National Research Council of Italy

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IBBR publication #1947

Identification of stress-responsive microRNAs in plant-microbe interactions

De Paola D, Ogata-Gutierrez K, Chumpitaz-Segovia C, Finetti Sialer M, Zúñiga-Dávila D

In: “FEMS Microbiology Congress 2017”. Valencia, Spain, 9-13/7/2017. (2017)
URL: http://www.fems-microbiology2017.kenes.com/scientific-information/abstrac...

Backgrounds Non-coding small RNAs (sRNAs) regulate gene expression in plant development and stress response. Different sRNA, in particular microRNAs (miRNAs) are principal mediators of the plant immune system. Roots endophytic bacteria and fungi affect crops performance acting as key soil factors underpinning ecosystem functioning and determining the properties of associated biota. Objectives We aimed at identifying plant miRNAs involved in the tri-trophic interaction of a beneficial bacterium (Pseudomonas sp.) and Solanum lycopersicum challenged with the pathogenic fungus Alternaria alternata, through comparative analyses carried out in different plant-microorganism associations. Methods sRNA profiling was performed on 3-week-old plants of S. lycopersicum plants treated with Pseudomonas sp. (Ps155) or challenged with the pathogen, alone or in combination, or non-treated as control. RNA extraction and library construction was carried out in two biological replicates per treatment and sequenced by Illumina technology. The sRNA libraries were aligned to the tomato genome (release 2.40). Reads (100% matching) were mapped onto the miRBase release 21.0 to identify known miRNAs. Differential miRNA expression (DE) analysis was based on DESeq2. Conclusions 334 known tomato miRNAs were identified in the sRNA libraries, of which near 50% were shared across libraries. The four treatments were evaluated in pairwise comparisons to gain evidence for their DE among treatments. Statistical analyses highlighted 27 differential regulated miRNAs. These DE targeted different tomato genes including transcription and growth-regulating factors based on computational target prediction. Data provide a comparative analysis of changes in the small RNA profiles and the putative gene targets they control.

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