Telomere-to-telomere, gap-free genome of mung bean (Vigna radiata) provides insights into domestication under structural variation

Abstract

Mung bean (Vigna radiata), an essential annual legume, holds substantial value in global agriculture due to its short growth cycle, low input requirements, and nutritional benefits. Despite extensive domestication, the genetic mechanisms underlying its morphological and physiological evolution remain incompletely understood. In this study, we present a gap-free, telomere-to-telomere genome assembly of the mung bean cultivar 'Weilv-9′, achieved through the integration of PacBio HiFi, Oxford Nanopore, and high-throughput chromosome conformation capture (Hi-C) sequencing technologies. The 500-Mb assembly, encompassing 11 chromosomes and containing 28 740 protein-coding genes, reveals that 49.17% of the genome comprises repetitive sequences. Within the genome, we found the recent amplification of transposable elements significantly impacts the expression of nearby genes. Furthermore, integrating structural variation and single-nucleotide polymorphism (SNP) data from resequencing, we identified that the fatty acid synthesis, suberin biosynthetic, and phenylpropanoid metabolic processes have undergone strong selection during domestication. These findings provide valuable insights into the genetic mechanisms driving domestication and offer a foundation for future genetic enhancement and breeding programs in mung beans and related species.

Aknowledgement

We extend our gratitude to the Modern Agriculture Coarse Grain Industry Technology System of Shandong Province for their valuable suport. This research was supported by the Key R&D Program of Shandong Province (2022LZGC022, 2021LZGC025, and 2023LZGC001), the National Natural Science Foundation of China (32201736), the Natural Science Foundation of Shandong Province for Young Scholars (ZR2023QC153), and the Agricultural Science and Technology Innovation Project of SAAS (CXGC2023F13, CXGC2024F13, and CXGC2023C02).

Author contributions

K.H.J. and N.N.L. conceived and designed the study; K.H.J., G.L., L.W., M.L., Z.W.W., R.Z.L., L.L.L., K.X., Y.Y.Y., R.M.T., X.C., Y.J.S., X.Y.Z., and F.J.S. collected materials and analyzed the data; K.H.J., L.W., and L.L.L. prepared figures and tables; K.H.J., L.W., and G.L. wrote and revised the manuscript; all authors approved the final manuscript.

Data availability

The whole genome sequence data reported in this paper have been deposited in the Genome Sequence Archive, under accession number PRJCA021300. The genome assembly and annotation data reported in this paper have been deposited in the Genome Warehouse in National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, under accession number GWHEQVC00000000 that is publicly accessible at https://ngdc.cncb.ac.cn/gwh.

Conflict of interest statement

The authors declare that they have no competing interests.

Supplementary Data

Supplementary data is available at Horticulture Research online.

References

[1] Nair R, Schreinemachers P. Global status and economic importance of mungbean. Mungbean Genome. 2020;1: 1-8

[2] Ha J, Lee S-H. Mung bean (Vigna radiata (L.) R. Wilczek) breeding. In: Al-Khayri J, Jain S, Johnson D, eds. Advances in Plant Breeding Strategies: Legumes. Springer: Cham, 2019,371-407

[3] Hou D, Yousaf L, Xue Y. et al. Mung bean ( Vigna radiata L.): bioac-tive polyphenols, polysaccharides, peptides, and health benefits. Nutrients. 2019;11:1238

[4] Lin YP, Chen HW, Yeh PM. et al. Demographic history and distinct selection signatures of two domestication genes in mungbean. Plant Physiol. 2023;193: 1197-212

[5] Ignacimuthu S, Babu C. Vigna radiata var. sublobata (Fabaceae): economically useful wild relative of urd and mung beans. Econ Bot. 1987;41: 418-22

[6] Pandiyan M, Senthil N, Ramamoorthi N. et al. Interspecific hybridization of Vigna radiata x13wild Vigna species for devel-oping MYMV donar. Electron J Plant Breed. 2010;1: 600-10

[7] Thuan ND. Expression and Inheritance of Traits in Wild Mungbean (Vigna Radiata Ssp. Sublobata) x Cultivated Mungbean (V. Radiata Ssp. Radiata) Hybrids. James Cook University; 2011:

[8] Xu X, Bai G. Whole-genome resequencing: changing the paradigms of SNP detection, molecular mapping and gene dis-covery. Mol Breed. 2015;35:33

[9] Nan J, Ling Y, An J. et al. Genome resequencing reveals inde-pendent domestication and breeding improvement of naked oat. Gigascience. 2022;12:giad061

[10] Wu D, Xie L, Sun Y. et al. A syntelog-based pan-genome provides insights into rice domestication and de-domestication. Genome Biol. 2023;24:179

[11] Cumer T, Boyer F, Pompanon F. Genome-wide detection of struc-tural variations reveals new regions associated with domestica-tion in small ruminants. Genome Biol Evol. 2021;13:evab165

[12] Hitte C. Toward the identification and role of structural varia-tions during dog domestication. Natl Sci Rev. 2019;6: 123-4

[13] Chaisson MJP, Sanders AD, Zhao X. et al. Multi-platform dis-covery of haplotype-resolved structural variation in human genomes. Nat Commun. 2019;10:1784

[14] Gaut BS, Seymour DK, Liu Q. et al. Demography and its effects on genomic variation in crop domestication. Nat Plants. 2018;4: 512-20

[15] Alonge M, Wang X, Benoit M. et al. Major impacts of widespread structural variation on gene expression and crop improvement in tomato. Cell. 2020;182: 145-161.e23

[16] Zhang T, Peng W, Xiao H. et al. Population genomics highlights structural variations in local adaptation to saline coastal envi-ronments in woolly grape. J Integr Plant Biol. 2024;66: 1408-26

[17] Jia KH, Zhang X, Li LL. et al. Telomere-to-telomere genome assemblies of cultivated and wild soybean provide insights into evolution and domestication under structural variation. Plant Commun. 2024;5:100919

[18] Ha J, Satyawan D, Jeong H. et al. A near-complete genome sequence of mungbean (Vigna radiata L.) provides key insights into the modern breeding program. Plant Genome. 2021;14:e20121

[19] Kang YJ, Kim SK, Kim MY. et al. Genome sequence of mungbean and insights into evolution within Vigna species. Nat Commun. 2014;5:5443

[20] Liu C, Wang Y, Peng J. et al. High-quality genome assembly and pan-genome studies facilitate genetic discovery in mung bean and its improvement. Plant Commun. 2022;3:100352

[21] Zeng Q, Wei M, Li S. et al. Complete genome assembly provides insights into the centromere architecture of pumpkin (Cucurbita maxima). Plant Commun. 2024;5:100935

[22] Liu D, Liu K, Tong B. et al. Telomere-to-telomere, gap-free assem-bly of the Rosa rugosa reference genome. Hortic Plant J. 2024

[23] Mo C, Wang H, Wei M. et al. Complete genome assembly provides a high-quality skeleton for pan-NLRome construction in melon. Plant J. 2024;118: 2249-68

[24] Wei M, Huang Y, Mo C. et al. Telomere-to-telomere genome assembly of melon ( Cucumis melo L. var. inodorus) provides a high-quality reference for meta-QTL analysis of important traits. Hortic Res. 2023;10:uhad189

[25] Wang L, Li LL, Chen L. et al. Telomere-to-telomere and haplotype-resolved genome assembly of the Chinese cork oak (Quercus variabilis). Front Plant Sci. 2023;14:1290913

[26] Cheng H, Concepcion GT, Feng X. et al. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods. 2021;18: 170-5

[27] Durand NC, Shamim MS, Machol I. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 2016;3: 95-8

[28] Dudchenko O, Batra SS, Omer AD. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science. 2017;356: 92-5

[29] Robinson JT, Turner D, Durand NC. et al. Juicebox. js provides a cloud-based visualization system for Hi-C data. Cell Syst. 2018;6: 256-258.e1

[30] Jin JJ, Yu WB, Yang JB. et al. GetOrganelle: a fast and versatile toolkit for accurate de novo assembly of organelle genomes. Genome Biol. 2020;21:241

[31] Jia KH, Wang ZX, Wang L. et al. SubPhaser: a robust allopolyploid subgenome phasing method based on subgenome-specific k-mers. New Phytol. 2022;235: 801-9

[32] Abbas Q, Wilhelm M, Kuster B. et al. Exploring crop genomes: assembly features, gene prediction accuracy, and implications for proteomics studies. BMC Genomics. 2024;25:619

[33] Alexander DH, Novembre J, Lange K. Fast model-based estima-tion of ancestry in unrelated individuals. Genome Res. 2009;19: 1655-64

[34] Ambreen H, Oraon PK, Wahlang DR. et al. Long-read-based draft genome sequence of Indian black gram IPU-94-1 ’Uttara’: insights into disease resistance and seed storage protein genes. Plant Genome. 2022;15:e20234

[35] Browning SR, Browning BL. Rapid and accurate haplotype phas-ing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007;81: 1084-97

[36] Buchfink B, Xie C, Huson DH. Fast and sensitive protein align-ment using DIAMOND. Nat Methods. 2015;12: 59-60

[37] Cantalapiedra CP, Hernández-Plaza A, Letunic I. et al. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol Biol Evol. 2021;38: 5825-9

[38] Cantarel BL, Korf I, Robb SM. et al. MAKER: an easy-to-use anno-tation pipeline designed for emerging model organism genomes. Genome Res. 2008;18: 188-96

[39] Francis A, Singh NP, Singh M. et al. The ricebean genome pro-vides insight into Vigna genome evolution and facilitates genetic enhancement. Plant Biotechnol J. 2023;21: 1522-4

[40] Liang L, Zhang J, Xiao J. et al. Genome and pan-genome assembly of asparagus bean (Vigna unguiculata ssp. sesquipedialis)revealthe genetic basis of cold adaptation. Front Plant Sci. 2022;13:1059804

[41] Wang X, Chen L, Ma J. Genomic introgression through interspe-cific hybridization counteracts genetic bottleneck during soy-bean domestication. Genome Biol. 2019;20: 1-15

[42] Pollex RL, Hegele RA. Copy number variation in the human genome and its implications for cardiovascular disease. Circu-lation. 2007;115: 3130-8

[43] Sudmant PH, Rausch T, Gardner EJ. et al. An integrated map of structural variation in 2,504 human genomes. Nature. 2015;526: 75-81

[44] Kupsch C, Ruwe H, Gusewski S. et al. Arabidopsis chloroplast RNA binding proteins CP31A and CP29A associate with large transcript pools and confer cold stress tolerance by influencing multiple chloroplast RNA processing steps. Plant Cell. 2012;24: 4266-80

[45] Teng X-X, Cao W-L, Lan H-X. et al. OsNHX2, an Na+/H+ antiporter gene, can enhance salt tolerance in rice plants through more effective accumulation of toxic Na+ in leaf mes-ophyll and bundle sheath cells. Acta Physiol Plant. 2017;39: 1-8

[46] Li H, Durbin R. Genome assembly in the telomere-to-telomere era. Nat Rev Genet. 2024;1: 1-13

[47] Takahashi Y, Sakai H, Ariga H. et al. Domesticating Vigna stip-ulacea: chromosome-level genome assembly reveals VsPSAT1 as a candidate gene decreasing hard-seededness. Front Plant Sci. 2023;14:1119625

[48] Kaul T, Easwaran M, Thangaraj A. et al. De novo genome assembly of rice bean (Vigna umbellata) - a nominated nutritionally rich future crop reveals novel insights into flowering potential, habit, and palatability centric - traits for efficient domestication. Front Plant Sci. 2022;13:739654

[49] Jegadeesan S, Raizada A, Dhanasekar P. et al. Draft genome sequence of the pulse crop blackgram [Vigna mungo (L.) Hepper] reveals potential R-genes. Sci Rep. 2021;11:11247

[50] Hassan AH, Mokhtar MM, El Allali A. Transposable elements: multifunctional players in the plant genome. Front Plant Sci. 2023;14:1330127

[51] He X, Qi Z, Liu Z. et al. Pangenome analysis reveals transposon-driven genome evolution in cotton. BMC Biol. 2024;22:92

[52] Scott AJ, Chiang C, Hall IM. Structural variants are a major source of gene expression differences in humans and often affect multiple nearby genes. Genome Res. 2021;31: 2249-57

[53] Jobson E, Roberts R. Genomic structural variation in tomato and its role in plant immunity. Mol Hortic. 2022;2:7

[54] He S, Sun G, Geng X. et al. The genomic basis of geographic differentiation and fiber improvement in cultivated cotton. Nat Genet. 2021;53: 916-24

[55] Li N, He Q, Wang J. et al. Super-pangenome analyses highlight genomic diversity and structural variation across wild and cul-tivated tomato species. Nat Genet. 2023;55: 852-60

[56] He Q, Tang S, Zhi H. et al. A graph-based genome and pan-genome variation of the model plant Setaria. Nat Genet. 2023;55: 1232-42

[57] Nomberg G, Marinov O, Arya GC. et al. The key enzymes in the suberin biosynthetic pathway in plants: an update. Plan Theory. 2022;11:392

[58] Xin A, Herburger K. Precursor biosynthesis regulation of lignin, suberin and cutin. Protoplasma. 2021;258: 1171-8

[59] Ma QH. Lignin biosynthesis and its diversified roles in disease resistance. Genes (Basel). 2024;15:295

[60] Qin S, Fan C, Li X. et al. LACCASE14 is required for the deposition of guaiacyl lignin and affects cell wall digestibility in poplar. Biotechnol Biofuels Bioprod. 2020;13:197

[61] Wang Y, Wang M, Yan X. et al. The DEP1 mutation improves stem lodging resistance and biomass saccharification by affecting cell wall biosynthesis in rice. Rice (N Y). 2024;17:35

[62] Xiong W, Wu Z, Liu Y. et al. Mutation of 4-coumarate: coenzyme a ligase 1 gene affects lignin biosynthesis and increases the cell wall digestibility in maize brown midrib5 mutants. Biotechnol Biofuels Bioprod. 2019;12:82

[63] Doyle JJ, Doyle JL. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull. 1987;19: 11-5

[64] Wang C, Liu C, Roqueiro D. et al. Genome-wide analysis of local chromatin packing in Arabidopsis thaliana. Genome Res. 2015;25: 246-56

[65] Sun H, Ding J, Piednoël M. et al. findGSE: estimating genome size variation within human and Arabidopsis using k-mer frequen-cies. Bioinformatics. 2018;34: 550-7

[66] Hu J, Fan J, Sun Z. et al. NextPolish: a fast and efficient genome polishing tool for long-read assembly. Bioinformatics. 2020;36: 2253-5

[67] Pryszcz LP, Gabaldon T. Redundans: an assembly pipeline for highly heterozygous genomes. Nucleic Acids Res. 2016;44: e113

[68] Ou S, Su W, Liao Y. et al. Benchmarking transposable element annotation methods for creation of a streamlined, comprehen-sive pipeline. Genome Biol. 2019;20:275

[69] Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinformatics. 2004; Chapter 4:4.10.1-14

[70] Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12: 357-60

[71] Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34: 3094-100

[72] Pertea M, Pertea GM, Antonescu CM. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33: 290-5

[73] Haas BJ, Delcher AL, Mount SM. et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assem-blies. Nucleic Acids Res. 2003;31: 5654-66

[74] Stanke M, Diekhans M, Baertsch R. et al. Using native and syn-tenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008;24: 637-44

[75] Haas BJ, Salzberg SL, Zhu W. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol. 2008;9:R7

[76] Zhang RG, Li GY, Wang XL. et al. TEsorter: an accurate and fast method to classify LTR-retrotransposons in plant genomes. Hortic Res. 2022;9:uhac017

[77] Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detec-tion of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25: 955-64

[78] Kalvari I, Argasinska J, Quinones-Olvera N. et al. Rfam 13.0: shift-ing to a genome-centric resource for non-coding RNA families. Nucleic Acids Res. 2018;46: D335-42

[79] Jones P, Binns D, Chang HY. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30: 1236-40

[80] Simão FA, Waterhouse RM, Ioannidis P. et al. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31: 3210-2

[81] Rhie A, Walenz BP, Koren S. et al. Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biol. 2020;21:245

[82] Guan J, Zhang J, Gong D. et al. Genomic analyses of rice bean landraces reveal adaptation and yield related loci to accelerate breeding. Nat Commun. 2022;13:5707

[83] Lonardi S, Munoz-Amatriain M, Liang Q. et al. The genome of cowpea ( Vigna unguiculata L. Walp.). Plant J. 2019;98: 767-82

[84] Quinlan AR. BEDTools: the Swiss-army tool for genome feature analysis. Curr Protoc Bioinformatics. 2014;47:11.12.1-34

[85] Chen S, Zhou Y, Chen Y. et al. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34: i884-90

[86] Patro R, Duggal G, Love MI. et al. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14: 417-9

[87] Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 2015;4:1521

[88] Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550

[89] Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. 2013; arXiv preprint arXiv 1303: 3997

[90] Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. 2012; arXiv preprint arXiv 1207: 3907

[91] Cleal K, Baird DM. Dysgu: efficient structural variant calling using short or long reads. Nucleic Acids Res. 2022;50:e53

[92] Li H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics. 2011;27: 718-9

[93] Yang J, Lee SH, Goddard ME. et al. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88: 76-82

[94] Kopelman NM, Mayzel J, Jakobsson M. et al. Clumpak: a pro-gram for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour. 2015;15: 1179-91

[95] Villanueva RAM, Chen ZJ. ggplot2:Elegant Graphics for Data Anal-ysis. Taylor & Francis; Springer. 2019:160-7

[96] Danecek P, Auton A, Abecasis G. et al. The variant call format and VCFtools. Bioinformatics. 2011;27: 2156-8

[97] Chen H, Patterson N, Reich D. Population differentiation as a test for selective sweeps. Genome Res. 2010;20: 393-402

[98] Wu T, Hu E, Xu S. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2:100141

[99] Yu G. Enrichplot: visualization of functional enrichment result. 2021; R Package Version 1: 1