Highly interwoven communities of a gene regulatory network unveil topologically important genes for maize seed development

Wenwei Xiong, Chunlei Wang, Xiangbo Zhang, Qinghua Yang, Ruixin Shao, Jinsheng Lai, Chunguang Du

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


The complex interactions between transcription factors (TFs) and their target genes in a spatially and temporally specific manner are crucial to all cellular processes. Reconstruction of gene regulatory networks (GRNs) from gene expression profiles can help to decipher TF-gene regulations in a variety of contexts; however, the inevitable prediction errors of GRNs hinder optimal data mining of RNA-Seq transcriptome profiles. Here we perform an integrative study of Zea mays (maize) seed development in order to identify key genes in a complex developmental process. First, we reverse engineered a GRN from 78 maize seed transcriptome profiles. Then, we studied collective gene interaction patterns and uncovered highly interwoven network communities as the building blocks of the GRN. One community, composed of mostly unknown genes interacting with opaque2, brittle endosperm1 and shrunken2, contributes to seed phenotypes. Another community, composed mostly of genes expressed in the basal endosperm transfer layer, is responsible for nutrient transport. We further integrated our inferred GRN with gene expression patterns in different seed compartments and at various developmental stages and pathways. The integration facilitated a biological interpretation of the GRN. Our yeast one-hybrid assays verified six out of eight TF-promoter bindings in the reconstructed GRN. This study identified topologically important genes in interwoven network communities that may be crucial to maize seed development.

Original languageEnglish
Pages (from-to)1143-1156
Number of pages14
JournalPlant Journal
Issue number6
StatePublished - Dec 2017


  • Zea mays
  • gene regulatory network
  • maize seed development
  • mutual information
  • network community
  • network inference


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