange one.

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