ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks

BMC Plant Biology

Research Article

Nayelli Marsch-Martínez¹  Stefan de Folter²  Karla L González-Aguilera²  Elena R Alvarez-Buylla³  Ricardo A Chávez Montes⁴  Gerardo Coello⁵ 

[1] Departamento de Biotecnologıa y Bioquımica, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Km 9.6 Libramiento Norte, Carretera Irapuato-León, AP 629, CP 36821, Irapuato, Guanajuato, Mexico;Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Km 9.6 Libramiento Norte, Carretera Irapuato-León, AP 629, CP 36821, Irapuato, Guanajuato, Mexico;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, 04510, México D.F., Mexico;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, 04510, México D.F., Mexico;Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Km 9.6 Libramiento Norte, Carretera Irapuato-León, AP 629, CP 36821, Irapuato, Guanajuato, Mexico;Unidad de Cómputo, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Ciudad Universitaria, 04510, México D.F., Mexico;

关键词: Transcriptional regulatory networks;    Root;    Arabidopsis;    Transcription factor;    ARACNe;   

DOI  :  10.1186/1471-2229-14-97

 received in 2013-09-03, accepted in 2014-03-27,  发布年份 2014

来源: Springer

【 摘 要 】

BackgroundUncovering 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.ResultsIn 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.ConclusionsThis 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.

【 授权许可】

Unknown   
© Montes et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.