Computational prediction and experimental validation of novel Hedgehog-responsive enhancers linked to genes of the Hedgehog pathway
Identifieur interne : 001495 ( Ncbi/Checkpoint ); précédent : 001494; suivant : 001496Computational prediction and experimental validation of novel Hedgehog-responsive enhancers linked to genes of the Hedgehog pathway
Auteurs : Katherine Gurdziel [États-Unis] ; Kyle R. Vogt [États-Unis] ; Gary Schneider [États-Unis] ; Neil Richards [États-Unis] ; Deborah L. Gumucio [États-Unis]Source :
- BMC Developmental Biology [ 1471-213X ] ; 2016.
Descripteurs français
- KwdFr :
- Animaux, Biologie informatique (), Données de séquences moléculaires, Facteurs de transcription (métabolisme), Liaison aux protéines, Lignée cellulaire, Machine à vecteur de support, Motifs nucléotidiques (génétique), Protéine à doigts de zinc GLI1, Protéines Hedgehog (génétique), Protéines Hedgehog (métabolisme), Protéines oncogènes (métabolisme), Reproductibilité des résultats, Souris de lignée C57BL, Séquence nucléotidique, Transactivateurs (métabolisme), Transduction du signal (génétique), Éléments activateurs (génétique) (génétique).
- MESH :
- génétique : Motifs nucléotidiques, Protéines Hedgehog, Transduction du signal, Éléments activateurs (génétique).
- métabolisme : Facteurs de transcription, Protéines Hedgehog, Protéines oncogènes, Transactivateurs.
- Animaux, Biologie informatique, Données de séquences moléculaires, Liaison aux protéines, Lignée cellulaire, Machine à vecteur de support, Protéine à doigts de zinc GLI1, Reproductibilité des résultats, Souris de lignée C57BL, Séquence nucléotidique.
English descriptors
- KwdEn :
- Animals, Base Sequence, Cell Line, Computational Biology (methods), Enhancer Elements, Genetic (genetics), Hedgehog Proteins (genetics), Hedgehog Proteins (metabolism), Mice, Inbred C57BL, Molecular Sequence Data, Nucleotide Motifs (genetics), Oncogene Proteins (metabolism), Protein Binding, Reproducibility of Results, Signal Transduction (genetics), Support Vector Machine, Trans-Activators (metabolism), Transcription Factors (metabolism), Zinc Finger Protein GLI1.
- MESH :
- chemical , genetics : Hedgehog Proteins.
- genetics : Enhancer Elements, Genetic, Nucleotide Motifs, Signal Transduction.
- chemical , metabolism : Hedgehog Proteins, Oncogene Proteins, Trans-Activators, Transcription Factors.
- methods : Computational Biology.
- Animals, Base Sequence, Cell Line, Mice, Inbred C57BL, Molecular Sequence Data, Protein Binding, Reproducibility of Results, Support Vector Machine, Zinc Finger Protein GLI1.
Abstract
The Hedgehog (Hh) signaling pathway, acting through three homologous transcription factors (GLI1, GLI2, GLI3) in vertebrates, plays multiple roles in embryonic organ development and adult tissue homeostasis. At the level of the genome, GLI factors bind to specific motifs in enhancers, some of which are hundreds of kilobases removed from the gene promoter. These enhancers integrate the Hh signal in a context-specific manner to control the spatiotemporal pattern of target gene expression. Importantly, a number of genes that encode Hh pathway molecules are themselves targets of Hh signaling, allowing pathway regulation by an intricate balance of feed-back activation and inhibition. However, surprisingly few of the critical enhancer elements that control these pathway target genes have been identified despite the fact that such elements are central determinants of Hh signaling activity. Recently, ChIP studies have been carried out in multiple tissue contexts using mouse models carrying FLAG-tagged GLI proteins (GLIFLAG). Using these datasets, we tested whether a meta-analysis of GLI binding sites, coupled with a machine learning approach, could reveal genomic features that could be used to empirically identify Hh-regulated enhancers linked to loci of the Hh signaling pathway.
A meta-analysis of four existing GLIFLAG datasets revealed a library of GLI binding motifs that was substantially more restricted than the potential sites predicted by previous in vitro binding studies. A machine learning method (kmer-SVM) was then applied to these datasets and enriched k-mers were identified that, when applied to the mouse genome, predicted as many as 37,000 potential Hh enhancers. For functional analysis, we selected nine regions which were annotated to putative Hh pathway molecules and found that seven exhibited GLI-dependent activity, indicating that they are directly regulated by Hh signaling (78 % success rate).
The results suggest that Hh enhancer regions share common sequence features. The kmer-SVM machine learning approach identifies those features and can successfully predict functional Hh regulatory regions in genomic DNA surrounding Hh pathway molecules and likely, other Hh targets. Additionally, the library of enriched GLI binding motifs that we have identified may allow improved identification of functional GLI binding sites.
The online version of this article (doi:10.1186/s12861-016-0106-0) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12861-016-0106-0
PubMed: 26912062
PubMed Central: 4765071
Affiliations:
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<term>Base Sequence</term>
<term>Cell Line</term>
<term>Computational Biology (methods)</term>
<term>Enhancer Elements, Genetic (genetics)</term>
<term>Hedgehog Proteins (genetics)</term>
<term>Hedgehog Proteins (metabolism)</term>
<term>Mice, Inbred C57BL</term>
<term>Molecular Sequence Data</term>
<term>Nucleotide Motifs (genetics)</term>
<term>Oncogene Proteins (metabolism)</term>
<term>Protein Binding</term>
<term>Reproducibility of Results</term>
<term>Signal Transduction (genetics)</term>
<term>Support Vector Machine</term>
<term>Trans-Activators (metabolism)</term>
<term>Transcription Factors (metabolism)</term>
<term>Zinc Finger Protein GLI1</term>
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<keywords scheme="KwdFr" xml:lang="fr"><term>Animaux</term>
<term>Biologie informatique ()</term>
<term>Données de séquences moléculaires</term>
<term>Facteurs de transcription (métabolisme)</term>
<term>Liaison aux protéines</term>
<term>Lignée cellulaire</term>
<term>Machine à vecteur de support</term>
<term>Motifs nucléotidiques (génétique)</term>
<term>Protéine à doigts de zinc GLI1</term>
<term>Protéines Hedgehog (génétique)</term>
<term>Protéines Hedgehog (métabolisme)</term>
<term>Protéines oncogènes (métabolisme)</term>
<term>Reproductibilité des résultats</term>
<term>Souris de lignée C57BL</term>
<term>Séquence nucléotidique</term>
<term>Transactivateurs (métabolisme)</term>
<term>Transduction du signal (génétique)</term>
<term>Éléments activateurs (génétique) (génétique)</term>
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<term>Nucleotide Motifs</term>
<term>Signal Transduction</term>
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<term>Protéines Hedgehog</term>
<term>Transduction du signal</term>
<term>Éléments activateurs (génétique)</term>
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<keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Hedgehog Proteins</term>
<term>Oncogene Proteins</term>
<term>Trans-Activators</term>
<term>Transcription Factors</term>
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<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term>
</keywords>
<keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Facteurs de transcription</term>
<term>Protéines Hedgehog</term>
<term>Protéines oncogènes</term>
<term>Transactivateurs</term>
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<keywords scheme="MESH" xml:lang="en"><term>Animals</term>
<term>Base Sequence</term>
<term>Cell Line</term>
<term>Mice, Inbred C57BL</term>
<term>Molecular Sequence Data</term>
<term>Protein Binding</term>
<term>Reproducibility of Results</term>
<term>Support Vector Machine</term>
<term>Zinc Finger Protein GLI1</term>
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<term>Biologie informatique</term>
<term>Données de séquences moléculaires</term>
<term>Liaison aux protéines</term>
<term>Lignée cellulaire</term>
<term>Machine à vecteur de support</term>
<term>Protéine à doigts de zinc GLI1</term>
<term>Reproductibilité des résultats</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>The Hedgehog (Hh) signaling pathway, acting through three homologous transcription factors (GLI1, GLI2, GLI3) in vertebrates, plays multiple roles in embryonic organ development and adult tissue homeostasis. At the level of the genome, GLI factors bind to specific motifs in enhancers, some of which are hundreds of kilobases removed from the gene promoter. These enhancers integrate the Hh signal in a context-specific manner to control the spatiotemporal pattern of target gene expression. Importantly, a number of genes that encode Hh pathway molecules are themselves targets of Hh signaling, allowing pathway regulation by an intricate balance of feed-back activation and inhibition. However, surprisingly few of the critical enhancer elements that control these pathway target genes have been identified despite the fact that such elements are central determinants of Hh signaling activity. Recently, ChIP studies have been carried out in multiple tissue contexts using mouse models carrying FLAG-tagged GLI proteins (GLI<sup>FLAG</sup>
). Using these datasets, we tested whether a meta-analysis of GLI binding sites, coupled with a machine learning approach, could reveal genomic features that could be used to empirically identify Hh-regulated enhancers linked to loci of the Hh signaling pathway.</p>
</sec>
<sec><title>Results</title>
<p>A meta-analysis of four existing GLI<sup>FLAG</sup>
datasets revealed a library of GLI binding motifs that was substantially more restricted than the potential sites predicted by previous in vitro binding studies. A machine learning method (kmer-SVM) was then applied to these datasets and enriched k-mers were identified that, when applied to the mouse genome, predicted as many as 37,000 potential Hh enhancers. For functional analysis, we selected nine regions which were annotated to putative Hh pathway molecules and found that seven exhibited GLI-dependent activity, indicating that they are directly regulated by Hh signaling (78 % success rate).</p>
</sec>
<sec><title>Conclusions</title>
<p>The results suggest that Hh enhancer regions share common sequence features. The kmer-SVM machine learning approach identifies those features and can successfully predict functional Hh regulatory regions in genomic DNA surrounding Hh pathway molecules and likely, other Hh targets. Additionally, the library of enriched GLI binding motifs that we have identified may allow improved identification of functional GLI binding sites.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/s12861-016-0106-0) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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