Multiplatform genome-wide identification and modeling of functional human estrogen receptor binding sites

Xie Min, Vega, Lin Chin-Yo, Lai Koon, Li, Su Xiaodi, Teh Huey, Thomsen, Sung Wing, Bourque Guillaume, Liu

Description

Transcription factor binding sites (TFBS) impart specificity to cellular transcriptional responses and have largely been defined by consensus motifs derived from a handful of validated sites. The low specificity of the computational predictions of TFBSs has been attributed to ubiquity of the motifs and the relaxed sequence requirements for binding. We posited that the inadequacy is due to limited input of empirically verified sites, and demonstrated a multiplatform approach to constructing a robust model. Results Using the TFBS for the estrogen receptor (ER)α (estrogen response element [ERE]) as a model system, we extracted EREs from multiple molecular and genomic platforms whose binding to ERα has been experimentally confirmed or rejected. In silico analyses revealed significant sequence information flanking the standard binding consensus, discriminating ERE-like sequences that bind ERα from those that are nonbinders. We extended the ERE consensus by three bases, bearing a terminal G at the third position 3' and an initiator C at the third position 5', which were further validated using surface plasmon resonance spectroscopy. Our functional human ERE prediction algorithm (h-ERE) outperformed existing predictive algorithms and produced fewer than 5% false negatives upon experimental validation. Conclusion Building upon a larger experimentally validated ERE set, the h-ERE algorithm is able to demarcate better the universe of ERE-like sequences that are potential ER binders. Only 14% of the predicted optimal binding sites were utilized under the experimental conditions employed, pointing to other selective criteria not related to EREs. Other factors, in addition to primary nucleotide sequence, will ultimately determine binding site selection.

Informations

• Publié par: biomed
• Publié le: 01 janvier 2006
• Nombre de lectures: 8
• Langue: English

Extrait

2e V R t0e oagl e 0lua.6 s m e e a 7 r , I c s h sue 9, Article R82 Open Access Multiplatform genome-wide ide ntification and modeling of functional human estrogen receptor binding sites Vinsensius B Vega ¤ * , Chin-Yo Lin ¤ *§ , Koon Siew Lai * , Say Li Kong * , Min Xie * , Xiaodi Su ¶ , Huey Fang Teh ¶ , Jane S Thomsen * , Ai Li Yeo * , Wing Kin Sung  , Guillaume Bourque  and Edison T Liu * Addresses: * Estrogen Receptor Biology Program, Genome Institute of Singapore, 60 Biopolis Street, Republic of Singapore 138672.  Information and Mathematical Sciences Group, Genome Institute of Singapore, 60 Biopolis Street, Republic of Singapore 138672.  Microarray and Expression Genomics Laboratory, Genome Institute of Singapore, 60 Biopolis Street, Republic of Singapore 138672. § Department of Microbiology and Molecular Biology, Brigham Young University, 753 WIDB, Provo, UT 84602, USA. ¶ Institute of Materials Research and Engineering, 3, Research Link, Republic of Singapore 117602. ¤ These authors contributed equally to this work. Correspondence: Edison T Liu. Email: [e-mail] Vinsensius B Vega. E-mail: [e-mail]

Published: 9 September 2006 Received: 27 February 2006 Genome Biology 2006, 7: R82 (doi:10.1186/gb-2006-7-9-r82)  Revised: 11 May 2006 Accepted: 9 September 2006 The electronic version of this arti cle is the complete one and can be found online at http://genomebiology.com/2006/7/9/R82 © 2006 Vega et al .; licensee 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 origin al work is properly cited. e<Hxpute>mnRadenefi den sebtirmnoedgine n t go  rfs etpch e ecpit f iuocrni tcbyti insoidngia n la glh .su<it/mepsa>n estrogen receptor binding site model using a multi-platform genome-wide approach reveals 

Abstract Background: Transcription factor binding sites (TFBS) impa rt specificity to cellular transcriptional responses and have largely been defined by consen sus motifs derived from a handful of validated sites. The low specificity of the computational pr edictions of TFBSs has been attributed to ubiquity of the motifs and the relaxed sequence requiremen ts for binding. We posi ted that the inadequacy is due to limited input of empiri cally verified sites, and demonstr ated a multiplatform approach to constructing a robust model. Results: Using the TFBS for the estrogen receptor (ER) α (estrogen response element [ERE]) as a model system, we extracted EREs from multiple molecular and genomic platforms whose binding to ER α  has been experimentally confirmed or rejected. In silico analyses revealed significant sequence information flanking the standard binding consensus, disc riminating ERE-like sequences that bind ER α from those that are nonbinders. We ex tended the ERE consensus by three bases, bearing a terminal G at the third position 3' and an initiator C at the third position 5', which were further validated using surfac e plasmon resonance spectrosco py. Our functional human ERE prediction algorithm (h-ERE) outperformed existing predictive algorithms and produced fewer than 5% false negatives upon e xperimental validation. Conclusion: Building upon a larger experimentally valida ted ERE set, the h-ERE algorithm is able to demarcate better the universe of ERE-like sequences that are po tential ER binders. Only 14% of the predicted optimal binding sites were utilized under the experimental conditions employed, pointing to other selective crit eria not related to EREs. Other factors, in addition to primary nucleotide sequence, wil l ultimately determine binding site selection.

Genome Biology 2006, 7: R82