title: ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks.
abstract: BACKGROUND
  Uncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge. However, a vast amount of data from public microarray experiments is available, which can be subject to inference algorithms in order to recover reliable TRN architectures.
  
  
  RESULTS
  In this study we present a simple bioinformatics methodology that uses public, carefully curated microarray data and the mutual information algorithm ARACNe in order to obtain a database of transcriptional interactions. We used data from Arabidopsis thaliana root samples to show that the transcriptional regulatory networks derived from this database successfully recover previously identified root transcriptional modules and to propose new transcription factors for the SHORT ROOT/SCARECROW and PLETHORA pathways. We further show that these networks are a powerful tool to integrate and analyze high-throughput expression data, as exemplified by our analysis of a SHORT ROOT induction time-course microarray dataset, and are a reliable source for the prediction of novel root gene functions. In particular, we used our database to predict novel genes involved in root secondary cell-wall synthesis and identified the MADS-box TF XAL1/AGL12 as an unexpected participant in this process.
  
  
  CONCLUSIONS
  This study demonstrates that network inference using carefully curated microarray data yields reliable TRN architectures. In contrast to previous efforts to obtain root TRNs, that have focused on particular functional modules or tissues, our root transcriptional interactions provide an overview of the transcriptional pathways present in Arabidopsis thaliana roots and will likely yield a plethora of novel hypotheses to be tested experimentally.
authors.forename: Ricardo A
authors.initials: RA
authors.lastname: Chávez Montes
authors.forename: Gerardo
authors.initials: G
authors.lastname: Coello
authors.forename: Karla L
authors.initials: KL
authors.lastname: González-Aguilera
authors.forename: Nayelli
authors.initials: N
authors.lastname: Marsch-Martínez
authors.forename: Stefan
authors.initials: S
authors.lastname: de Folter
authors.affiliation: Laboratorio de Genética Molecular, Desarrollo y Evolución de Plantas, Instituto de Ecología and Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad Universitaria, México D,F, 04510, Mexico. eabuylla@gmail.com.
authors.forename: Elena R
authors.initials: ER
authors.lastname: Alvarez-Buylla
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics* / Q000528:radiation effects; D029681:Arabidopsis Proteins / Q000235:genetics / Q000378:metabolism; D002473:Cell Wall / Q000378:metabolism / Q000528:radiation effects; D057225:Data Mining; D030541:Databases, Genetic; D004843:Epistasis, Genetic / Q000528:radiation effects; D020869:Gene Expression Profiling; D018506:Gene Expression Regulation, Plant / Q000528:radiation effects; D053263:Gene Regulatory Networks / Q000235:genetics*; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing; D009154:Mutation / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D018517:Plant Roots / Q000235:genetics* / Q000528:radiation effects; D013997:Time Factors; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism; D014466:Ultraviolet Rays
chemical_list: D029681:Arabidopsis Proteins; D014157:Transcription Factors
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