Integrative genome, transcriptome, microRNA, and degradome analysis of water dropwort (Oenanthe javanica) in response to water stress

Abstract

Water dropwort (Liyang Baiqin, Oenanthe javanica (BI.) DC.) is an aquatic perennial plant from the Apiaceae family with abundant protein, dietary fiber, vitamins, and minerals. It usually grows in wet soils and can even grow in water. Here, whole-genome sequencing of O. javanica via HiSeq 2000 sequencing technology was reported for the first time. The genome size was 1.28 Gb, including 42,270 genes, of which 93.92% could be functionally annotated. An online database of the whole-genome sequences of water dropwort, Water dropwortDB, was established to share the results and facilitate further research on O. javanica (database homepage: http://apiaceae.njau.edu.cn/waterdropwortdb). Water dropwortDB offers whole-genome and transcriptome sequences and a Basic Local Alignment Search Tool. Comparative analysis with other species showed that the evolutionary relationship between O. javanica and Daucus carota was the closest. Twenty-five gene families of O. javanica were found to be expanded, and some genetic factors (such as genes and miRNAs) related to phenotypic and anatomic differentiation in O. javanica under different water conditions were further investigated. Two miRNA and target gene pairs (miR408 and Oja15472, miR171 and Oja47040) were remarkably regulated by water stress. The obtained reference genome of O. javanica provides important information for future work, thus making in-depth genetic breeding and gene editing possible. The present study also provides a foundation for the understanding of the O. javanica response to water stress, including morphological, anatomical, and genetic differentiation.

References

[1] Naruhashi, N. & Iwatsubo, Y. Chromosome numbers and distributions of Oenanthe javanica (Umbelliferae) in Japan. J. Phytogeorg. Taxon. 46, 161-166 (1998).

[2] Sun, B. Y., Park, J. H., Kwak, M. J., Kim, C. H. & Kim, K. S. Chromosome counts from the flora of Korea with emphasis on Apiaceae. J. Plant Biol. 39, 15-22 (1996).

[3] Zhao, D., Yan, Z. M., Zhang, S. N., Li, J. X. & Liu, H. J. Karyotype analysis of main Umbelliferous vegetables. Acta Bot. Boreal-Occident. Sin. 30, 1978-1981 (2010).

[4] Li, M. Y. et al. The genome sequence of celery (Apium graveolens L.), an important leaf vegetable crop rich in apigenin in the Apiaceae family. Hortic. Res. 7, 9 (2020).

[5] Que, F. et al. Advances in research on the carrot, an important root vegetable in the Apiaceae family. Hortic. Res. 6, 69 (2019).

[6] Li, M. Y. et al. Advances in the research of celery, an important Apiaceae vegetable crop. Crit. Rev. Biotechnol. 38, 172-183 (2018).

[7] Kim, J. Y. et al. Oenanthe javanica extract accelerates ethanol metabolism in ethanol-treated animals. BMB Rep. 42, 482-485 (2009).

[8] Han, Y. Q., Huang, Z. M., Yang, X. B., Liu, H. Z. & Wu, G. X. In vivo and in vitro anti-hepatitis B virus activity of total phenolics from Oenanthe javanica. J. Ethnopharmacol. 118, 148-153 (2008).

[9] Ma, C. J. et al. Persicarin from water dropwort (Oenanthe javanica) protects primary cultured rat cortical cells from glutamate-induced neurotoxicity. Phytother. Res. 24, 913-918 (2010).

[10] Ma, G. et al. The flavonoid component isorhamnetin in vitro inhibits pro-liferation and induces apoptosis in Eca-109 cells. Chem. Biol. Interact. 167, 153-160 (2007).

[11] Ku, S. K., Han, M. S. & Bae, J. S. Down-regulation of endothelial protein C receptor shedding by persicarin and isorhamnetin-3-O-galactoside. Thromb. Res. 132, e58-e63 (2013).

[12] Jiang, Q. et al. De novo transcriptome assembly, gene annotation, marker development, and miRNA potential target genes validation under abiotic stresses in Oenanthe javanica. Mol. Genet. Genomics 290, 671-683 (2015).

[13] Jiang, Q. et al. Effects of abiotic stresses on the expression of Lhcb1 gene and photosynthesis of Oenanthe javanica and Apium graveolens. Biol. Plant. 58, 256-264 (2014).

[14] Jiang, Q. et al. Selection of suitable reference genes for qPCR normalization under abiotic stresses in Oenanthe javanica (BI.) DC. PLoS ONE 9, e92262 (2014).

[15] Najla, S., Sanoubar, R. & Murshed, R. Morphological and biochemical changes in two parsley varieties upon water stress. Physiol. Mol. Biol. Plants 18, 133-139 (2012).

[16] Striker, G. G., Casas, C., Manzur, M. E., Ploschuk, R. A. & Casal, J. J. Phenomic networks reveal largely independent root and shoot adjustment in water-logged plants of Lotus japonicus. Plant Cell Environ. 37, 2278-2293 (2014).

[17] Qi, B., Yang, Y., Yin, Y., Xu, M. & Li, H. De novo sequencing, assembly, and analysis of the Taxodium ‘Zhongshansa’ roots and shoots transcriptome in response to short-term waterlogging. BMC Plant Biol. 14, 201 (2014).

[18] Zhu, J. K. Salt and drought stress signal transduction in plants. Annu. Rev. Plant Biol. 53, 247-273 (2002).

[19] Sun, X. et al. Advances in identification and validation of plant microRNAs and their target genes. Physiol. Plant. 152, 203-218 (2014).

[20] Wang, C. et al. Characterization of target mRNAs for grapevine microRNAs with an integrated strategy of modified RLM-RACE, newly developed PPM-RACE and qPCRs. J. Plant Physiol. 170, 943-957 (2013).

[21] Shamimuzzaman, M. & Vodkin, L. Identification of soybean seed develop-mental stage-specific and tissue-specific miRNA targets by degradome sequencing. BMC Genomics 13, 310 (2012).

[22] Luo, R. B. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1, 18 (2012).

[23] Grabherr, M. G. et al. Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat. Biotechnol. 29, 644-652 (2011).

[24] Kötting, O. et al. Identification of a novel enzyme required for starch meta-bolism in Arabidopsis leaves. The phosphoglucan, water pikinase. Plant Physiol. 137, 242-252 (2005).

[25] Nylander, M., Svensson, J., Palva, E. T. & Welin, B. V. Stress-induced accumu-lation and tissue-specific localization of dehydrins in Arabidopsis thaliana. Plant Mol. Biol. 45, 263-279 (2001).

[26] Lee, S. C. et al. Characterization of an abiotic stress-inducible dehydrin gene, OsDhn1, in rice (Oryza sativa L.). Mol. Cells 19, 212-218 (2005).

[27] Chan, Z. L. Expression profiling of ABA pathway transcripts indicates crosstalk between abiotic and biotic stress responses in Arabidopsis. Genomics 100, 110-115 (2012).

[28] Grondin, A., Mauleon, R., Vadez, V. & Henry, A. Root aquaporins contribute to whole plant water fluxes under drought stress in rice (Oryza sativa L.). Plant Cell Environ. 39, 347-365 (2016).

[29] Addo-Quaye, C., Miller, W. & Axtell, M. J. CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics 25, 130-131 (2009).

[30] Ma, Z. et al. Arabidopsis miR171-targeted scarecrow-like proteins bind to GT cis-elements and mediate gibberellin-regulated chlorophyll biosynthesis under light conditions. PLoS Genet. 10, e1004519 (2014).

[31] Liu, Z. W., Li, H., Liu, J. X., Wang, Y. & Zhuang, J. Integrative transcriptome, proteome, and microRNA analysis reveals the effects of nitrogen sufficiency and deficiency conditions on theanine metabolism in the tea plant (Camellia sinensis). Hortic. Res. 7, 65 (2020).

[32] Kaessmann, H. Origins, evolution, and phenotypic impact of new genes. Genome Res. 20, 1313-1326 (2010).

[33] Iorizzo, M. et al. A high-quality carrot genome assembly provides new insights into carotenoid accumulation and asterid genome evolution. Nat. Genet. 48, 657-666 (2016).

[34] Song, X. M. et al. Deciphering the high-quality genome sequence of coriander that causes controversial feelings. Plant Biotechnol. J. 18, 1444-1456 (2020).

[35] Song, X. M. et al. The celery genome sequence reveals sequential paleo-polyploidizations, karyotype evolution, and resistance gene reduction in Apiales. Plant Biotechnol. J. 19, 731-744 (2020).

[36] Han, M. V., Thomas, G. W., Lugo-Martinez, J. & Hahn, M. W. Estimating gene gain and loss rates in the presence of error in genome assembly and annotation using CAFE 3. Mol. Biol. Evol. 30, 1987-1997 (2013).

[37] Morales-Cruz, A. et al. Distinctive expansion of gene families associated with plant cell wall degradation, secondary metabolism, and nutrient uptake in the genomes of grapevine trunk pathogens. BMC Genomics 16, 469 (2015).

[38] Zhao, M. M. et al. The application of single-cell RNA sequencing in studies of autoimmune diseases: a comprehensive review. Clin. Rev. Allergy Immunol. 60, 68-86 (2021).

[39] Li, J. R. et al. The chromosome-based lavender genome provides new insights into Lamiaceae evolution and terpenoid biosynthesis. Hortic. Res. 8, 53 (2021).

[40] Li, C. S., Lin, F., An, D., Wang, W. Q. & Huang, R. D. Genome sequencing and assembly by long reads in plants. Genes 9, 6 (2017).

[41] Ca LI, H. P. & Mücke, I. History, overview and applications of mediated lig-nolytic systems, especially laccase-mediator-systems (Lignozym-process). J. Biotechnol. 53, 163-202 (1997).

[42] Miniussi, M., Del Terra, L., Savi, T., Pallavicini, A. & Nardini, A. Aquaporins in Coffea arabica L.: identification, expression, and impacts on plant water rela-tions and hydraulics. Plant Physiol. Biochem. 95, 92-102 (2015).

[43] de la Cruz, J., Karbstein, K. & Woolford, J. L. Functions of ribosomal proteins in assembly of eukaryotic ribosomes in vivo. Annu. Rev. Biochem 84, 93-129 (2015).

[44] Chou, Y. T. & Lo, K. Y. Thallium(I) treatment induces nucleolar stress to stop protein synthesis and cell growth. Sci. Rep. 9, 6905 (2019).

[45] Axtell, M. J. & Meyers, B. C. Revisiting criteria for plant MicroRNA annotation in the era of big data. Plant Cell 30, 272-284 (2018).

[46] Jovanović, Ž., Stanisavljević, N., Mikić, A., Radović, S. & Maksimović, V. Water deficit down-regulates miR398 and miR408 in pea (Pisum sativum L.). Plant Physiol. Biochem. 83, 26-31 (2014).

[47] Hajyzadeh, M., Turktas, M., Khawar, K. M. & Unver, T. miR408 overexpression causes increased drought tolerance in chickpea. Gene 555, 186-193 (2015).

[48] Curaba, J., Talbot, M., Li, Z. Y. & Helliwell, C. Over-expression of microRNA171 affects Phase transitions and floral meristem determinancy in barley. BMC Plant Biol. 13, 6 (2013).

[49] Rogers, S. O. & Bendich, A. J. Extraction of DNA from milligram amounts of fresh, herbarium and mummified plant tissues. Plant Mol. Biol. 5, 69-76 (1985).

[50] Cantarel, B. L. et al. MAKER: An easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 18, 188-196 (2008).

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

[52] Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435-W439 (2006).

[53] Slater, G. S. & Birney, E. Automated generation of heuristics for biological sequence comparison. BMC Bioinforma. 6, 31 (2005).

[54] Yandell, M. & Ence, D. A beginner’s guide to eukaryotic genome annotation. Nat. Rev. Genet. 13, 329-342 (2012).

[55] Eilbeck, K., Moore, B., Holt, C. & Yandell, M. Quantitative measures for the management and comparison of annotated genomes. BMC Bioinforma. 10, 67 (2009).

[56] Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236-1240 (2014).

[57] Hunter, S. et al. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 40, D306-D312 (2012).

[58] Finn, R. D. et al. The Pfam protein families database. Nucleic Acids Res. 38, D211-D222 (2010).

[59] Attwood, T. K. et al. The PRINTS database: a fine-grained protein sequence annotation and analysis resource-its status in 2012. Database 2012, bas019 (2012).

[60] Letunic, I., Doerks, T. & Bork, P. SMART 7: recent updates to the protein domain annotation resource. Nucleic Acids Res. 40, D302-D305 (2012).

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

[62] Li, L., Stoeckert, C. J. Jr & Roos, D. S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 13, 2178-2189 (2003).

[63] Riaño-Pachón, D. M., Ruzicic, S., Dreyer, I. & Mueller-Roeber, B. PlnTFDB: an integrative plant transcription factor database. BMC Bioinforma. 8, 42 (2007).

[64] Hedges, S. B., Marin, J., Suleski, M., Paymer, M. & Kumar, S. Tree of life reveals clock-like speciation and diversification. Mol. Biol. Evol. 32, 835-845 (2015).

[65] Fu, L. M., Niu, B. F., Zhu, Z. W., Wu, S. T. & Li, W. Z. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150-3152 (2012).

[66] Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinforma. 5, 113 (2004).

[67] Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641-1650 (2009).

[68] Stein, L. D. et al. The generic genome browser: a building block for a model organism system database. Genome Res. 12, 1599-1610 (2002).

[69] Wang, G. L., Que, F., Xu, Z. S., Wang, F. & Xiong, A. S. Exogenous gibberellin enhances secondary xylem development and lignification in carrot taproot. Protoplasma 254, 839-848 (2016).

[70] Wang, G. L. et al. Morphological characteristics, anatomical structure, and gene expression: novel insights into gibberellin biosynthesis and perception during carrot growth and development. Hortic. Res. 2, 15028 (2015).

[71] Li, M. Y., Wang, F., Jiang, Q., Ma, J. & Xiong, A. S. Identification of SSRs and differentially expressed genes in two cultivars of celery (Apium graveolens L.) by deep transcriptome sequencing. Hortic. Res. 1, 10 (2014).

[72] Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

[73] Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinforma. 12, 323 (2011).

[74] Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621-628 (2008).

[75] Leng, N. et al. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics 29, 1035-1043 (2013).

[76] Burge, S. W. et al. Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 41, D226-D232 (2013).

[77] Friedländer, M. R., Mackowiak, S. D., Li, N., Chen, W. & Rajewsky, N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 40, 37-52 (2012).

[78] Zuker, M . Mfold web server for nucleic acid folding and hybridization pre-diction. Nucleic Acids Res. 31, 3406-3415 (2003).

[79] Fahlgren, N. et al. High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA Genes. PLoS ONE 2, e219 (2007).

[80] German, M. A., Luo, S., Schroth, G., Meyers, B. C. & Green, P. J. Construction of parallel analysis of RNA ends (PARE) libraries for the study of cleaved miRNA targets and the RNA degradome. Nat. Protoc. 4, 356-362 (2009).

[81] Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinforma. 4, 41 (2003).

[82] Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674-3676 (2005).

[83] Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A. C. & Kanehisa, M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W182-W1855 (2007).