m6A RNA Methylation in Marine Plants: First Insights and Relevance for Biological Rhythms
Abstract
:1. Introduction
2. Results and Discussion
2.1. First Identification of RNA Methyltransferase (Writers) and Demethylase (Erasers) Genes in Seagrasses
2.2. Interspecific Variations in the Daily Transcript Levels of Writers and Erasers and Global N6-Methyl-Adenosine (m6A) in Seagrasses
2.3. Intraspecific Variations in the Daily Transcript Levels of Writers and Erasers and Global N6-methyl-Adenosine (m6A) in Seagrasses across Latitudes
3. Materials and Methods
3.1. Inventory of RNA-Methylation-Associated Genes (m6A) in Seagrasses and Orthologs/Paralogs Dataset Construction
3.2. Study Site, Plant Sampling, and Experimental Design
3.3. RNA Extraction and cDNA Synthesis
3.4. Primer Design, cDNA Synthesis, and Reverse-Transcription Quantitative Polymerase Chain Reaction (RT-qPCR)
3.5. Global N6-Methyl-Adenosine (m6A) Quantification
3.6. Statistical Analysis
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Allis, C.D.; Jenuwein, T. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 2016, 17, 487–500. [Google Scholar] [CrossRef]
- Chinnusamy, V.; Zhu, J.-K. Epigenetic regulation of stress responses in plants. Curr. Opin. Plant Biol. 2009, 12, 133–139. [Google Scholar] [CrossRef][Green Version]
- Mirouze, M.; Paszkowski, J. Epigenetic contribution to stress adaptation in plants. Curr. Opin. Plant Biol. 2011, 14, 267–274. [Google Scholar] [CrossRef]
- Kumar, S. Epigenomics of Plant Responses to Environmental Stress. Epigenomes 2018, 2, 6. [Google Scholar] [CrossRef][Green Version]
- Saletore, Y.; Meyer, K.D.; Korlach, J.; Vilfan, I.D.; Jaffrey, S.R.; Mason, C.E. The birth of the Epitranscriptome: Deciphering the function of RNA modifications. Genome Biol. 2012, 13, 175. [Google Scholar] [CrossRef][Green Version]
- Meyer, K.D.; Jaffrey, S.R. The dynamic epitranscriptome: N6-methyladenosine and gene expression control. Nat. Rev. Mol. Cell Biol. 2014, 15, 313–326. [Google Scholar] [CrossRef][Green Version]
- Frye, M.; Jaffrey, S.R.; Pan, T.; Rechavi, G.; Suzuki, T. RNA modifications: What have we learned and where are we headed? Nat. Rev. Genet. 2016, 17, 365–372. [Google Scholar] [CrossRef]
- Liang, Z.; Riaz, A.; Chachar, S.; Ding, Y.; Du, H.; Gu, X. Epigenetic Modifications of mRNA and DNA in Plants. Mol. Plant 2020, 13, 14–30. [Google Scholar] [CrossRef]
- Kiani, J.; Grandjean, V.; Liebers, R.; Tuorto, F.; Ghanbarian, H.; Lyko, F.; Cuzin, F.; Rassoulzadegan, M. RNA–Mediated Epigenetic Heredity Requires the Cytosine Methyltransferase Dnmt2. PLoS Genet. 2013, 9, e1003498. [Google Scholar] [CrossRef][Green Version]
- Liebers, R.; Rassoulzadegan, M.; Lyko, F. Epigenetic Regulation by Heritable RNA. PLoS Genet. 2014, 10, e1004296. [Google Scholar] [CrossRef][Green Version]
- Rassoulzadegan, M.; Cuzin, F. Epigenetic heredity: RNA-mediated modes of phenotypic variation. Ann. N. Y. Acad. Sci. 2015, 1341, 172–175. [Google Scholar] [CrossRef]
- Cantara, W.A.; Crain, P.F.; Rozenski, J.; McCloskey, J.A.; Harris, K.A.; Zhang, X.; Vendeix, F.A.P.; Fabris, D.; Agris, P.F. The RNA modification database, RNAMDB: 2011 update. Nucleic Acids Res. 2010, 39, D195–D201. [Google Scholar] [CrossRef][Green Version]
- Boccaletto, P.; A Machnicka, M.; Purta, E.; Piątkowski, P.; Bagiński, B.; Wirecki, T.K.; De Crécy-Lagard, V.; Ross, R.; A Limbach, P.; Kotter, A.; et al. MODOMICS: A database of RNA modification pathways. 2017 update. Nucleic Acids Res. 2017, 46, D303–D307. [Google Scholar] [CrossRef]
- Liu, N.; Pan, T. N6-methyladenosine–encoded epitranscriptomics. Nat. Struct. Mol. Biol. 2016, 23, 98–102. [Google Scholar] [CrossRef]
- Choi, J.; Ieong, K.-W.; Demirci, H.; Chen, J.; Petrov, A.; Prabhakar, A.; O’Leary, S.E.; Dominissini, D.; Rechavi, G.; Soltis, S.M.; et al. N6-methyladenosine in mRNA disrupts tRNA selection and translation-elongation dynamics. Nat. Struct. Mol. Biol. 2016, 23, 110–115. [Google Scholar] [CrossRef][Green Version]
- Haussmann, I.U.; Bodi, Z.; Sanchez-Moran, E.; Mongan, N.P.; Archer, N.; Fray, R.G.; Soller, M. m6A potentiates Sxl alternative pre-mRNA splicing for robust Drosophila sex determination. Nat. Cell Biol. 2016, 540, 301–304. [Google Scholar] [CrossRef][Green Version]
- Wei, L.-H.; Song, P.; Wang, Y.; Lu, Z.; Tang, Q.; Yu, Q.; Xiao, Y.; Zhang, X.; Duan, H.-C.; Jia, G. The m6A Reader ECT2 Controls Trichome Morphology by Affecting mRNA Stability in Arabidopsis. Plant Cell 2018, 30, 968–985. [Google Scholar] [CrossRef][Green Version]
- Lewis, C.J.; Pan, T.; Kalsotra, A. RNA modifications and structures cooperate to guide RNA–protein interactions. Nat. Rev. Mol. Cell Biol. 2017, 18, 202–210. [Google Scholar] [CrossRef][Green Version]
- Zheng, G.; Dahl, J.A.; Niu, Y.; Fedorcsak, P.; Huang, C.-M.; Li, C.J.; Vågbø, C.B.; Shi, Y.; Wang, W.-L.; Song, S.-H.; et al. ALKBH5 Is a Mammalian RNA Demethylase that Impacts RNA Metabolism and Mouse Fertility. Mol. Cell 2013, 49, 18–29. [Google Scholar] [CrossRef][Green Version]
- Li, Y.; Wang, X.; Li, C.; Hu, S.; Yu, J.; Song, S. Transcriptome-wide N⁶-methyladenosine profiling of rice callus and leaf reveals the presence of tissue-specific competitors involved in selective mRNA modification. RNA Biol. 2014, 11, 1180–1188. [Google Scholar] [CrossRef][Green Version]
- Luo, G.-Z.; MacQueen, A.; Zheng, G.; Duan, H.; Dore, L.C.; Lu, Z.; Liu, J.; Chen, K.; Jia, G.; Bergelson, J.; et al. Unique features of the m6A methylome in Arabidopsis thaliana. Nat. Commun. 2014, 5, 5630. [Google Scholar] [CrossRef][Green Version]
- Jia, G.; Fu, Y.; Zhao, X.; Dai, Q.; Zheng, G.; Yang, Y.; Yi, C.; Lindahl, T.; Pan, T.; Yang, Y.-G.; et al. N6-Methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat. Chem. Biol. 2011, 7, 885–887. [Google Scholar] [CrossRef]
- Luo, S.; Tong, L. Molecular basis for the recognition of methylated adenines in RNA by the eukaryotic YTH domain. Proc. Natl. Acad. Sci. USA 2014, 111, 13834–13839. [Google Scholar] [CrossRef][Green Version]
- Ping, X.-L.; Sun, B.-F.; Wang, L.; Xiao, W.; Yang, X.; Wang, W.-J.; Adhikari, S.; Shi, Y.; Lv, Y.; Chen, Y.-S.; et al. Mammalian WTAP is a regulatory subunit of the RNA N6-methyladenosine methyltransferase. Cell Res. 2014, 24, 177–189. [Google Scholar] [CrossRef][Green Version]
- Xu, C.; Wang, X.; Liu, K.; A Roundtree, I.; Tempel, W.; Li, Y.; Lu, Z.; He, C.; Min, J. Structural basis for selective binding of m6A RNA by the YTHDC1 YTH domain. Nat. Chem. Biol. 2014, 10, 927–929. [Google Scholar] [CrossRef]
- Hu, J.; Manduzio, S.; Kang, H. Epitranscriptomic RNA Methylation in Plant Development and Abiotic Stress Responses. Front. Plant Sci. 2019, 10, 500. [Google Scholar] [CrossRef][Green Version]
- Shen, L.; Liang, Z.; Wong, C.E.; Yu, H. Messenger RNA Modifications in Plants. Trends Plant Sci. 2019, 24, 328–341. [Google Scholar] [CrossRef]
- Robinson, M.; Shah, P.; Cui, Y.-H.; He, Y.-Y. The Role of Dynamic m6A RNA Methylation in Photobiology. Photochem. Photobiol. 2018, 95, 95–104. [Google Scholar] [CrossRef][Green Version]
- Vandivier, L.E.; Gregory, B.D. New insights into the plant epitranscriptome. J. Exp. Bot. 2018, 69, 4659–4665. [Google Scholar] [CrossRef][Green Version]
- Hofmann, N.R. Epitranscriptomics and Flowering: mRNA Methylation/Demethylation Regulates Flowering Time. Plant Cell 2017, 29, 2949–2950. [Google Scholar] [CrossRef][Green Version]
- Arribas-Hernández, L.; Brodersen, P. Occurrence and Functions of m6A and Other Covalent Modifications in Plant mRNA. Plant Physiol. 2019, 182, 79–96. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Yue, Y.; Liu, J.; Cui, X.; Cao, J.; Luo, G.; Zhang, Z.; Cheng, T.; Gao, M.; Shu, X.; Ma, H.; et al. VIRMA mediates preferential m6A mRNA methylation in 3′UTR and near stop codon and associates with alternative polyadenylation. Cell Discov. 2018, 4, 1–17. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Růžička, K.; Zhang, M.; Campilho, A.; Bodi, Z.; Kashif, M.; Saleh, M.; Eeckhout, D.; El-Showk, S.; Li, H.; Zhong, S.; et al. Identification of factors required for m6 A mRNA methylation in Arabidopsis reveals a role for the conserved E3 ubiquitin ligase HAKAI. New Phytol. 2017, 215, 157–172. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Alemu, E.A.; He, C.; Klungland, A. ALKBHs-facilitated RNA modifications and de-modifications. DNA Repair 2016, 44, 87–91. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Li, D.; Zhang, H.; Hong, Y.; Huang, L.; Li, X.; Zhang, Y.; Ouyang, Z.; Song, F. Genome-Wide Identification, Biochemical Characterization, and Expression Analyses of the YTH Domain-Containing RNA-Binding Protein Family in Arabidopsis and Rice. Plant Mol. Biol. Rep. 2014, 32, 1169–1186. [Google Scholar] [CrossRef]
- Barak, S.; Tobin, E.M.; Green, R.M.; Andronis, C.; Sugano, S. All in good time: The Arabidopsis circadian clock. Trends Plant Sci. 2000, 5, 517–522. [Google Scholar] [CrossRef]
- Harmer, S.L. The Circadian System in Higher Plants. Annu. Rev. Plant Biol. 2009, 60, 357–377. [Google Scholar] [CrossRef][Green Version]
- Dodd, A.N.; Salathia, N.; Hall, A.; Kévei, E.; Tóth, R.; Nagy, F.; Hibberd, J.M.; Millar, A.J.; Webb, A.A.R. Plant Circadian Clocks Increase Photosynthesis, Growth, Survival, and Competitive Advantage. Science 2005, 309, 630–633. [Google Scholar] [CrossRef][Green Version]
- Michael, T.P.; Salomé, P.A.; Yu, H.J.; Spencer, T.R.; Sharp, E.L.; McPeek, M.A.; Alonso, J.M.; Ecker, J.R.; McClung, C.R. Enhanced Fitness Conferred by Naturally Occurring Variation in the Circadian Clock. Science 2003, 302, 1049–1053. [Google Scholar] [CrossRef]
- Hastings, M.H. m6A mRNA Methylation: A New Circadian Pacesetter. Cell 2013, 155, 740–741. [Google Scholar] [CrossRef][Green Version]
- Fustin, J.-M.; Ye, S.; Rakers, C.; Kaneko, K.; Fukumoto, K.; Yamano, M.; Versteven, M.; Grünewald, E.; Cargill, S.J.; Tamai, T.K.; et al. Methylation deficiency disrupts biological rhythms from bacteria to humans. Commun. Biol. 2020, 3, 1–14. [Google Scholar] [CrossRef]
- Mateos, J.L.; De Leone, M.J.; Torchio, J.; Reichel, M.; Staiger, D. Beyond Transcription: Fine-Tuning of Circadian Timekeeping by Post-Transcriptional Regulation. Genes 2018, 9, 616. [Google Scholar] [CrossRef][Green Version]
- Chen, Z.J.; Mas, P. Interactive roles of chromatin regulation and circadian clock function in plants. Genome Biol. 2019, 20, 62. [Google Scholar] [CrossRef][Green Version]
- Romanowski, A.; Yanovsky, M.J. Circadian rhythms and post-transcriptional regulation in higher plants. Front. Plant Sci. 2015, 6, 437. [Google Scholar] [CrossRef][Green Version]
- Nolte, C.; Staiger, D. RNA around the clock—Regulation at the RNA level in biological timing. Front. Plant Sci. 2015, 6, 311. [Google Scholar] [CrossRef][Green Version]
- Parker, M.T.; Knop, K.; Sherwood, A.V.; Schurch, N.J.; MacKinnon, K.; Gould, P.D.; Hall, A.J.; Barton, G.J.; Simpson, G.G. Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m6A modification. eLife 2020, 9. [Google Scholar] [CrossRef]
- Kim, J.; Kim, Y.; Yeom, M.; Kim, J.-H.; Gil Nam, H. FIONA1 Is Essential for Regulating Period Length in the Arabidopsis Circadian Clock. Plant Cell 2008, 20, 307–319. [Google Scholar] [CrossRef][Green Version]
- Fustin, J.-M.; Doi, M.; Yamaguchi, Y.; Hida, H.; Nishimura, S.; Yoshida, M.; Isagawa, T.; Morioka, M.S.; Kakeya, H.; Manabe, I.; et al. RNA-Methylation-Dependent RNA Processing Controls the Speed of the Circadian Clock. Cell 2013, 155, 793–806. [Google Scholar] [CrossRef][Green Version]
- Fustin, J.-M.; Kojima, R.; Itoh, K.; Chang, H.-Y.; Ye, S.; Zhuang, B.; Oji, A.; Gibo, S.; Narasimamurthy, R.; Virshup, D.; et al. Two Ck1δ transcripts regulated by m6A methylation code for two antagonistic kinases in the control of the circadian clock. Proc. Natl. Acad. Sci. USA 2018, 115, 5980–5985. [Google Scholar] [CrossRef][Green Version]
- Wang, C.-Y.; Yeh, J.-K.; Shie, S.-S.; Hsieh, I.-C.; Wen, M.-S. Circadian rhythm of RNA N6-methyladenosine and the role of cryptochrome. Biochem. Biophys. Res. Commun. 2015, 465, 88–94. [Google Scholar] [CrossRef] [PubMed]
- Olsen, J.L.; Rouzé, P.; Verhelst, B.; Lin, Y.-C.; Bayer, T.; Collen, J.; Dattolo, E.; De Paoli, E.; Dittami, S.M.; Maumus, F.; et al. The genome of the seagrass Zostera marina reveals angiosperm adaptation to the sea. Nat. Cell Biol. 2016, 530, 331–335. [Google Scholar] [CrossRef][Green Version]
- Costanza, R.; De Groot, R.; Sutton, P.; Van Der Ploeg, S.; Anderson, S.J.; Kubiszewski, I.; Farber, S.; Turner, R.K. Changes in the global value of ecosystem services. Glob. Environ. Chang. 2014, 26, 152–158. [Google Scholar] [CrossRef]
- Golicz, A.A.; Schliep, M.; Lee, H.; Larkum, A.W.; Dolferus, R.; Batley, J.; Chan, C.K.; Sablok, G.; Ralph, P.J.; Edwards, D. Genome-wide survey of the seagrass Zostera muelleri suggests modification of the ethylene signalling network. J. Exp. Bot. 2015, 66, 1489–1498. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Lee, H.; Golicz, A.A.; Bayer, P.E.; Jiao, Y.; Tang, H.; Paterson, A.H.; Sablok, G.; Krishnaraj, R.; Chan, C.K.; Batley, J.; et al. The Genome of a Southern Hemisphere Seagrass Species (Zostera muelleri). Plant Physiol. 2016, 172, 272–283. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Wissler, L.; Codoñer, F.M.; Gu, J.; Reusch, T.B.H.; Olsen, J.L.; Procaccini, G.; Bornberg-Bauer, E. Back to the sea twice: Identifying candidate plant genes for molecular evolution to marine life. BMC Evol. Biol. 2011, 11, 8. [Google Scholar] [CrossRef][Green Version]
- Dattolo, E.; D’Esposito, D.; Lauritano, C.; Ruocco, M.; Procaccini, G. Circadian Fluctuation of Gene Expression along a Bathymetric Cline in the Marine Angiosperm Posidonia oceanica. Available online: https://peerj.com/preprints/1058/ (accessed on 15 March 2020).
- Procaccini, G.; Ruocco, M.; Marín-Guirao, L.; Dattolo, E.; Brunet, C.; D’Esposito, D.; Lauritano, C.; Mazzuca, S.; Serra, I.A.; Bernardo, L.; et al. Depth-specific fluctuations of gene expression and protein abundance modulate the photophysiology in the seagrass Posidonia oceanica. Sci. Rep. 2017, 7, srep42890. [Google Scholar] [CrossRef]
- Ruocco, M.; Musacchia, F.; Olivé, I.; Costa, M.M.; Barrote, I.; Santos, R.; Sanges, R.; Procaccini, G.; Silva, J. Genomewide transcriptional reprogramming in the seagrass Cymodocea nodosa under experimental ocean acidification. Mol. Ecol. 2017, 26, 4241–4259. [Google Scholar] [CrossRef]
- Duan, H.-C.; Wei, L.-H.; Zhang, C.; Wang, Y.; Chen, L.; Lu, Z.; Chen, P.R.; He, C.; Jia, G. ALKBH10B Is an RNA N6-Methyladenosine Demethylase Affecting Arabidopsis Floral Transition. Plant Cell 2017, 29, 2995–3011. [Google Scholar] [CrossRef][Green Version]
- Martínez-Pérez, M.; Aparicio, F.; López-Gresa, M.P.; Bellés, J.M.; Sánchez-Navarro, J.A.; Pallás, V. Arabidopsis m6A demethylase activity modulates viral infection of a plant virus and the m6A abundance in its genomic RNAs. Proc. Natl. Acad. Sci. USA 2017, 114, 10755–10760. [Google Scholar] [CrossRef][Green Version]
- Shen, L.; Liang, Z.; Gu, X.; Chen, Y.; Teo, Z.W.N.; Hou, X.; Cai, W.M.; Dedon, P.C.; Liu, L.; Yu, H. N6-Methyladenosine RNA Modification Regulates Shoot Stem Cell Fate in Arabidopsis. Dev. Cell 2016, 38, 186–200. [Google Scholar] [CrossRef][Green Version]
- Larkum, A.W.; Orth, R.J.; Duarte, C.M. Seagrasses: Biology, Ecology and Conservation; Springer: Dordrecht, The Netherlands, 2006. [Google Scholar]
- Swarbreck, D.; Wilks, C.; Lamesch, P.; Berardini, T.Z.; Garcia-Hernandez, M.; Foerster, H.; Li, D.; Meyer, T.; Muller, R.; Ploetz, L.; et al. The Arabidopsis Information Resource (TAIR): Gene structure and function annotation. Nucleic Acids Res. 2007, 36, D1009–D1014. [Google Scholar] [CrossRef] [PubMed]
- Ouyang, S.; Zhu, W.; Hamilton, J.; Lin, H.; Campbell, M.; Childs, K.; Thibaud-Nissen, F.; Malek, R.L.; Lee, Y.; Zheng, L.; et al. The TIGR Rice Genome Annotation Resource: Improvements and new features. Nucleic Acids Res. 2007, 35, D883–D887. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Ambrosino, L.; Chiusano, M.L. Transcriptologs: A Transcriptome-Based Approach to Predict Orthology Relationships. Bioinform. Biol. Insights 2017, 11, 1177932217690136. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Ambrosino, L.; Ruggieri, V.; Bostan, H.; Miralto, M.; Vitulo, N.; Zouine, M.; Barone, A.; Bouzayen, M.; Frusciante, L.; Pezzotti, M.; et al. Multilevel comparative bioinformatics to investigate evolutionary relationships and specificities in gene annotations: An example for tomato and grapevine. BMC Bioinform. 2018, 19, 435. [Google Scholar] [CrossRef] [PubMed]
- Camacho, C.; Coulouris, G.; Avagyan, V.; Ma, N.; Papadopoulos, J.S.; Bealer, K.; Madden, T.L. BLAST+: Architecture and applications. BMC Bioinform. 2009, 10, 1–9. [Google Scholar] [CrossRef][Green Version]
- Hagberg, A.; Swart, P.; Chult, D.S. Exploring Network Structure, Dynamics, and Function Using NetworkX; Los Alamos National Lab. (LANL): Los Alamos, NM, USA, 2008. Available online: https://www.osti.gov/biblio/960616-exploring-network-structure-dynamics-function-using-networkx (accessed on 15 March 2020).
- Rosenfeld, J.A.; DeSalle, R. E value cutoff and eukaryotic genome content phylogenetics. Mol. Phylogenet. Evol. 2012, 63, 342–350. [Google Scholar] [CrossRef] [PubMed]
- Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
- Apweiler, R. UniProt: The Universal Protein knowledgebase. Nucleic Acids Res. 2004, 32, 115D–119D. [Google Scholar] [CrossRef]
- Huang, X. CAP3: A DNA Sequence Assembly Program. Genome Res. 1999, 9, 868–877. [Google Scholar] [CrossRef][Green Version]
- Sievers, F.; Higgins, D.G. Clustal Omega, Accurate Alignment of Very Large Numbers of Sequences. In Recent Results in Cancer Research; Springer Science and Business Media LLC: Berlin, Germany, 2013; Volume 1079, pp. 105–116. [Google Scholar]
- Katoh, K.; Standley, D.M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 2013, 30, 772–780. [Google Scholar] [CrossRef][Green Version]
- Capella-Gutiérrez, S.; Silla-Martínez, J.M.; Gabaldón, T. trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 2009, 25, 1972–1973. [Google Scholar] [CrossRef] [PubMed]
- Kalyaanamoorthy, S.; Minh, B.Q.; Wong, T.K.F.; Von Haeseler, A.; Jermiin, L.S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 2017, 14, 587–589. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Nguyen, L.-T.; Schmidt, H.A.; Von Haeseler, A.; Minh, B.Q. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Mol. Biol. Evol. 2014, 32, 268–274. [Google Scholar] [CrossRef] [PubMed]
- Price, M.N.; Dehal, P.S.; Arkin, A.P. FastTree 2—Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE 2010, 5, e9490. [Google Scholar] [CrossRef] [PubMed]
- Letunic, I.; Bork, P. Interactive tree of life (iTOL) v3: An online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016, 44, W242–W245. [Google Scholar] [CrossRef]
- Koressaar, T.; Remm, M. Enhancements and modifications of primer design program Primer3. Bioinformatics 2007, 23, 1289–1291. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Untergasser, A.; Cutcutache, I.; Koressaar, T.; Ye, J.; Faircloth, B.C.; Remm, M.; Rozen, S. Primer3—New capabilities and interfaces. Nucleic Acids Res. 2012, 40, e115. [Google Scholar] [CrossRef][Green Version]
- Rasmusson, L.M.; Lauritano, C.; Procaccini, G.; Gullström, M.; Buapet, P.; Björk, M. Respiratory oxygen consumption in the seagrass Zostera marina varies on a diel basis and is partly affected by light. Mar. Biol. 2017, 164, 140. [Google Scholar] [CrossRef][Green Version]
- Ransbotyn, V.; Reusch, T.B.H. Housekeeping gene selection for quantitative real-time PCR assays in the seagrass Zostera marina subjected to heat stress. Limnol. Oceanogr. Methods 2006, 4, 367–373. [Google Scholar] [CrossRef]
- Bergmann, N.; Winters, G.; Rauch, G.; Eizaguirre, C.; Gu, J.; Nelle, P.; Fricke, B.; Reusch, T.B.H. Population-specificity of heat stress gene induction in northern and southern eelgrass Zostera marina populations under simulated global warming. Mol. Ecol. 2010, 19, 2870–2883. [Google Scholar] [CrossRef]
- Winters, G.; Nelle, P.; Fricke, B.; Rauch, G.; Reusch, T.B.H. Effects of a simulated heat wave on photophysiology and gene expression of high- and low-latitude populations of Zostera marina. Mar. Ecol. Prog. Ser. 2011, 435, 83–95. [Google Scholar] [CrossRef][Green Version]
- Olivé, I.; Silva, J.; Lauritano, C.; Costa, M.M.; Ruocco, M.; Procaccini, G.; Santos, R. Linking gene expression to productivity to unravel long- and short-term responses of seagrasses exposed to CO2 in volcanic vents. Sci. Rep. 2017, 7, 42278. [Google Scholar] [CrossRef][Green Version]
- Ruocco, M.; Marín-Guirao, L.; Procaccini, G. Within- and among-leaf variations in photo-physiological functions, gene expression and DNA methylation patterns in the large-sized seagrass Posidonia oceanica. Mar. Biol. 2019, 166, 24. [Google Scholar] [CrossRef]
- Clarke, K.; Gorley, R. Getting Started with PRIMER v7. PRIMER-E; Plymouth Marine Laboratory: Plymouth, UK, 2006; Available online: http://updates.primer-e.com/primer7/manuals/Getting_started_with_PRIMER_7.pdf (accessed on 15 March 2020).
- Jończyk, M.; Sobkowiak, A.; Siedlecki, P.; Biecek, P.; Trzcinska-Danielewicz, J.; Tiuryn, J.; Fronk, J.; Sowiński, P. Rhythmic Diel Pattern of Gene Expression in Juvenile Maize Leaf. PLoS ONE 2011, 6, e23628. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Müller, L.M.; Von Korff, M.; Davis, S.J. Connections between circadian clocks and carbon metabolism reveal species-specific effects on growth control. J. Exp. Bot. 2014, 65, 2915–2923. [Google Scholar] [CrossRef] [PubMed][Green Version]
Type | Gene Name | Arabidopsis thaliana | Oryza sativa | Zostera marina | Cymodocea nodosa | |||||
---|---|---|---|---|---|---|---|---|---|---|
N° Proteins | ID | N° Proteins | ID | N° Proteins | ID | N° Proteins | ID | Network | ||
Writers | MTA | 1 | AT4G10760.1 | 1 | NP_001047707.1 | 1 | Zosma246g00180 | 2 | c47690_g5_i2 | NET_3623 |
c47690_g5_i1 | NET_3623 | |||||||||
Writers | MTB | 2 | AT4G09980.2 | 3 | NP_001064723.2 | 1 | Zosma418g00030 | 4 | c45450_g1_i3 | NET_3623 |
AT4G09980.1 | NP_001048963.2 | c45450_g1_i5 | NET_3623 | |||||||
NP_001042682.1 | c45450_g1_i1 | NET_3623 | ||||||||
c45450_g1_i2 | NET_3623 | |||||||||
Writers | FIP37 | 1 | AT3G54170.1 | 1 | NP_001057630.2 | 6 | Zosma8185g00010 | 7 | c43887_g1_i12 | NET_2064 |
Zosma355g00070 | c43887_g1_i2 | NET_2064 | ||||||||
Zosma33g00670 | c43887_g1_i6 | NET_2064 | ||||||||
Zosma3g02080 | c43887_g1_i7 | NET_2064 | ||||||||
Zosma138g00010 | c43887_g1_i5 | NET_2064 | ||||||||
Zosma105g00210 | c43887_g1_i9 | NET_2064 | ||||||||
c43887_g1_i3 | NET_2064 | |||||||||
Writers | VIRILIZER | 2 | AT3G05680.1 | - | - | 1 | Zosma170g00400 | 3 | c47165_g1_i3 | NET_1791 |
AT3G05680.2 | c47165_g1_i8 | NET_1791 | ||||||||
c47165_g1_i5 | NET_1791 | |||||||||
Writers | HAKAI | 1 | AT5G01160.1 | 1 | NP_001064945.2 | 1 | Zosma37g01170 | 1 | c45117_g2_i2 | NET_8223 |
Erasers | ALKBH9B | 10 | AT4G36090.2 | 1 | NP_001056738.1 | 1 | Zosma87g00220 | 13 | c41578_g1_i1 | NET_160 |
AT1G48980.1 | c41578_g1_i2 | NET_160 | ||||||||
AT4G36090.3 | c41578_g1_i3 | NET_160 | ||||||||
AT1G48980.4 | c41578_g1_i4 | NET_160 | ||||||||
AT1G48980.2 | c41578_g1_i5 | NET_160 | ||||||||
AT2G17970.2 | c41578_g1_i6 | NET_160 | ||||||||
AT2G17970.3 | c41578_g1_i8 | NET_160 | ||||||||
AT2G17970.1 | c46801_g7_i1 | NET_160 | ||||||||
AT1G48980.3 | c46801_g7_i2 | NET_160 | ||||||||
AT4G36090.1 | c46801_g7_i3 | NET_160 | ||||||||
c46801_g7_i4 | NET_160 | |||||||||
c46801_g7_i7 | NET_160 | |||||||||
c46801_g7_i8 | NET_160 | |||||||||
Erasers | ALKBH10B | 4 | AT1G14710.2 | 2 | NP_001064055.1 | 3 | Zosma89g00160 | 20 | c45808_g2_i10 | NET_160 |
AT1G14710.1 | NP_001049502.1 | Zosma25g00610 | c45808_g2_i3 | NET_160 | ||||||
AT4G02940.1 | Zosma2g02460 | c45808_g2_i4 | NET_160 | |||||||
AT2G48080.1 | c45808_g2_i6 | NET_160 | ||||||||
c45808_g2_i8 | NET_160 | |||||||||
c45808_g2_i9 | NET_160 | |||||||||
c45808_g3_i2 | NET_160 | |||||||||
c45808_g3_i3 | NET_160 | |||||||||
c45808_g3_i5 | NET_160 | |||||||||
c45808_g3_i6 | NET_160 | |||||||||
c45808_g3_i7 | NET_160 | |||||||||
c45808_g3_i8 | NET_160 | |||||||||
c45808_g3_i4 | NET_160 | |||||||||
c46051_g1_i1 | NET_160 | |||||||||
c46051_g1_i11 | NET_160 | |||||||||
c46051_g1_i2 | NET_160 | |||||||||
c46051_g1_i3 | NET_160 | |||||||||
c46051_g1_i4 | NET_160 | |||||||||
c46051_g1_i5 | NET_160 | |||||||||
c46718_g4_i2 | NET_160 |
Gene Acronym | Protein | Species | Primer Sequences 5′→3′ | S | E | R2 | ID | Network |
---|---|---|---|---|---|---|---|---|
MTA | N6-adenosine-methyltransferase MT-A70-like | C. nodosa | F: GGGGCAGTTTGGGGTTATTA R: GCTCGTCCAGTTACCCAAAG | 150 | 100% | 0.99 | c47690_g5_i2 | NET_3623 |
MTB | N6-adenosine-methyltransferase non-catalytic subunit MTB | C. nodosa | F: CCTTGGGAGGAGTATGTCCA R: GCAAACTTGGAGTGGCATTT | 244 | 100% | 0.99 | c45450_g1_i5 | NET_3623 |
MTA | N6-adenosine-methyltransferase MT-A70-like | Z. marina | F: TTATGGCAGATCCACCTTGG R: GCTCGTCCAGTTACCCAAAG | 132 | 100% | 0.99 | Zosma246g00180 | NET_3623 |
MTB | N6-adenosine-methyltransferase non-catalytic subunit MTB | Z. marina | F: CTCCATAGAGCTCCTGGTTCTG R: ACACTGCCTACCCTGCTCAA | 150 | 100% | 0.98 | Zosma418g00030 | NET_3623 |
ALKBH9B | RNA demethylase ALKBH9B | C. nodosa | F: ATCGGTCAGTTGGGATGAAG R: AACTCGTACACACAATTCAC | 225 | 100% | 0.99 | c46801_g7_i8 | NET_160 |
ALKBH9B | RNA demethylase ALKBH9B | Z. marina | F: ACGACTTTGTCCGACCCTTC R: GAACACCTGGGATGCAATGC | 189 | 90% | 0.99 | Zosma87g00220 | NET_160 |
Table PERMANOVAs | |||||||
---|---|---|---|---|---|---|---|
Pseudo- | Unique | ||||||
Source | df | SS | MS | F | P(perm) | perms | Pairwise Tests (PMC) |
Inter-specific analysis | |||||||
Species (Sp) | 1 | 252.64 | 252.64 | 172.14 | 0.0001 | 9919 | Z. marina: Sunrise ≠ Sunset; Sunrise ≠ dusk; Dawn = Sunset (p = 0.06); Sunrise = Solar noon (p = 0.08) C. nodosa: Sunrise ≠ Solar noon; Sunrise ≠ Sunset; Midnight = Solar noon (p = 0.06); Midnight = Sunset (p = 0.06) |
Time (Ti) | 5 | 11.525 | 2.305 | 1.5705 | 0.1633 | 9942 | |
Sp × Ti | 5 | 28.621 | 5.7242 | 3.9002 | 0.0038 | 9941 | |
Res | 24 | 35.224 | 1.4677 | ||||
Total | 35 | 328.01 | |||||
Intra-specific analysis | |||||||
Latitude (La) | 1 | 7.0596 | 7.0596 | 3.9333 | 0.0328 | 9963 | Z. marina Faro: Sunrise ≠ Sunset; Sunrise = Solar noon (p = 0.08) Z. marina Tjärnö: Midnight = Sunset (p = 0.08) |
Time (Ti) | 3 | 22.844 | 7.6147 | 4.2426 | 0.0124 | 9956 | |
La × Ti | 3 | 6.1978 | 2.0659 | 1.1511 | 0.3495 | 9946 | |
Res | 16 | 28.717 | 1.7948 | ||||
Total | 23 | 64.819 | |||||
Two-Way ANOVAs | |||||||||
---|---|---|---|---|---|---|---|---|---|
Effect | df | Interspecific Analysis | Effect | df | Intraspecific Analysis | ||||
MS | F | p | MS | F | p | ||||
MTA | MTA | ||||||||
Species (Sp) | 1 | 173.420 | 289.482 | 0.000 | Latitude (La) | 1 | 0.086 | 0.111 | 0.743 |
Time (Ti) | 5 | 1.373 | 2.292 | 0.077 | Time (Ti) | 3 | 3.737 | 4.841 | 0.014 |
Sp × Ti | 5 | 2.557 | 4.269 | 0.006 | La × Ti | 3 | 1.111 | 1.439 | 0.269 |
Error | 24 | 0.599 | Error | 16 | 0.772 | ||||
MTB | MTB | ||||||||
Species (Sp) | 1 | 73.936 | 135.660 | 0.000 | Latitude (La) | 1 | 0.584 | 0.812 | 0.381 |
Time (Ti) | 5 | 0.593 | 1.089 | 0.392 | Time (Ti) | 3 | 3.077 | 4.274 | 0.021 |
Sp × Ti | 5 | 1.534 | 2.814 | 0.039 | La × Ti | 3 | 0.594 | 0.825 | 0.499 |
Error | 24 | 0.545 | Error | 16 | 0.720 | ||||
ALKBH9B | ALKBH9B | ||||||||
Species (Sp) | 1 | 5.2886 | 16.343 | 0.000 | Latitude (La) | 1 | 6.389 | 21.081 | 0.000 |
Time (Ti) | 5 | 0.3386 | 1.046 | 0.414 | Time (Ti) | 3 | 0.801 | 2.642 | 0.085 |
Sp × Ti | 5 | 1.6332 | 5.047 | 0.003 | La × Ti | 3 | 0.361 | 1.192 | 0.344 |
Error | 24 | 0.3236 | Error | 16 | 0.303 |
Two-Way ANOVAs | |||||||||
---|---|---|---|---|---|---|---|---|---|
Effect | df | Interspecific Analysis | Effect | df | Intraspecific Analysis | ||||
MS | F | p | MS | F | p | ||||
m6A | m6A | ||||||||
Species (Sp) | 1 | 0.001 | 17.763 | 0.000 | Latitude (La) | 1 | 0.305 | 2.297 | 0.149 |
Time (Ti) | 5 | 0.000 | 1.274 | 0.309 | Time (Ti) | 3 | 0.124 | 0.936 | 0.446 |
Sp × Ti | 5 | 0.000 | 1.851 | 0.143 | La × Ti | 3 | 0.068 | 0.511 | 0.680 |
Error | 23 | 0.000 | Error | 16 | 0.133 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ruocco, M.; Ambrosino, L.; Jahnke, M.; Chiusano, M.L.; Barrote, I.; Procaccini, G.; Silva, J.; Dattolo, E. m6A RNA Methylation in Marine Plants: First Insights and Relevance for Biological Rhythms. Int. J. Mol. Sci. 2020, 21, 7508. https://doi.org/10.3390/ijms21207508
Ruocco M, Ambrosino L, Jahnke M, Chiusano ML, Barrote I, Procaccini G, Silva J, Dattolo E. m6A RNA Methylation in Marine Plants: First Insights and Relevance for Biological Rhythms. International Journal of Molecular Sciences. 2020; 21(20):7508. https://doi.org/10.3390/ijms21207508
Chicago/Turabian StyleRuocco, Miriam, Luca Ambrosino, Marlene Jahnke, Maria Luisa Chiusano, Isabel Barrote, Gabriele Procaccini, João Silva, and Emanuela Dattolo. 2020. "m6A RNA Methylation in Marine Plants: First Insights and Relevance for Biological Rhythms" International Journal of Molecular Sciences 21, no. 20: 7508. https://doi.org/10.3390/ijms21207508