title: Expression-based network biology identifies immune-related functional modules involved in plant defense.
abstract: BACKGROUND
  Plants respond to diverse environmental cues including microbial perturbations by coordinated regulation of thousands of genes. These intricate transcriptional regulatory interactions depend on the recognition of specific promoter sequences by regulatory transcription factors. The combinatorial and cooperative action of multiple transcription factors defines a regulatory network that enables plant cells to respond to distinct biological signals. The identification of immune-related modules in large-scale transcriptional regulatory networks can reveal the mechanisms by which exposure to a pathogen elicits a precise phenotypic immune response.
  
  
  RESULTS
  We have generated a large-scale immune co-expression network using a comprehensive set of Arabidopsis thaliana (hereafter Arabidopsis) transcriptomic data, which consists of a wide spectrum of immune responses to pathogens or pathogen-mimicking stimuli treatments. We employed both linear and non-linear models to generate Arabidopsis immune co-expression regulatory (AICR) network. We computed network topological properties and ascertained that this newly constructed immune network is densely connected, possesses hubs, exhibits high modularity, and displays hallmarks of a "real" biological network. We partitioned the network and identified 156 novel modules related to immune functions. Gene Ontology (GO) enrichment analyses provided insight into the key biological processes involved in determining finely tuned immune responses. We also developed novel software called OCCEAN (One Click Cis-regulatory Elements ANalysis) to discover statistically enriched promoter elements in the upstream regulatory regions of Arabidopsis at a whole genome level. We demonstrated that OCCEAN exhibits higher precision than the existing promoter element discovery tools. In light of known and newly discovered cis-regulatory elements, we evaluated biological significance of two key immune-related functional modules and proposed mechanism(s) to explain how large sets of diverse GO genes coherently function to mount effective immune responses.
  
  
  CONCLUSIONS
  We used a network-based, top-down approach to discover immune-related modules from transcriptomic data in Arabidopsis. Detailed analyses of these functional modules reveal new insight into the topological properties of immune co-expression networks and a comprehensive understanding of multifaceted plant defense responses. We present evidence that our newly developed software, OCCEAN, could become a popular tool for the Arabidopsis research community as well as potentially expand to analyze other eukaryotic genomes.
authors.forename: Joel P
authors.initials: JP
authors.lastname: Tully
authors.forename: Aubrey E
authors.initials: AE
authors.lastname: Hill
authors.forename: Hadia M R
authors.initials: HM
authors.lastname: Ahmed
authors.forename: Ryan
authors.initials: R
authors.lastname: Whitley
authors.forename: Anthony
authors.initials: A
authors.lastname: Skjellum
authors.affiliation: Department of Biology, University of Alabama at Birmingham, Birmingham, AL, 35294-1170, USA. smukhtar@uab.edu.
authors.forename: M Shahid
authors.initials: MS
authors.lastname: Mukhtar
mesh_terms: D017360:Arabidopsis / Q000235:genetics* / Q000276:immunology*; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D017343:Genes, Plant; D017711:Nonlinear Dynamics; D011401:Promoter Regions, Genetic; D012044:Regression Analysis; D012984:Software