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Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon

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Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon

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dc.contributor.author Sanz-Carbonell, Alejandro es_ES
dc.contributor.author Marques Romero, Mª Carmen es_ES
dc.contributor.author Bustamante-González, Antonio Javier es_ES
dc.contributor.author Fares Riaño, Mario Ali es_ES
dc.contributor.author Rodrigo Tarrega, Guillermo es_ES
dc.contributor.author Gomez, Gustavo Germán es_ES
dc.date.accessioned 2020-11-07T04:32:43Z
dc.date.available 2020-11-07T04:32:43Z
dc.date.issued 2019-02-18 es_ES
dc.identifier.issn 1471-2229 es_ES
dc.identifier.uri http://hdl.handle.net/10251/154391
dc.description.abstract [EN] Background: MiRNAs have emerged as key regulators of stress response in plants, suggesting their potential as candidates for knock-in/out to improve stress tolerance in agricultural crops. Although diverse assays have been performed, systematic and detailed studies of miRNA expression and function during exposure to multiple environments in crops are limited. Results: Here, we present such pioneering analysis in melon plants in response to seven biotic and abiotic stress conditions. Deep-sequencing and computational approaches have identified twenty-four known miRNAs whose expression was significantly altered under at least one stress condition, observing that down-regulation was preponderant. Additionally, miRNA function was characterized by high scale degradome assays and quantitative RNA measurements over the intended target mRNAs, providing mechanistic insight. Clustering analysis provided evidence that eight miRNAs showed a broad response range under the stress conditions analyzed, whereas another eight miRNAs displayed a narrow response range. Transcription factors were predominantly targeted by stressresponsive miRNAs in melon. Furthermore, our results show that the miRNAs that are down-regulated upon stress predominantly have as targets genes that are known to participate in the stress response by the plant, whereas the miRNAs that are up-regulated control genes linked to development. Conclusion: Altogether, this high-resolution analysis of miRNA-target interactions, combining experimental and computational work, Illustrates the close interplay between miRNAs and the response to diverse environmental conditions, in melon. es_ES
dc.description.sponsorship The authors thank Dr. A. Monforte for providing melon seeds and Dra. B. Pico (Cucurbits Group - COMAV) for providing melon seeds and Monosporascus isolate respectively. This work was supported by grants AGL2016-79825-R, BIO2014-61826-EXP (GG), and BFU2015-66894-P (GR) from the Spanish Ministry of Economy and Competitiveness (co-supported by FEDER). The funders had no role in the experiment design, data analysis, decision to publish, or preparation of the manuscript. es_ES
dc.language Inglés es_ES
dc.publisher Springer (Biomed Central Ltd.) es_ES
dc.relation.ispartof BMC Plant Biology es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Agriculture es_ES
dc.subject Climatic change es_ES
dc.subject Cucurbits es_ES
dc.subject Non-coding RNAs es_ES
dc.subject RNA silencing es_ES
dc.subject Stress tolerance es_ES
dc.title Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1186/s12870-019-1679-0 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//AGL2013-47886-R/ES/CARACTERIZACION DE LA RESPUESTA A ESTRES MULTIPLE REGULADA POR NCRNAS EN CUCURBITACEAS. BASES PARA EL DISEÑO DE ESTRATEGIAS INTEGRALES PARA LA PROTECCION DE CULTIVOS¿/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//AGL2016-79825-R/ES/VALIDACION FUNCIONAL DE LAS REDES DE SNCRNAS QUE REGULAN LA REPUESTA A ESTRES EN MELON. ANALISIS DE SU POTENCIAL COMO FUENTE DE TOLERANCIA A CONDICIONES AMBIENTALES ADVERSAS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//BIO2014-61826-EXP/ES/OPTIMIZACION PARA USO A ESCALA INDUSTRIAL DE UN SISTEMA PARA LA EXPRESION SELECTIVA DE COMPUESTOS HETEROLOGOS EN CLOROPLASTOS MEDIADO POR NON-CODING RNAS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//BFU2015-66894-P /ES/MODELADO, DISEÑO DE NOVO E INGENIERIA DE INTERRUPTORES DE RNA QUE RESPONDEN A SEÑALES GENETICAS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario Mixto de Biología Molecular y Celular de Plantas - Institut Universitari Mixt de Biologia Molecular i Cel·lular de Plantes es_ES
dc.description.bibliographicCitation Sanz-Carbonell, A.; Marques Romero, MC.; Bustamante-González, AJ.; Fares Riaño, MA.; Rodrigo Tarrega, G.; Gomez, GG. (2019). Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon. BMC Plant Biology. 1-17. https://doi.org/10.1186/s12870-019-1679-0 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1186/s12870-019-1679-0 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.identifier.pmid 30777009 es_ES
dc.identifier.pmcid PMC6379984 es_ES
dc.relation.pasarela S\379855 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references Zhang B. MicroRNAs: a new target for improving plant tolerance to abiotic stress. J Exp Bot. 2015;66:1749–61. es_ES
dc.description.references Zhu JK. Abiotic stress signaling and responses in plants. Cell. 2016;167:313–24. es_ES
dc.description.references Bielach A, Hrtyan M, Tognetti VB. Plants under stress: involvement of auxin and Cytokinin. Int J Mol Sci. 2017;4(18):7. es_ES
dc.description.references Zarattini M, Forlani G. Toward unveiling the mechanisms for transcriptional regulation of proline biosynthesis in the plant cell response to biotic and abiotic stress conditions. Front Plant Sci. 2017;2(8):927. es_ES
dc.description.references Nolan T, Chen J, Yin Y. Cross-talk of Brassinosteroid signaling in controlling growth and stress responses. Biochem J. 2017;474:2641–61. es_ES
dc.description.references Mittler R. Abiotic stress, the field environment and stress combinations. Trends Plant Sci. 2006;11:15–9. es_ES
dc.description.references Djami-Tchatchou AT, Sanan-Mishra N, Ntushelo K, Dubery IA. Functional roles of microRNAs in Agronomically important plants—potential as targets for crop improvement and protection. Front Plant Sci. 2017;8:378. es_ES
dc.description.references Baxter A, Mittler R, Suzuki N. ROS as key players in plant stress signaling. J Exp Bot. 2014;65:1229–40. es_ES
dc.description.references Golldack D, Li C, Mohan H, Probst N. Tolerance to drought and salt stress in plants: unraveling the signaling networks. Front Plant Sci. 2014;5:151. es_ES
dc.description.references Lee SH, Li HW, Koh KW, Chuang HY, Chen YR, Lin CS, Chan MT. MSRB7 reverses oxidation of GSTF2/3 to confer tolerance of Arabidopsis thaliana to oxidative stress. J Exp Bot. 2014;65:5049–62. es_ES
dc.description.references Carrera J, Rodrigo G, Jaramillo A, Elena SF. Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome Biol. 2009;10(9):R96. es_ES
dc.description.references Shriram V, Kumar V, Devarumath RM, Khare TS, Wani SH. MicroRNAs as potential targets for abiotic stress tolerance in plants. Front Plant Sci. 2016;7:817. es_ES
dc.description.references Sunkar R, Chinnusamy V, Zhu J, Zhu JH. Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends Plant Sci. 2007;12:301–9. es_ES
dc.description.references Kumar R. Role of microRNAs in biotic and abiotic stress responses in crop plants. Appl Biochem Biotechnology. 2014;174:93–115. es_ES
dc.description.references Reis RS, Eamens AL, Waterhouse PM. Missing pieces in the puzzle of plant MicroRNAs. Trends Plant Sci. 2015;20:721–8. es_ES
dc.description.references Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–97. es_ES
dc.description.references Borges F, Martienssen RA. The expanding world of small RNAs in plants. Nat Rev Mol Cell Biol. 2015;16:727–41. es_ES
dc.description.references Axtell MJ, Bartel DP. Antiquity of microRNAs and their targets in land-plants. Plant Cell. 2005;17:1658–73. es_ES
dc.description.references Cuperus JT, Fahlgren N, Carrington JC. Evolution and functional diversification of MIRNA genes. Plant Cell. 2011;23:431–42. es_ES
dc.description.references Cui J, You C, Chen X. The evolution of microRNAs in plants. Current Opinions in Plant Biology. 2016;35:61–7. es_ES
dc.description.references Sunkar R, Li YF, Jagadeeswaran G. Functions of microRNAs in plant stress responses. Trends Plant Sci. 2012;17:196–203. es_ES
dc.description.references Zhang T, Zhao YL, Zhao JH, Wang S, Jin Y, Chen ZQ, Fang YY, Hua CL, Ding SW, Guo HS. Cotton plants export microRNAs to inhibit virulence gene expression in a fungal pathogen. Nature Plants. 2016;2(10):16153. es_ES
dc.description.references Chaloner T, vanKan JA, Grant-Downton R. RNA ‘Information Warfare’ in pathogenic and mutualistic interactions. Trends Plant Sci. 2016;9:738–48. es_ES
dc.description.references Niu D, Wang Z, Wang S, Qiao L Zhao H. Profiling of small RNAs involved in plant-pathogen interactions. Methods Molecular Biology. 2015;1287:61–79. es_ES
dc.description.references Wei S, Wang L, Zhang Y, Huang D. Identification of early response genes to salt stress in roots of melon (Cucumis melo L.) seedlings. Molecular Biology Report. 2013;40:2915–26. es_ES
dc.description.references Clepet C, Joobeur T, Zheng Y, Jublot D, Huang M, Truniger V, et al. Analysis of expressed sequence tags generated from full-length enriched cDNA libraries of melon. BMC Genomics. 2011;12:252. es_ES
dc.description.references González M, Xu M, Esteras C, Roig C, Monforte AJ, Troadec C, et al. Towards a TILLING platform for functional genomics in Piel de Sapo melons. BMC Research Notes. 2011;4:289. es_ES
dc.description.references García MJ. The genome of melon (Cucumis melo L.). Proc Natl Acad Sci U S A. 2012;109:11872–7. es_ES
dc.description.references Pollack FG, Uecker FA. Monosporascus cannonballus: an unusual ascomycete in cantaloupe roots. Mycologia. 1974;66:346–9. es_ES
dc.description.references Kofalvi S, Marcos J, Cañizares MC, Pallas V, Candresse T. Hop stunt viroid (HSVd) sequence variants from Prunus species: evidence for recombination between HSVd isolates. J Gen Virol. 1997;78:3177–86. es_ES
dc.description.references Sattar S, Song Y, Anstead J, Sunkar R, Thompson G. Cucumis melo expression profile during aphid herbivory in a resistant and susceptible interaction. Mol Plant-Microbe Interact. 2012;25:839–48. es_ES
dc.description.references Herranz MC, Navarro JA, Sommen E, Pallas V. Comparative analysis among the small RNA populations of source, sink and conductive tissues in two different plant-virus pathosystems. BMC Genomics. 2015;16:117. es_ES
dc.description.references Jagadeeswaran G, Nimmakayala P, Zheng Y, Gowdu K, Reddy UK, Sunkar R. Characterization of the small RNA component of leaves and fruits from four different cucurbit species. BMC Genomics. 2012;13:329. es_ES
dc.description.references Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42:D68–73. es_ES
dc.description.references Barciszewska-Pacak M, Milanowska K, Knop K, Bielewicz D, Nuc P, Plewka P, et al. Arabidopsis microRNA expression regulation in a wide range of abiotic stress responses. Front Plant Sci. 2015;6:410. es_ES
dc.description.references Zhou L, Liu Y, Liu Z, Kong D, Duan M, Luo L. Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. J Exp Bot. 2010;61:4157–68. es_ES
dc.description.references Samad A, Sajad M, Nazaruddin N, Fauzi I, Murad A, Zainal Z, Ismanizan Ismail I. MicroRNA and transcription factor: key players in plant regulatory network. Front Plant Sci. 2017;8:565. es_ES
dc.description.references Danisman S. TCP transcription factors at the Interface between environmental challenges and the Plant’s growth responses. Front Plant Sci. 2016;7:1930. es_ES
dc.description.references Llave C, Xie Z, Kasschau KD, Carrington JC. Cleavage of scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science. 2002;297:2053–6. es_ES
dc.description.references Gupta OP, Meena NL, Sharma I, et al. Differential regulation of microRNAs in response to osmotic, salt and cold stresses in wheat. Mol Biol Rep. 2014;41:4623. es_ES
dc.description.references Wang M, Wang Q, Zhang B. 2013. Response of miRNAs and their targets to salt and drought stresses in cotton (Gossypium hirsutum ). Gene 30: 26–32. es_ES
dc.description.references Savageau MA. Demand theory of gene regulation. I. Quantitative development of the theory. Genetics. 1998;149:1665–76. es_ES
dc.description.references Negrão S, Schmöckel SM, Tester M. Evaluating physiological responses of plants to salinity stress. Ann Bot. 2017;119:1–11. es_ES
dc.description.references Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004;5(2):101–13. es_ES
dc.description.references Megraw M, Cumbie J, Ivanchenko M, Filichkin S. Small genetic circuits and MicroRNAs: big players in polymerase II transcriptional control in plants. Plant Cell. 2016;28:286–303. es_ES
dc.description.references Wang St, Sun Xl, Hoshino Y, Yu Y, Jia B, et al. 2014. MicroRNA319 Positively Regulates Cold Tolerance by Targeting OsPCF6 and OsTCP21 in Rice (Oryza sativa). PLoS ONE 9(3): e91357. es_ES
dc.description.references Fang Y, Xie K, Xiong L. Conserved miR164-targeted NAC genes regulate drought resistence in rice. J Exp Bot. 2014;65:2119–35. es_ES
dc.description.references Goossens A, de la Fuente N, Forment J, Serrano R, Portillo F. Regulation of yeast H+-ATPase by protein kinases belonging to a family dedicated to activation of plasma membrane transporters. Mol Cell Biol. 2000;20:7654–61. es_ES
dc.description.references Roig C, Fita A, Ríos G, Hammond JP, Nuez F, Picó B. Root transcriptional responses of two melon genotypes with contrasting resistance to Monosporascus cannonballus (Pollack et Uecker) infection. BMC Genomics. 2012;13:601. es_ES
dc.description.references Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal. 2011;17:10–2. es_ES
dc.description.references R Core Team 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3–900051–07-0, URL http://www.R-project.org /. es_ES
dc.description.references Tarazona S, Furió-Tarí P, Turrà D, Di Pietro A, Nueda MJ, Ferrer A, Conesa A. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/bioc package. Nucleic Acids Res. 2015;43:e140. es_ES
dc.description.references Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. es_ES
dc.description.references Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40. es_ES
dc.description.references Czimmerer Z, Hulvely J, Simandi Z, Varallyay E, Havelda Z, Szabo E, Balint BL. A versatile method to design stem-loop primer-based quantitative PCR assays for detecting small regulatory RNA molecules. PLoS One. 2013;8(1):e55168. es_ES
dc.description.references Zhai J, Arikit S, Simon S, Kingham B, Meyers B. Rapid construction of parallel analysis of RNA end (PARE) libraries for Illumina sequencing. Methods. 2014;67:84–90. es_ES
dc.description.references Pink S, Vogel S. 2014. D3NETWORK: Stata module to create network visualizations using D3.js http://EconPapers.repec.org/RePEc:boc:bocode:s457844 . es_ES
dc.description.references Csardi G, Nepusz T. The igraph software package for complex network research. Int J Complex Systems. 2006;1695:1–9. es_ES


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