Time-course RNA-seq analysis provides an improved understanding of gene regulation during the formation of...

Using a time-course RNA-seq analysis we identified transcriptomic changes during formation of nodule-like structures (NLS) in rice and compared rice RNA-seq dataset with a nodule transcriptome dataset in Medicago truncatula. Plant hormones can induce the formation of nodule-like structures (NLS) in plant roots even in the absence of bacteria. These structures can be induced in roots of both legumes and non-legumes. Moreover, nitrogen-fixing bacteria can recognize and colonize these root structures. Therefore, identifying the genetic switches controlling the NLS organogenesis program in crops, especially cereals, can have important agricultural implications. Our recent study evaluated the transcriptomic response occurring in rice roots during NLS formation, 7 days post-treatment (dpt) with auxin, 2,4-D. In this current study, we investigated the regulation of gene expression occurring in rice roots at different stages of NLS formation: early (1-dpt) and late (14-dpt). At 1-dpt and 14-dpt, we identified 1662 and 1986 differentially expressed genes (DEGs), respectively. Gene ontology enrichment analysis revealed that the dataset was enriched with genes involved in auxin response and signaling; and in anatomical structure development and morphogenesis. Next, we compared the gene expression profiles across the three time points (1-, 7-, and 14-dpt) and identified genes that were uniquely or commonly differentially expressed at all three time points. We compared our rice RNA-seq dataset with a nodule transcriptome dataset in Medicago truncatula. This analysis revealed there is some amount of overlap between the molecular mechanisms governing nodulation and NLS formation. We also identified that some key nodulation genes were not expressed in rice roots during NLS formation. We validated the expression pattern of several genes via reverse transcriptase polymerase chain reaction (RT-PCR). The DEGs identified in this dataset may serve as a useful resource for future studies to characterize the genetic pathways controlling NLS formation in cereals.