Incorporating motif analysis into gene co-expression networks reveals novel modular expression pattern and new signaling pathways

PLoS Genet. 2013;9(10):e1003840. doi: 10.1371/journal.pgen.1003840. Epub 2013 Oct 3.

Abstract

Understanding of gene regulatory networks requires discovery of expression modules within gene co-expression networks and identification of promoter motifs and corresponding transcription factors that regulate their expression. A commonly used method for this purpose is a top-down approach based on clustering the network into a range of densely connected segments, treating these segments as expression modules, and extracting promoter motifs from these modules. Here, we describe a novel bottom-up approach to identify gene expression modules driven by known cis-regulatory motifs in the gene promoters. For a specific motif, genes in the co-expression network are ranked according to their probability of belonging to an expression module regulated by that motif. The ranking is conducted via motif enrichment or motif position bias analysis. Our results indicate that motif position bias analysis is an effective tool for genome-wide motif analysis. Sub-networks containing the top ranked genes are extracted and analyzed for inherent gene expression modules. This approach identified novel expression modules for the G-box, W-box, site II, and MYB motifs from an Arabidopsis thaliana gene co-expression network based on the graphical Gaussian model. The novel expression modules include those involved in house-keeping functions, primary and secondary metabolism, and abiotic and biotic stress responses. In addition to confirmation of previously described modules, we identified modules that include new signaling pathways. To associate transcription factors that regulate genes in these co-expression modules, we developed a novel reporter system. Using this approach, we evaluated MYB transcription factor-promoter interactions within MYB motif modules.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Arabidopsis / genetics*
  • Cluster Analysis
  • Computational Biology*
  • Gene Expression Regulation, Plant
  • Gene Regulatory Networks*
  • Nucleotide Motifs
  • Promoter Regions, Genetic
  • Signal Transduction / genetics*
  • Transcription Factors / genetics

Substances

  • Transcription Factors

Grant support

This work is supported by National Science Foundation grants DBI-0723722 and DBI-1042344 to SPDK and MS, and UC Davis funds to SPDK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.