Mining the cucumber core collection: phenotypic and genetic characterization of morphological diversity for fruit quality characteristics

Abstract

Commercial value of cucumber is primarily driven by fruit quality. However, breeding goals frequently focus on production constraints caused by biotic and abiotic stresses. As sources of resistances are often present in unadapted germplasm, we sought to provide morphological and genetic information characterizing the diversity of fruit quality traits present in the CucCAP cucumber core collection. These 388 accessions representing >96% of the genetic diversity for cucumber present in the US National Plant Germplasm System harbor important sources of resistances and extensive morphological diversity. Data were collected for skin color, length/diameter ratio (L/D), tapering, curvature, and spine density for young fruits [5-7 days postpollination (dpp)], and length, diameter, L/D, skin color, netting, seed cavity size, flesh thickness, hollowness, and flesh color for mature fruits (30-40 dpp). Significant associations of single nucleotide polymorphisms (SNPs) with each trait were identified from genome-wide association studies. In several cases, quantitative trait loci (QTL) for highly correlated traits were closely clustered. Principal component analysis, driven primarily by the highly correlated traits of fruit length, young and mature L/D ratios, and curvature showed a clear divergence of East Asian accessions. Significant SNPs contributing to the longest fruits, including development-stage specific QTL, were distributed across multiple chromosomes, indicating broad genomic effects of selection. Many of the SNPs identified for the various morphological traits were in close vicinity to previously identified fruit trait QTL and candidate genes, while several novel genes potentially important for these traits were also identified.

Acknowledgements

We thank Sue Hammar and Savannah Beyer for laboratory, greenhouse, and field assistance; Bill Chase and Mitch Fox for preparation of field plots; and Matthew Feighner, Chris Culp, Carissa Kelley, Katie Dailey, Mckayla Dzyngel, Kaylee Graham, and Megan Nowak for assistance with plant care and collection of phenotypic data. This research was funded by the National Institute of Food and Agriculture, U.S. Department of Agriculture (Award Number 2020-51181-32139) and USDA NIFA Hatch project number MICL02780.

Author Contributions

Y.C.L. and R.G. conceived the project and wrote the paper. All authors edited the manuscript. YCL collected the experimental data and performed the GWAS and other analyses. Z.F. performed the data processing and SNP calling for the re-sequenced cucumber core population and deposition of the phenotypic data into the Cucurbit genomics database. Y.W. developed inbred families for the core population.

Data availability

Fruit images of the cucumber core collection have been deposited in the Cucurbit Genomics Database (http://www.cucurbitgenomics.org/cgi-bin/core?pid=P04).Phenotypic data for individual fruits from each year are available at the Dryad Data Repository (https://doi.org/10.5061/dryad.tb2rbp0b9). The genotype data are available at (http://www.cucurbitgenomics.org/v2/genotype; Projects ‘P04’ and ‘P05’). The direct link to the vcf files on our ftp site is (http://www.cucurbitgenomics.org/v2/ftp/reseq/cucumber/core/).

Conflict of interest statement

The authors declare that they have no conflict of interest.

Supplementary data

Supplementary data is available at Horticulture Research online.

References

[1] Naegele RP, Wehner TC. Genetic resources of cucumber. In: RGrumet, NKatzir, J edsGarcia-Mas. and Genomics of CucurbitaceaeGenetics. Cham Switzerland: Springer International, 2017, 1-26.

[2] Lv J, Qi J, Shi Q. et al. Genetic diversity and population structure of cucumber (Cucumis sativus L.). PLoS One. 2012;7:e46919.

[3] Paris HS, Daunay M-C, Janick J. Occidental diffusion of cucumber (Cucumis sativus) 500-1300 CE: two routes to Europe. Ann Bot. 2012;109: 117-26

[4] Weng Y. Cucumis sativus chromosome evolution, domestication, and genetic diversity:Implications for cucumber breeding. In: Goldman I (ed.), Plant Breeding Reviews. Vol. 79-111. Hoboken NJ, USA: John Wiley & Sons, 2021.

[5] Grumet R, Lin Y-C, Rett-Cadman S. et al. Morphological and genetic diversity of cucumber (Cucumis sativus L.) fruit develop-ment. Plants (Basel). 2022;12:23

[6] Zhang J, Feng S, Yuan J. et al. The formation of fruit quality in Cucumis sativus L. Front Plant Sci. 2021;12:729448.

[7] Grumet R, Fei Z, Levi A. et al. The CucCAP project: leveraging applied genomics to improve disease resistance in cucurbit crops. Acta Hortic. 2020;91-104:91-104

[8] Wang X, Bao K, Reddy UK. et al. The USDA cucumber (Cucumis sativus L.) collection: genetic diversity, population structure, genome-wide association studies, and core collection develop-ment. Horticulture Research. 2018;5: 64-13

[9] Yu J, Wu S, Sun H. et al. CuGenDBv2: an updated database for cucurbit genomics. Nucleic Acids Res. 2023;51: D1457-64

[10] Tibbs Cortes L, Zhang Z, Yu J. Status and prospects of genome-wide association studies in plants. Plant Genome. 2021;14: 1-17

[11] Pan Y, Wang Y, McGregor C. et al. Genetic architecture of fruit size and shape variation in cucurbits: a comparative perspective. Theor Appl Genet. 2020;133: 1-21

[12] Weng Y, Colle M, Wang Y. et al. QTL mapping in multiple pop-ulations and development stages reveals dynamic quantitative trait loci for fruit size in cucumbers of different market classes. Theor Appl Genet. 2015;128: 1747-63

[13] Yuan XJ, Pan JS, Cai R. et al. Genetic mapping and QTL anal-ysis of fruit and flower related traits in cucumber (Cucumis sativus L.) using recombinant inbred lines. Euphytica. 2008;164: 473-91

[14] Pan Y, Qu S, Bo K. et al. QTL mapping of domestication and diversifying selection related traits in round-fruited semi-wild Xishuangbanna cucumber (Cucumis sativus L. var. xishuangban-nanesis). Theor Appl Genet. 2017;130: 1531-48

[15] Shimomura K, Fukino N, Sugiyama M. et al. Quantitative trait locus analysis of cucumber fruit morphological traits based on image analysis. Euphytica. 2017;213:138

[16] Li S, Pan Y, Wen C. et al. Integrated analysis in bi-parental and natural populations reveals CsCLAVATA 3 (CsCLV3) under-lying carpel number variations in cucumber. Theor Appl Genet. 2016;129: 1007-22

[17] Han L, Huang Y, Li C. et al. Heterotrimeric Gα-subunit regulates flower and fruit development in CLAVATA signaling pathway in cucumber. Horticulture Research. 2024;11:uhae110.

[18] Wu S, Zhang B, Keyhaninejad N. et al. A common genetic mecha-nism underlies morphological diversity in fruits and other plant organs. Nat Commun. 2018;9:4734

[19] Xie Y, Liu X, Sun C. et al. CsTRM5 regulates fruit shape via medi-ating cell division direction and cell expansion in cucumber. Horticulture Research. 2023;10:uhad007

[20] Xing Y, Cao Y, Ma Y. et al. QTL mapping and transcrip-tomic analysis of fruit length in cucumber. Frontiers. Plant Sci. 2023;14:1208675.

[21] Chen C, Yin S, Liu X. et al. The WD-repeat protein CsTTG1 regulates fruit wart formation through interaction with the homeodomain-leucine zipper I protein Mict1. Plant Physiol. 2016;171: 1156-68

[22] Livanos P, Müller S. Division plane establishment and cytokine-sis. Annu Rev Plant Biol. 2019;70: 239-67

[23] Miears HL, Gruber DR, Horvath NM. et al. Plant villin headpiece domain demonstrates a novel surface charge pattern and high affinity for F-actin. Biochemistry. 2018;57: 1690-701

[24] Hotta T, Kong Z, Ho C-MK. et al. Characterization of the Ara-bidopsis augmin complex uncovers its critical function in the assembly of the acentrosomal spindle and phragmoplast micro-tubule arrays[W]. Plant Cell. 2012;24: 1494-509

[25] Lin J, Nazarenus TJ, Frey JL. et al. A plant DJ-1 homolog is essential for Arabidopsis thaliana chloroplast development. PLoS One. 2011;6:e23731.

[26] Liu X, Yang X, Xie Q. et al. NS encodes an auxin transporter that regulates the ‘numerous spines’ trait in cucumber (Cucumis sativus) fruit. Plant J. 2022;110: 325-36

[27] Brodie A, Azaria JR, Ofran Y. How far from the SNP may the causative genes be? Nucleic Acids Res. 2016;44: 6046-54

[28] Uffelmann E, Huang QQ, Munung NS. et al. Genome-wide asso-ciation studies. Nature Reviews Methods Primers. 2021;1: 1-21

[29] Xu X, Pan J, He M. et al. Transcriptome profiling reveals key genes related to astringency during cucumber fruit development. 3 Biotech. 2019;9:390

[30] Zhang HJ, Wang YH, Tan J. et al. Functional copy number vari-ation of CsSHINE1 is associated with fruit skin netting inten-sity in cucumber, Cucumis sativus. Theor Appl Genet. 2022;135: 2101-19

[31] Oren E, Tzuri G, Dafna A. et al. High-density NGS-based map construction and genetic dissection of fruit shape and rind netting in Cucumis melo. Theor Appl Genet. 2020;133: 1927-45

[32] Ranocha P, Dima O, Nagy R. et al. Arabidopsis WAT 1 is a vacuolar auxin transport facilitator required for auxin homoeostasis. Nat Commun. 2013;4:2625

[33] Qi J, Liu X, Shen D. et al. A genomic variation map provides insights into the genetic basis of cucumber domestication and diversity. Nat Genet. 2013;45: 1510-5

[34] Wang Y, Bo K, Gu X. et al. Molecularly tagged genes and quan-titative trait loci in cucumber with recommendations for QTL nomenclature. Horticulture Research. 2020;7:3

[35] Colle M, Weng Y, Kang Y. et al. Variation in cucumber (Cucumis sativus L.) fruit size and shape results from multiple compo-nents acting pre-anthesis and post-pollination. Planta. 2017;246: 641-58

[36] Lin Y-C, Mansfeld BN, Tang X. et al. Identification of QTL asso-ciated with resistance to Phytophthora fruit rot in cucumber (Cucumis sativus L.). Front Plant Sci. 2023;14:1281755

[37] Rett-Cadman S, Weng Y, Fei Z. et al. Genome-wide associa-tion study of cuticle and lipid droplet properties of cucumber (Cucumis sativus L.) fruit. Molecular sciences. 2024;25:9306

[38] Li S, Wang C, Zhou X. et al. The curvature of cucumber fruits is associated with spatial variation in auxin accumulation and expression of a YUCCA biosynthesis gene. Horticulture Research. 2020;7: 135-12

[39] Wang Z, Zhou Z, Wang L. et al. The CsHEC1-CsOVATE mod-ule contributes to fruit neck length variation via modulat-ing auxin biosynthesis in cucumber. Proc Natl Acad Sci. 2022; 119:e2209717119.

[40] Zhao J, Jiang L, Che G. et al. A functional allele of CsFUL1 regulates fruit length through repressing CsSUP and inhibiting auxin transport in cucumber. Plant Cell. 2019;31: 1289-307

[41] Zhang Z, Wang B, Wang S. et al. Genome-wide target mapping shows histone deacetylase complex1 regulates cell proliferation in cucumber fruit. Plant Physiol. 2020;182: 167-84

[42] Hu Y, Li Y, Zhu B. et al. Genome-wide identification of the expansin gene family in netted melon and their transcriptional responses to fruit peel cracking. Front Plant Sci. 2024;15:1332240

[43] Song M, Zhang M, Cheng F. et al. An irregularly striped rind mutant reveals new insight into the function of PG1β in cucum-ber (Cucumis sativus L.). Theor Appl Genet. 2020;133: 371-82

[44] Chen C, Zhou G, Chen J. et al. Integrated metabolome and tran-scriptome analysis unveils novel pathway involved in the forma-tion of yellow peel in cucumber. Molecular sciences. 2021;22:1494

[45] Bo K, Wei S, Wang W. et al. QTL mapping and genome-wide association study reveal two novel loci associated with green flesh color in cucumber. BMC Plant Biol. 2019b;19:243

[46] Bo K, Miao H, Wang M. et al. Novel loci fsd6.1 and Csgl 3 regulate ultra-high fruit spine density in cucumber. Theor Appl Genet. 2019a;132: 27-40

[47] Huang H, Yang Q, Zhang L. et al. Genome-wide association anal-ysis reveals a novel QTL CsPC1 for pericarp color in cucumber. BMC Genomics. 2022b;23:383

[48] Huang H, Du Y, Long Z. et al. Fine mapping of a novel QTL CsFSG1 for fruit skin gloss in cucumber (Cucumis sativus L.). Mol Breed. 2022a;42:25

[49] Alqudah AM, Sallam A, Stephen Baenziger P. et al. GWAS: fast-forwarding gene identification and characterization in temper-ate cereals: lessons from barley—a review. J Adv Res. 2020;22: 119-35

[50] Schindelin J, Arganda-Carreras I, Frise E. et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9: 676-82

[51] Clement J, Novas N, Gazquez J-A. et al. An active contour com-puter algorithm for the classification of cucumbers. Comput Electron Agric. 2013;92: 75-81

[52] Danecek P, Bonfield JK, Liddle J. et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10:giab008

[53] Van der Auwera GA, Carneiro MO, Hartl C. et al. From FastQ data to high confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinformatics. 2013;43: 11.10.1-33

[54] Chang CC, Chow CC, Tellier LC. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. 2015;4:7

[55] Lê S, Josse J, Husson F. FactoMineR: an R package for multivariate analysis. J Stat Softw. 2008;25: 1-18

[56] Bates D, Mächler M, Bolker B. et al. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67: 1-48

[57] Wang J, Zhang Z. GAPIT version 3: boosting power and accuracy for genomic association and prediction. Genomics, Proteomics & Bioinformatics. 2021;19: 629-40