The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Genome-wide identification of hypoxia-inducible factor binding sites and target genes by a probabilistic model integrating transcription-profiling data and in silico binding site prediction
Open accessAbstract
The transcriptional response driven by Hypoxia-inducible factor (HIF) is central to the adaptation to oxygen restriction. Hence, the complete identification of HIF targets is essential for understanding the cellular responses to hypoxia. Herein we describe a computational strategy based on the combination of phylogenetic footprinting and transcription profiling meta-analysis for the identification of HIF-target genes. Comparison of the resulting candidates with published HIF1a genome-wide chromatin immunoprecipitation indicates a high sensitivity (78%) and specificity (97.8%). To validate our strategy, we performed HIF1a chromatin immunoprecipitation on a set of putative targets. Our results confirm the robustness of the computational strategy in predicting HIF-binding sites and reveal several novel HIF targets, including RE1-silencing transcription factor co-repressor (RCOR2). In addition, mapping of described polymorphisms to the predicted HIF-binding sites identified several single-nucleotide polymorphisms (SNPs) that could alter HIF binding. As a proof of principle, we demonstrate that SNP rs17004038, mapping to a functional hypoxia response element in the macrophage migration inhibitory factor (MIF) locus, prevents induction of this gene by hypoxia. Altogether, our results show that the proposed strategy is a powerful tool for the identification of HIF direct targets that expands our knowledge of the cellular adaptation to hypoxia and provides cues on the inter-individual variation in this response.
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Citations
Cites
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288
Jaakkola, P., Mole, D. R., Tian, Y.-M., Wilson, M. I., Gielbert, J., Gaskell, S. J., Kriegsheim, A. V., Hebestreit, H. F., Mukherji, M., Schofield, C. J., et al. (2001) · Science · doi:10.1126/science.1059796 · Cited by 5,535
The tumour suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis
Maxwell, P. H., Wiesener, M. S., Chang, G.-W., Clifford, S. C., Vaux, E. C., Cockman, M. E., Wykoff, C. C., Pugh, C. W., Maher, E. R., & Ratcliffe, P. J. (1999) · Nature · doi:10.1038/20459 · Cited by 5,180
HIFα Targeted for VHL-Mediated Destruction by Proline Hydroxylation: Implications for O 2 Sensing
Ivan, M., Kondo, K., Yang, H., Kim, W., Valiando, J., Ohh, M., Salic, A., Asara, J. M., Lane, W. S., & Kaelin Jr., W. G. (2001) · Science · doi:10.1126/science.1059817 · Cited by 4,735 · cited 1×
“In normoxia, HIFa is hydroxylated at two proline residues (6,7) by a family of dioxygenases (EGL nine homologs, EGLNs) that require oxygen as cosubstrate (8,9).”
BackgroundEvolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes
Siepel, A., Bejerano, G., Pedersen, J. S., Hinrichs, A. S., Hou, M., Rosenbloom, K., Clawson, H., Spieth, J., Hillier, L. W., Richards, S., et al. (2005) · Genome Research · doi:10.1101/gr.3715005 · Cited by 3,971 · cited 2×
“The algorithm PhastCons implements a two state hidden Markov model that provides a score value that reflects the conservation of each base of a reference genome within a multiple species alignment (35).”
BackgroundMethodAsparagine Hydroxylation of the HIF Transactivation Domain: A Hypoxic Switch
Lando, D., Peet, D. J., Whelan, D. A., Gorman, J. J., & Whitelaw, M. L. (2002) · Science · doi:10.1126/science.1068592 · Cited by 1,558
Lando, D., Peet, D. J., Gorman, J. J., Whelan, D. A., Whitelaw, M. L., & Bruick, R. K. (2002) · Genes & Development · doi:10.1101/gad.991402 · Cited by 1,550
Differential Roles of Hypoxia-Inducible Factor 1α (HIF-1α) and HIF-2α in Hypoxic Gene Regulation
Hu, C.-J., Wang, L.-Y., Chodosh, L. A., Keith, B., & Simon, M. C. (2003) · Molecular and Cellular Biology · doi:10.1128/mcb.23.24.9361-9374.2003 · Cited by 1,386
A MicroRNA Signature of Hypoxia
Kulshreshtha, R., Ferracin, M., Wojcik, S. E., Garzon, R., Alder, H., Agosto-Perez, F. J., Davuluri, R., Liu, C.-G., Croce, C. M., Negrini, M., et al. (2007) · Molecular and Cellular Biology · doi:10.1128/mcb.01395-06 · Cited by 1,055
Raval, R. R., Lau, K. W., Tran, M. G. B., Sowter, H. M., Mandriota, S. J., Li, J.-L., Pugh, C. W., Maxwell, P. H., Harris, A. L., & Ratcliffe, P. J. (2005) · Molecular and Cellular Biology · doi:10.1128/mcb.25.13.5675-5686.2005 · Cited by 940
Cited by
iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections
Janky, R., Verfaillie, A., Imrichová, H., Van de Sande, B., Standaert, L., Christiaens, V., Hulselmans, G., Herten, K., Naval Sanchez, M., Potier, D., et al. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003731 · Cited by 900
“The predicted HIF1A targets are likely functional targets because they overlap much more (41%) with known HIF1A targets [54] than the non predicted targets (15%).”
BackgroundHigh-resolution genome-wide mapping of HIF-binding sites by ChIP-seq
Schödel, J., Oikonomopoulos, S., Ragoussis, J., Pugh, C. W., Ratcliffe, P. J., & Mole, D. R. (2011) · Blood · doi:10.1182/blood-2010-10-314427 · Cited by 724
Fructose-1,6-bisphosphatase opposes renal carcinoma progression
Li, B., Qiu, B., Lee, D. S. M., Walton, Z. E., Ochocki, J. D., Mathew, L. K., Mancuso, A., Gade, T. P. F., Keith, B., Nissim, I., et al. (2014) · Nature · doi:10.1038/nature13557 · Cited by 535
Hypoxia induces rapid changes to histone methylation and reprograms chromatin
Batie, M., Frost, J., Frost, M., Wilson, J. W., Schofield, P., & Rocha, S. (2019) · Science · doi:10.1126/science.aau5870 · Cited by 385
Gameiro, P. A., Yang, J., Metelo, A. M., Pérez-Carro, R., Baker, R., Wang, Z., Arreola, A., Rathmell, W. K., Olumi, A., López-Larrubia, P., et al. (2013) · Cell Metabolism · doi:10.1016/j.cmet.2013.02.002 · Cited by 325
Metadata
- Authors
- Ortiz-Barahona, A., Villar, D., Pescador, N., Amigo, J., & del Peso, L.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-01-08
- Volume
- 38
- Issue
- 7
- Pages
- 2332-2345
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2131078129
- Cited by
- 202
- References
- 12
- FWCI
- 3.43
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ortiz-Barahona, A., Villar, D., Pescador, N., Amigo, J., & del Peso, L. (2010). Genome-wide identification of hypoxia-inducible factor binding sites and target genes by a probabilistic model integrating transcription-profiling data and in silico binding site prediction. Nucleic Acids Research. https://doi.org/10.1093/nar/gkp1205
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