Systematic measurements of dose-dependent responses for combinatorial treatments of SA and JA led to the development of transcriptomic biomarkers

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Systematic measurements of dose-dependent responses for combinatorial treatments of SA and JA led to the development of transcriptomic biomarkers

Authors

Tomita, A.; Maeda, T.; Mori-Moriyama, N.; Nomura, Y.; Kurita, Y.; Kashima, M.; Betsuyaku, S.; Nagano, A. J.

Abstract

Salicylic acid (SA) and jasmonic acid (JA) regulate plant defense against pathogens and herbivores. The SA-JA pathways often interact antagonistically with the induction of one, leading to the suppression of the other. However, the effect of combined SA/JA concentrations on plant responses is unknown. Here, we show transcriptional responses specific to combinations of SA and JA concentrations and develop transcriptomic biomarkers to estimate SA/JA response states. Combinatorial treatments with eight concentrations of SA and JA, combined with large-scale transcriptome analysis, revealed 43 different expression patterns, including not only well-known antagonistic responses but also responses specific to combinations of SA/JA concentrations. Notably, the expression of CYP94B3, a key enzyme involved in JA-Ile turnover, was finely tuned by specific SA/JA concentration combinations being induced under high SA conditions in a JA dose-dependent manner. In addition, the expression peaks of indole glucosinolate biosynthesis genes were found at approximately 30-fold higher JA concentrations than aliphatic glucosinolate biosynthesis genes. Furthermore, to quantitatively estimate SA/JA response states, we developed transcriptomic biomarkers using a machine learning approach. The marker specifically estimated response states to target phytohormones. Through analysis of npr3/4 double mutants, we confirmed that the biomarkers were applicable to the analysis of mutants. The biomarkers will allow us to extract insights into phytohormone responses from various large-scale transcriptome data, e.g., field transcriptomics and single-cell RNA-Seq.

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