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Nucleic Acids Research, 2009, Vol Published online 2 June 2009 Nucleic Acids Research, 2009, Vol. 37, No. 14 4587–4602 doi:10.1093/nar/gkp425 An integrative genomics approach identifies Hypoxia Inducible Factor-1 (HIF-1)-target genes that form the core response to hypoxia Yair Benita1, Hirotoshi Kikuchi2, Andrew D. Smith3, Michael Q. Zhang3, Daniel C. Chung2 and Ramnik J. Xavier1,2,* 1Center for Computational and Integrative Biology, 2Gastrointestinal Unit, Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 and 3Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA Received April 20, 2009; Revised May 6, 2009; Accepted May 8, 2009 ABSTRACT the pivotal mediators of the cellular response to hypoxia is hypoxia-inducible factor (HIF), a transcription factor The transcription factor Hypoxia-inducible factor 1 that contains a basic helix-loop-helix motif as well as (HIF-1) plays a central role in the transcriptional PAS domain. There are three known members of the response to oxygen flux. To gain insight into HIF family (HIF-1, HIF-2 and HIF-3) and all are a/b the molecular pathways regulated by HIF-1, it is heterodimeric proteins. HIF-1 was the first factor to be essential to identify the downstream-target genes. cloned and is the best understood isoform (1). HIF-3 is We report here a strategy to identify HIF-1-target a distant relative of HIF-1 and little is currently known genes based on an integrative genomic approach about its function and involvement in oxygen homeosta- combining computational strategies and experi- sis. HIF-2, however, is closely related to HIF-1 and both mental validation. To identify HIF-1-target genes activate hypoxia-dependent gene transcription. In all three microarrays data sets were used to rank genes isoforms the a-subunit senses oxygen levels (2–5). In nor- based on their differential response to hypoxia. moxia the a-subunit is ubiquitously expressed but is rap- The proximal promoters of these genes were then idly degraded through interaction with the VHL ubiquitin analyzed for the presence of conserved HIF-1- ligase complex (6). In hypoxic conditions, it is stabilized -binding sites. Genes were scored and ranked through multiple mechanisms that are transcriptional, based on their response to hypoxia and their HIF- post-transcriptional, as well as post-translational (6–9). binding site score. Using this strategy we recovered The b-subunit is widely expressed and generally not regulated by hypoxia. Once stabilized HIF-1 and HIF-2 41% of the previously confirmed HIF-1-target genes interact with co-activators such as CREBBP/EP300 and that responded to hypoxia in the microarrays and bind to a consensus sequence element (50-RCGTG-30)in provide a catalogue of predicted HIF-1 targets. the proximal promoters of hypoxia-regulated genes. We present experimental validation for ANKRD37 Together these genes control cellular process including as a novel HIF-1-target gene. Together these analy- a switch from oxidative to glycolytic metabolism, inhibi- ses demonstrate the potential to recover novel tion of cellular proliferation, and stimulation of oxygen HIF-1-target genes and the discovery of mamma- delivery through erythropoiesis and angiogenesis (10). lian-regulatory elements operative in the context of Metabolic deregulation is the final outcome if the adaptive microarray data sets. response is inadequate. Hypoxia can arise in a variety of developmental, phys- iological and pathological states. Solid tumors, such as breast cancer, quickly outgrow their blood supply and INTRODUCTION manipulate the hypoxia pathways to promote their own Maintenance of oxygen homeostasis in mammalian cells survival. The role of HIF-1 in tumor onset, progression, is fundamental to the survival of the organism. One of invasion and metastasis has been demonstrated in *To whom correspondence should be addressed. Tel: 617 643 3331; Fax: 617 643 3328; Email: [email protected] Present address: Andrew D. Smith, Department of Biology, University of Southern California, Los Angeles, CA 90089, USA The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. ß 2009 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 4588 Nucleic Acids Research, 2009, Vol. 37, No. 14 several solid tumors (11,12). In addition, cancer specific- described by Xie et al. (26). PWMs of HIF-1 have been regulatory roles for HIF-2 have been demonstrated in an previously used to identify genome-wide target genes. For expanding list of cancers (13). For instance, in clear renal instance, the Gene Set Enrichment Analysis program con- cell carcinoma the normal predominance of HIF1A tains a set of HIF-1-target genes as part of its Molecular expression is altered in favor of EPAS1 (HIF2a) expres- Signature Database (26,33). However, these approaches sion and promotes prosurvival rather than proapoptotic do not take into consideration promoter availability. factors (14,15). As a result HIF-target genes have emerged In addition, the consensus HIF-binding sequence is very as potential targets for cancer specific therapy (16,17). short, has low information content and is ubiquitously HIF-1 is an important metabolic regulator that allows present in the human genome making in-silico genome- rapid adaptation to oxygen availability. This effect is wide prediction unreliable. To generate a reliable predic- mediated through key regulators of bioenergetics and tion we propose to reduce the search space to a subset growth. For instance, PDK1 is directly induced by HIF-1 of genes expressed in specific tissues under specific condi- resulting in attenuation of mitochondrial function and tions. This can be achieved by microarray expression pro- oxygen consumption (18) and CDKN1A is induced leading files under the assumption that promoters of genes that to growth arrest at the G1 phase (19). Identifying respond to a stimulus are available for TF binding. such HIF-target genes is central to our understanding Several groups previously demonstrated that microarray of the HIF network and the mechanism of cellular path- expression profiles can be used to improve TFBS predic- ways modulation during hypoxia. In addition, identifying tion (32,34–36) and that cis-regulatory modules in proxi- additional binding sites in HIF-regulated genes might mal promoters correlate with differential expression of contribute to the paradigm of inferring function from downstream-target genes (27). sequence. In this study we present an integrative genomic Currently, studies attempting to identify novel HIF- approach combining both computational and experimen- target genes rely on microarray expression profiles to tal strategies to identify HIF-target genes. Using this detect genes that respond to changes in HIF protein approach we recovered 41% of the known HIF-1-target levels. Typically a gene of interest is selected and experi- genes that were differentially expressed in hypoxia, defined mentally validated for HIF binding to its promoter. Kim a ranked list of HIF-target genes and experimentally vali- et al. identified 25 transcripts that increase by at least dated ANKRD37 as a novel HIF-1 target. 4-fold during hypoxia in a B cell line. PDK1 was selected for further validation due to its role in regulating the TCA cycle. Consequently they were able to show that HIF-1 MATERIALS AND METHODS binds to the PDK1 promoter (18). Shen et al. (20) used Microarrays a more rationale microarray design where tissues from Caenorhabditis elegans were profiled in normoxia and in Raw microarray data was obtained from GEO (37) and hypoxia in a wild-type, HIF-1 knockout and a VHL ArrayExpress (38). Microarray studies were filtered to mutant strain. This study resulted in 63 HIF-1 dependent identify those that profiled the same tissue in normoxia genes that respond to hypoxia but only 12 of those had a and hypoxia on Affymetrix platform and used at least human homolog. Elvidge et al. (21) compared expression two replicates and provided raw data. Affymetrix-based profiles of human breast cancer cells in normoxia, hypoxia data sets were normalized using the GCRMA module and in normoxia treated with a 2-OG-dependent dioxy- (39) of bioconductor (40) and present–absent calls were genase inhibitor, dimethyloxalylglycine (DMOG) which calculated for each probe using the MAS5 module. All activates HIF-1. In addition, they compared expression probes with no present calls were removed and from the profiles in hypoxia and in hypoxia treated with siRNAs remaining at least one sample was required to have an to HIF1A and EPAS1 (HIF-2a). Manalo et al. (22) pro- expression value larger than log2 (100), as recommended filed arterial endothelial cells in normoxia and in hypoxia by Affymetrix. In all experiments, only pairwise compar- as well as in normoxia in the presence of a HIF-1a con- isons were made with respect to normoxia. After the initial stitutively active form. Only 45 genes were identified in filtering was applied, the expression values were centered both studies as HIF-1 dependent. Although these to the mean for each probe with replicates. Only probes approaches are more likely to filter out genes that are for which the expression values of the replicates were all not dependent on HIF-1, it remains challenging to deter- positive or all negative were further tested for differential mine whether the affected transcripts were directly or expression. The Limma module (41) of biocoductor was indirectly modulated by HIF. used to fit a linear model for each probe and P-values were Several methods for in-silico identification of tran- corrected using the false discovery rate method (42).
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