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• W4382657759 abstract "SUMMARY Transcriptome profiles in plants are heterogenous at every level of morphological organization. Even within organs, cells of the same type can have different patterns of gene expression depending on where they are positioned within tissues. This heterogeneity is associated with non‐uniform distribution of biological processes within organs. The regulatory mechanisms that establish and sustain the spatial heterogeneity are unknown. Here, we identify regulatory modules that support functional specialization of different parts of Oryza sativa cv. Nipponbare leaves by leveraging transcriptome data, transcription factor binding motifs and global gene regulatory network prediction algorithms. We generated a global gene regulatory network in which we identified six regulatory modules that were active in different parts of the leaf. The regulatory modules were enriched for genes involved in spatially relevant biological processes, such as cell wall deposition, environmental sensing and photosynthesis. Strikingly, more than 86.9% of genes in the network were regulated by members of only five transcription factor families. We also generated targeted regulatory networks for the large MYB and bZIP/bHLH families to identify interactions that were masked in the global prediction. This analysis will provide a baseline for future single cell and array‐based spatial transcriptome studies and for studying responses to environmental stress and demonstrates the extent to which seven coarse spatial transcriptome analysis can provide insight into the regulatory mechanisms supporting functional specialization within leaves." @default.
• W4382657759 created "2023-07-01" @default.
• W4382657759 creator A5037107799 @default.
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• W4382657759 date "2023-07-08" @default.
• W4382657759 modified "2023-10-17" @default.
• W4382657759 title "Spatially resolved gene regulatory networks in Asian rice (<i>Oryza sativa</i> cv. Nipponbare) leaves" @default.
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• W4382657759 doi "https://doi.org/10.1111/tpj.16375" @default.
• W4382657759 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37390084" @default.
• W4382657759 hasPublicationYear "2023" @default.
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