Deep Sequencing of the Medicago truncatula Root Transcriptome Reveals a Massive and Early Interaction between Nodulation Factor and Ethylene Signals.
Estíbaliz LarrainzarBrendan K RielySang Cheol KimNoelia Carrasquilla-GarciaHee-Ju YuHyun-Ju HwangMijin OhGoon Bo KimAnandkumar K SurendraraoDeborah A ChasmanAlireza Fotuhi SiahpiraniRamachandra V PenmetsaGang-Seob LeeNamshin KimSushmita RoyJeong-Hwan MunDouglas R CookPublished in: Plant physiology (2015)
The legume-rhizobium symbiosis is initiated through the activation of the Nodulation (Nod) factor-signaling cascade, leading to a rapid reprogramming of host cell developmental pathways. In this work, we combine transcriptome sequencing with molecular genetics and network analysis to quantify and categorize the transcriptional changes occurring in roots of Medicago truncatula from minutes to days after inoculation with Sinorhizobium medicae. To identify the nature of the inductive and regulatory cues, we employed mutants with absent or decreased Nod factor sensitivities (i.e. Nodulation factor perception and Lysine motif domain-containing receptor-like kinase3, respectively) and an ethylene (ET)-insensitive, Nod factor-hypersensitive mutant (sickle). This unique data set encompasses nine time points, allowing observation of the symbiotic regulation of diverse biological processes with high temporal resolution. Among the many outputs of the study is the early Nod factor-induced, ET-regulated expression of ET signaling and biosynthesis genes. Coupled with the observation of massive transcriptional derepression in the ET-insensitive background, these results suggest that Nod factor signaling activates ET production to attenuate its own signal. Promoter:β-glucuronidase fusions report ET biosynthesis both in root hairs responding to rhizobium as well as in meristematic tissue during nodule organogenesis and growth, indicating that ET signaling functions at multiple developmental stages during symbiosis. In addition, we identified thousands of novel candidate genes undergoing Nod factor-dependent, ET-regulated expression. We leveraged the power of this large data set to model Nod factor- and ET-regulated signaling networks using MERLIN, a regulatory network inference algorithm. These analyses predict key nodes regulating the biological process impacted by Nod factor perception. We have made these results available to the research community through a searchable online resource.