Genome assembly of pomegranate highlights structural variations driving population differentiation and key loci underpinning cold adaption

Abstract

Cold damage poses a significant challenge to the cultivation of soft-seeded pomegranate varieties, hindering the growth of the pomegranate industry. The genetic basis of cold tolerance in pomegranates has remained elusive, largely due to the lack of high-quality genome assemblies for cold-tolerant varieties and comprehensive population-scale genomic studies. In this study, we addressed these challenges by assembling a high-quality chromosome-level reference genome for 'Sanbai', a pomegranate variety renowned for its freezing resistance, achieving an impressive contig N50 of 15.93 Mb. This robust assembly, enhanced by long-read sequencing of 38 pomegranate accessions, facilitated the identification of 14 239 polymorphic structural variants, revealing their critical roles in genomic diversity and population differentiation related to cold tolerance. Of particular significance was the discovery of a ~ 5.4-Mb inversion on chromosome 1, which emerged as an important factor affecting cold tolerance in pomegranate. Moreover, through the integration of bulked segregant analysis, differential selection analysis, and genetic transformation techniques, we identified and validated the interaction between the PgNAC12 transcription factor and PgCBF1, disclosing their pivotal roles in response to cold stress. These findings mark a significant advancement in pomegranate genomics, offering novel insights into the genetic mechanisms of cold tolerance and providing valuable resources for the genetic improvement of soft-seeded pomegranate varieties.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (31901343), Natural Science Foundation of Henan Province (232300420011, 242300420155), Key Research and Development Program of Shandong Providence in China (2022TZXD009), and Shandong Province Agricultural Improved Seed Project (2021LZGC007).

Author Contributions

X.L., and W.Y., designed the experiments. X.L, performed the experiments. W.Y., D.T., X.Z., and Z.S., performed de novo genome assembly, annotation and RNA-seq data analysis. X.L., D.Z., B.L., H.L., Y.C., D.M., Zhihua Song, Q.Y., and J.Z., designed and completed the biological experiments. X.L., wrote the manuscript, with help from W.Y., K.M., J.Z., and Z.W. All the authors read and approved the final manuscript.

Data availability

All datasets have been deposited in National Genomics Data Center (NGDC) with the following accession codes: genome assembly and gene annotation of 'Sanbai', GWHEQOV00000000; genome resequencing of 38 pomegranate accessions using the Illumina platform, CRA021734; genome resequencing of 38 pomegranate accessions using the Oxford Nanopore platform, CRA021775; and genome resequencing data for Bulked Segregant Analysis, CRA021764.

Conflict of interest statement

The authors declare no competing interests.

Supplementary Data

Supplementary data is available at Horticulture Research online.

References

[1] Teixeira da Silva JA, Rana TS, Narzary D. et al. Pomegranate biology and biotechnology: a review. Sci Hortic. 2013;160:85-107

[2] Yuan Z, Fang Y, Zhang T. et al. The pomegranate (Punica granatum L.) genome provides insights into fruit quality and ovule devel-opmental biology. Plant Biotechnol J. 2018;16:1363-74

[3] Hu J, Ma H, Cheng B. CFD simulation optimization of soft seed pomegranate cold protection facility. Agric Eng. 2023;13: 54-9

[4] Liu W, Liu B, Cai W. et al. Research progress of cultivars of soft-seed pomegranate in domestic. J North Agric. 2020;48:75-82

[5] Qin G, Xu C, Ming R. et al. The pomegranate (Punica granatum L.) genome and the genomics of punicalagin biosynthesi. Plant J. 2017;91:1108-28

[6] Luo X, Li H, Wu Z. et al. The pomegranate (Punica granatum L.) draft genome dissects genetic divergence between soft- and hard-seeded cultivars. Plant Biotechnol J. 2020;18:955-68

[7] Luo X, Cao D, Zhang J. et al. Integrated microRNA and mRNA expression profiling reveals a complex network regu-lating pomegranate (Punica granatum L.) seed hardness. Sci Rep. 2018;8:9292

[8] Niu J, Cao D, Li H. et al. Quantitative proteomics of pomegranate varieties with contrasting seed hardness during seed develop-ment stages. Tree Genet Genomes. 2018;14:14

[9] Xia X, Li H, Cao D. et al. Characterization of a NAC transcription factor involved in the regulation of pomegranate seed hardness (Punica granatum L.). Plant Physiol Biochem. 2019;139:379-88

[10] Xue H, Cao S, Li H. et al. De novo transcriptome assembly and quantification reveal differentially expressed genes between soft-seed and hard-seed pomegranate ( Punica granatum L.). PLoS One. 2017;12:e0178809

[11] Qin G, Liu C, Li J. et al. Diversity of metabolite accumulation patterns in inner and outer seed coats of pomegranate: explor-ing their relationship with genetic mechanisms of seed coat development. Hortic Res. 2020;7:10

[12] Zarei A, Zamani Z, Fatahi R. et al. Differential expression of cell wall related genes in the seeds of soft- and hard-seeded pomegranate genotypes. Sci Hortic. 2016;205:7-16

[13] Ou S, Chen J, Jiang N. Assessing genome assembly quality using the LTR assembly index (LAI). Nucleic Acids Res. 2018;46:e126

[14] Ouzhuluobu HY, Lou H. et al. De novo assembly of a Tibetan genome and identification of novel structural variants associ-ated with high-altitude adaptation. Natl Sci Rev. 2020;7:391-402

[15] Barkley NA, Roose ML, Krueger RR. et al. Assessing genetic diver-sity and population structure in a citrus germplasm collection utilizing simple sequence repeat markers (SSRs). Theor Appl Genet. 2006;112:1519-31

[16] Kou Y, Liao Y, Toivainen T. et al. Evolutionary genomics of structural variation in Asian Rice (Oryza sativa) domestication. Mol Biol Evol. 2020;37:3507-24

[17] Zhou Y, Minio A, Massonnet M. et al. The population genetics of structural variants in grapevine domestication. Nat Plants. 2019;5:965-79

[18] Aslam M, Fakher B, Ashraf MA. et al. Plant low-temperature stress: signaling and response. Agronomy. 2022;12:702

[19] Sedding DG. FoxO transcription factors in oxidative stress response and ageing - a new fork on the way to longevity? Biol Chem. 2008;389:279-83

[20] Diao P, Chen C, Zhang Y. et al. The role of NAC transcription factor in plant cold response. Plant Signal Behav. 2020;15:1785668

[21] Hao YJ, Wei W, Song QX. et al. Soybean NAC transcription factors promote abiotic stress tolerance and lateral root formation in transgenic plants. Plant J Cell Mol Biol. 2011;68:302-13

[22] Shan W, Kuang JF, Lu WJ. et al. Banana fruit NAC transcription factor MaNAC1 is a direct target of MaICE1 and involved in cold stress through interacting with MaCBF1. Plant Cell Environ. 2014;37:2116-27

[23] Hurwitz BL, Kudrna D, Yu Y. et al. Rice structural variation: a comparative analysis of structural variation between rice and three of its closest relatives in the genus Oryza. Plant J. 2010;63: 990-1003

[24] Ma Z, Zhang Y, Wu L. et al. High-quality genome assembly and resequencing of modern cotton cultivars provide resources for crop improvement. Nat Genet. 2021;53:1385-91

[25] Zhang X, Liu T, Wang J. et al. Pan-genome of Raphanus highlights genetic variation and introgression among domesticated, wild, and weedy radishes. Mol Plant. 2021;14:2032-55

[26] Li C, Xiang X, Huang Y. et al. Long-read sequencing reveals genomic structural variations that underlie creation of quality protein maize. Nat Commun. 2020;11:17

[27] Yang N, Liu J, Gao Q. et al. Genome assembly of a tropical maize inbred line provides insights into structural variation and crop improvement. Nat Genet. 2019;51:1052-9

[28] Kreplak J, Madoui MA, Capal P. et al. A reference genome for pea provides insight into legume genome evolution. Nat Genet. 2019;51:1411-22

[29] Chin CS, Peluso P, Sedlazeck FJ. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat Meth-ods. 2016;13:1050-4

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

[31] Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint. 2013;q-bio.GN:1303.3997

[32] Marcais G, Delcher AL, Phillippy AM. et al. MUMmer4: a fast and versatile genome alignment system. PLoS Comput Biol. 2018;14:e1005944

[33] Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999;27:573-80

[34] Stanke M, Keller O, Gunduz I. et al. AUGUSTUS: ab initio predic-tion of alternative transcripts. Nucleic Acids Res. 2006;34:W435-9

[35] Korf I. Gene finding in novel genomes. BMC Bioinformatics. 2004;5:59

[36] 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

[37] Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019;47: D155-62

[38] Kalvari I, Nawrocki EP, Ontiveros-Palacios N. et al. Rfam 14: expanded coverage of metagenomic, viral and microRNA fam-ilies. Nucleic Acids Res. 2021;49:D192-200

[39] Chan PP, Lin BY, Mak AJ. et al. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res. 2021;49:9077-96

[40] Goel M, Sun H, Jiao WB. et al. SyRI: finding genomic rear-rangements and local sequence differences from whole-genome assemblies. Genome Biol. 2019;20:277

[41] Khelik K, Lagesen K, Sandve GK. et al. NucDiff: in-depth characterization and annotation of differences between two sets of DNA sequences. BMC Bioinformatics. 2017;18: 338

[42] Wang Y, Jia L, Tian G. et al. shinyCircos-V2.0: leveraging the creation of Circos plot with enhanced usability and advanced features. iMeta. 2023;2:e109

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

[44] Vilella AJ, Blanco-Garcia A, Hutter S. et al. VariScan: analysis of evolutionary patterns from large-scale DNA sequence polymor-phism data. Bioinformatics. 2005;21:2791-3

[45] 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

[46] Purcell S, Neale B, Todd-Brown K. et al. PLINK: a tool set for whole-genome association and population-based linkage anal-yses. Am J Hum Genet. 2007;81:559-75

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

[48] Xue H, Shi T, Wang F. et al. Interval mapping for red/green skin color in Asian pears using a modified QTL-seq method. Hortic Res. 2017;4:17053

[49] Clough SJ, Bent AF. Floral dip: a simplified method for agrobac-terium-mediated transformation of Arabidopsis thaliana. Plant J. 1998;16:735-43