A chromosome-level genome assembly of rugged rose (Rosa rugosa) provides insights into its evolution, ecology, and floral characteristics

Abstract

Rosa rugosa, commonly known as rugged rose, is a perennial ornamental shrub. It produces beautiful flowers with a mild fragrance and colorful seed pods. Unlike many other cultivated roses, R. rugosa adapts to a wide range of habitat types and harsh environmental conditions such as salinity, alkaline, shade, drought, high humidity, and frigid temperatures. Here, we produced and analyzed a high-quality genome sequence for R. rugosa to understand its ecology, floral characteristics and evolution. PacBio HiFi reads were initially used to construct the draft genome of R. rugosa, and then Hi-C sequencing was applied to assemble the contigs into 7 chromosomes. We obtained a 382.6 Mb genome encoding 39,704 protein-coding genes. The genome of R. rugosa appears to be conserved with no additional whole-genome duplication after the gamma whole-genome triplication (WGT), which occurred ~100 million years ago in the ancestor of core eudicots. Based on a comparative analysis of the high-quality genome assembly of R. rugosa and other high-quality Rosaceae genomes, we found a unique large inverted segment in the Chinese rose R. chinensis and a retroposition in strawberry caused by post-WGT events. We also found that floral development- and stress response signaling-related gene modules were retained after the WGT. Two MADS-box genes involved in floral development and the stress-related transcription factors DREB2A-INTERACTING PROTEIN 2 (DRIP2) and PEPTIDE TRANSPORTER 3 (PTR3) were found to be positively selected in evolution, which may have contributed to the unique ability of this plant to adapt to harsh environments. In summary, the high-quality genome sequence of R. rugosa provides a map for genetic studies and molecular breeding of this plant and enables comparative genomic studies of Rosa in the near future.

References

[1] Altiner, D. & Kilicgun, H. The antioxidant effect of Rosa rugosa. Drug Metabol. Drug Interact. 23, 323-327 (2008).

[2] Cendrowski, A., Krasniewska, K., Przybyl, J.L., Zielinska, A. & Kalisz, S. Antibacterial and antioxidant activity of extracts from rose fruits (Rosa rugosa). Molecules 25, 1365 (2020).

[3] Belcher, C. Effect of sand cover on the survival and vigor of Rosa rugosa. Int. J. Biometeorol. 21, 276-280 (1977).

[4] Hashidoko, Y. The phytochemistry of Rosa rugosa. Phytochemistry 43, 535-549 (1996).

[5] Stefanowicz, A. M. et al. Invasion of Rosa rugosa induced changes in soil nutrients and microbial communities of coastal sand dunes. Sci. Total Environ. 677, 340-349 (2019).

[6] Chen, F. et al. The sequenced angiosperm genomes and genome databases. Front. Plant Sci. 9, 418 (2018).

[7] Zhang, L. et al. The water lily genome and the early evolution of flowering plants. Nature 577, 79-84 (2020).

[8] Chen, F. et al. Genome sequences of horticultural plants: past, present, and future. Hortic. Res. 6, 112 (2019).

[9] Wissemann, V. & Ritz, C. M. The genus Rosa (Rosoideae, Rosaceae) revisited: molecular analysis of nrITS-1 and atpB-rbcL intergenic spacer (IGS) versus conventional taxonomy. Bot. J. Linn. Soc. 147, 275-290 (2005).

[10] Nakamura, N. et al. Genome structure of Rosa multiflora, a wild ancestor of cultivated roses. DNA Res. 25, 113-121 (2018).

[11] Raymond, O. et al. The Rosa genome provides new insights into the domestication of modern roses. Nat. Genet. 50, 772-777 (2018).

[12] Hibrand Saint-Oyant, L. et al. A high-quality genome sequence of Rosa chi-nensis to elucidate ornamental traits. Nat. Plants 4, 473-484 (2018).

[13] Kim, Y. et al. The complete chloroplast genome of candidate new species from Rosa rugosa in Korea (Rosaceae). Mitochondrial DNA B Resour. 4, 2433-2435 (2019).

[14] Park, J., Xi, H., Kim, Y., Nam, S. & Heo, K. I. The complete mitochondrial genome of new species candidate of Rosa rugosa (Rosaceae). Mitochondrial DNA B Resour. 5, 3435-3437 (2020).

[15] Edger, P. P. et al. Single-molecule sequencing and optical mapping yields an improved genome of woodland strawberry (Fragaria vesca) with chromosome-scale contiguity. Gigascience 7, 1-7 (2018).

[16] Shulaev, V. et al. The genome of woodland strawberry (Fragaria vesca). Nat. Genet. 43, 109-116 (2011).

[17] Zhang, J. et al. The high-quality genome of diploid strawberry (Fragaria nil-gerrensis) provides new insights into anthocyanin accumulation. Plant Bio-technol. J. 18, 1908-1924 (2020).

[18] Liu, C. Y. et al. Phylogenetic Relationships in the genus Rosa revisited based on rpl16, trnL-F, and atpB-rbcL sequences. Hortscience 50, 1618-1624 (2015).

[19] Zhang, L. et al. The ancient wave of polyploidization events in flowering plants and their facilitated adaptation to environmental stress. Plant Cell Environ. 43, 2847-2856 (2020).

[20] Van de Peer, Y., Mizrachi, E. & Marchal, K. The evolutionary significance of polyploidy. Nat. Rev. Genet. 18, 411-424 (2017).

[21] Jaillon, O. et al. The grapevine genome sequence suggests ancestral hex-aploidization in major angiosperm phyla. Nature 449, 463-467 (2007).

[22] Xiang, Y. et al. Evolution of Rosaceae fruit types based on nuclear phylogeny in the context of geological times and genome duplication. Mol. Biol. Evol. 34, 262-281 (2017).

[23] Zhou, W. et al. Studies of aroma components on essential oil of Chinese kushui rose. Se Pu 20, 560-564 (2002).

[24] Feng, L. et al. Flowery odor formation revealed by differential expression of monoterpene biosynthetic genes and monoterpene accumulation in rose (Rosa rugosa Thunb.). Plant Physiol. Biochem. 75, 80-88 (2014).

[25] Chen, F., Tholl, D., Bohlmann, J. & Pichersky, E. The family of terpene synthases in plants: a mid-size family of genes for specialized metabolism that is highly diversified throughout the kingdom. Plant J. 66, 212-229 (2011).

[26] Nurk, S. et al. HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads. Genome Res. 30, 1291-1305 (2020).

[27] Pryszcz, L. P. & Gabaldon, T. Redundans: an assembly pipeline for highly heterozygous genomes. Nucleic Acids Res. 44, e113 (2016).

[28] Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754-1760 (2009).

[29] Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92-95 (2017).

[30] Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99-101 (2016).

[31] Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness. Methods Mol. Biol. 1962, 227-245 (2019).

[32] Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl Acad. Sci. USA. 117, 9451-9457 (2020).

[33] Tempel, S. Using and understanding RepeatMasker. Methods Mol. Biol. 859, 29-51 (2012).

[34] Bao, W., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob. DNA 6, 11 (2015).

[35] Korf, I. Gene finding in novel genomes. BMC Bioinforma. 5, 59 (2004).

[36] Stanke, M., Schoffmann, O., Morgenstern, B. & Waack, S. Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources. BMC Bioinforma. 7, 62 (2006).

[37] Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357-360 (2015).

[38] Li, H. et al. Genome Project Data Processing S: The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078-2079 (2009).

[39] Keilwagen, J., Hartung, F. & Grau, J. GeMoMa: homology-based gene predic-tion utilizing intron position conservation and RNA-seq data. Methods Mol. Biol. 1962, 161-177 (2019).

[40] Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVi-denceModeler and the program to assemble spliced alignments. Genome Biol. 9, R7 (2008).

[41] Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33-36 (2000).

[42] Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25-29 (2000).

[43] Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27-30 (2000).

[44] Johnson, L. S., Eddy, S. R. & Portugaly, E. Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinforma. 11, 431 (2010).

[45] Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222-D230 (2014). Database issue.

[46] Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).

[47] Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792-1797 (2004).

[48] Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312-1313 (2014).

[49] Mirarab, S. & Warnow, T. ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes. Bioinformatics 31, i44-i52 (2015).

[50] Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586-1591 (2007).

[51] De Bie, T., Cristianini, N., Demuth, J. P. & Hahn, M. W. CAFE: a computational tool for the study of gene family evolution. Bioinformatics 22, 1269-1271 (2006).

[52] Tang, H. et al. Synteny and collinearity in plant genomes. Science 320, 486-488 (2008).

[53] Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639-1645 (2009).

[54] Zwaenepoel, A. & Van de Peer, Y. wgd-simple command line tools for the analysis of ancient whole-genome duplications. Bioinformatics 35, 2153-2155 (2019).

[55] Zhang, Z. et al. ParaAT: a parallel tool for constructing multiple protein-coding DNA alignments. Biochem. Biophys. Res. Commun. 419, 779-781 (2012).

[56] Wang, D., Zhang, Y., Zhang, Z., Zhu, J. & Yu, J. KaKs_Calculator 2.0: a toolkit incorporating gamma-series methods and sliding window strategies. Geno-mics Proteom. Bioinforma. 8, 77-80 (2010).

[57] Tian, T. et al. agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Res. 45, W122-W129 (2017).