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

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.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.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/s12870-019-1679-0S117Zhang B. MicroRNAs: a new target for improving plant tolerance to abiotic stress. J Exp Bot. 2015;66:1749–61.Zhu JK. Abiotic stress signaling and responses in plants. Cell. 2016;167:313–24.Bielach A, Hrtyan M, Tognetti VB. Plants under stress: involvement of auxin and Cytokinin. Int J Mol Sci. 2017;4(18):7.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.Nolan T, Chen J, Yin Y. Cross-talk of Brassinosteroid signaling in controlling growth and stress responses. Biochem J. 2017;474:2641–61.Mittler R. Abiotic stress, the field environment and stress combinations. Trends Plant Sci. 2006;11:15–9.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.Baxter A, Mittler R, Suzuki N. ROS as key players in plant stress signaling. J Exp Bot. 2014;65:1229–40.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.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.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.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.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.Kumar R. Role of microRNAs in biotic and abiotic stress responses in crop plants. Appl Biochem Biotechnology. 2014;174:93–115.Reis RS, Eamens AL, Waterhouse PM. Missing pieces in the puzzle of plant MicroRNAs. Trends Plant Sci. 2015;20:721–8.Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–97.Borges F, Martienssen RA. The expanding world of small RNAs in plants. Nat Rev Mol Cell Biol. 2015;16:727–41.Axtell MJ, Bartel DP. Antiquity of microRNAs and their targets in land-plants. Plant Cell. 2005;17:1658–73.Cuperus JT, Fahlgren N, Carrington JC. Evolution and functional diversification of MIRNA genes. Plant Cell. 2011;23:431–42.Cui J, You C, Chen X. The evolution of microRNAs in plants. Current Opinions in Plant Biology. 2016;35:61–7.Sunkar R, Li YF, Jagadeeswaran G. Functions of microRNAs in plant stress responses. Trends Plant Sci. 2012;17:196–203.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.Chaloner T, vanKan JA, Grant-Downton R. RNA ‘Information Warfare’ in pathogenic and mutualistic interactions. Trends Plant Sci. 2016;9:738–48.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.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.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.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.García MJ. The genome of melon (Cucumis melo L.). Proc Natl Acad Sci U S A. 2012;109:11872–7.Pollack FG, Uecker FA. Monosporascus cannonballus: an unusual ascomycete in cantaloupe roots. Mycologia. 1974;66:346–9.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.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.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.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.Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42:D68–73.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.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.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.Danisman S. TCP transcription factors at the Interface between environmental challenges and the Plant’s growth responses. Front Plant Sci. 2016;7:1930.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.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.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.Savageau MA. Demand theory of gene regulation. I. Quantitative development of the theory. Genetics. 1998;149:1665–76.Negrão S, Schmöckel SM, Tester M. Evaluating physiological responses of plants to salinity stress. Ann Bot. 2017;119:1–11.Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004;5(2):101–13.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.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.Fang Y, Xie K, Xiong L. Conserved miR164-targeted NAC genes regulate drought resistence in rice. J Exp Bot. 2014;65:2119–35.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.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.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal. 2011;17:10–2.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 /.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.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.Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.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.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.Pink S, Vogel S. 2014. D3NETWORK: Stata module to create network visualizations using D3.js http://EconPapers.repec.org/RePEc:boc:bocode:s457844 .Csardi G, Nepusz T. The igraph software package for complex network research. Int J Complex Systems. 2006;1695:1–9