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Transition of grapevine buds from paradormancy to endodormancy is coordinated by changes in gene expression, phytohormones, transcription factors, and other molecular regulators, but the mechanisms involved in transcriptional and post-transcriptional regulation of dormancy stages are not well delineated. To identify potential regulatory targets, an integrative analysis of differential gene expression profiles and their inverse relationships with miRNA abundance was performed in paradormant (long day (LD) 15 h) or endodormant (short day (SD), 13 h) Vitis riparia buds. There were 400 up- and 936 downregulated differentially expressed genes in SD relative to LD buds. Gene set and gene ontology enrichment analysis indicated that hormone signaling and cell cycling genes were downregulated in SD relative to LD buds. miRNA abundance and inverse expression analyses of miRNA target genes indicated increased abundance of miRNAs that negatively regulate genes involved with cell cycle and meristem development in endodormant buds and miRNAs targeting starch metabolism related genes in paradormant buds. Analysis of interactions between abundant miRNAs and transcription factors identified a network with coinciding regulation of cell cycle and epigenetic regulation related genes in SD buds. This network provides evidence for cross regulation occurring between miRNA and transcription factors both upstream and downstream of MYB3R1. 

Abstract Legume plants such as soybean produce two major types of root lateral organs, lateral roots and root nodules. A robust computational framework was developed to predict potential gene regulatory networks (GRNs) associated with root lateral organ development in soybean. A genome-scale expression data set was obtained from soybean root nodules and lateral roots and subjected to biclustering using QUBIC (QUalitative BIClustering algorithm). Biclusters and transcription factor (TF) genes with enriched expression in lateral root tissues were converged using different network inference algorithms to predict high-confidence regulatory modules that were repeatedly retrieved in different methods. The ranked combination of results from all different network inference algorithms into one ensemble solution identified 21 GRN modules of 182 co-regulated genes networks, potentially involved in root lateral organ development stages in soybean. The workflow correctly predicted previously known nodule- and lateral root-associated TFs including the expected hierarchical relationships. The results revealed distinct high-confidence GRN modules associated with early nodule development involving AP2, GRF5 and C3H family TFs, and those associated with nodule maturation involving GRAS, LBD41 and ARR18 family TFs. Knowledge from this work supported by experimental validation in the future is expected to help determine key gene targets for biotechnological strategies to optimize nodule formation and enhance nitrogen fixation.