Identificación de nuevos genes diana del factor inducible por hipoxia HIF mediante técnicas computacionales

Tesis doctoral realizada en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Bioquímica y el Instituto de Investigaciones Biomédicas de Madrid "Alberto Sols". Fecha de lectura: 30 de Septiembre 201

Cooperativity of stress-responsive transcription factors in core hypoxia-inducible factor binding regions

The transcriptional response driven by Hypoxia-inducible factor (HIF) is central to the adaptation to oxygen restriction. Despite recent characterization of genome-wide HIF DNA binding locations and hypoxia-regulated transcripts in different cell types, the molecular bases of HIF target selection remain unresolved. Herein, we combined multi-level experimental data and computational predictions to identify sequence motifs that may contribute to HIF target selectivity. We obtained a core set of bona fide HIF binding regions by integrating multiple HIF1 DNA binding and hypoxia expression profiling datasets. This core set exhibits evolutionarily conserved binding regions and is enriched in functional responses to hypoxia. Computational prediction of enriched transcription factor binding sites identified sequence motifs corresponding to several stress-responsive transcription factors, such as activator protein 1 (AP1), cAMP response element-binding (CREB), or CCAAT-enhancer binding protein (CEBP). Experimental validations on HIF-regulated promoters suggest a functional role of the identified motifs in modulating HIF-mediated transcription. Accordingly, transcriptional targets of these factors are over-represented in a sorted list of hypoxia-regulated genes. Altogether, our results implicate cooperativity among stress-responsive transcription factors in fine-tuning the HIF transcriptional responseThis work was supported by Ministerio de Ciencia e Innovación (Spanish Ministry of Science and Innovation, MICINN) [grant number SAF2008-03147 to L. del P.], Comunidad Autónoma de Madrid [grant number S-SAL-0311_2006 to L. del P.] and the 7th Research Framework Programme of the European Union [grant number METOXIA project ref. HEALTH-F2-2009-222741] to L. del P. D.V. was a recipient of PhD funding from the Spanish Ministry of Science and Innovation [FPU programme] and the European Molecular Biology Organization [Short-Term Fellowships

Hypoxia Promotes Glycogen Accumulation through Hypoxia Inducible Factor (HIF)-Mediated Induction of Glycogen Synthase 1

When oxygen becomes limiting, cells reduce mitochondrial respiration and increase ATP production through anaerobic fermentation of glucose. The Hypoxia Inducible Factors (HIFs) play a key role in this metabolic shift by regulating the transcription of key enzymes of glucose metabolism. Here we show that oxygen regulates the expression of the muscle glycogen synthase (GYS1). Hypoxic GYS1 induction requires HIF activity and a Hypoxia Response Element within its promoter. GYS1 gene induction correlated with a significant increase in glycogen synthase activity and glycogen accumulation in cells exposed to hypoxia. Significantly, knockdown of either HIF1α or GYS1 attenuated hypoxia-induced glycogen accumulation, while GYS1 overexpression was sufficient to mimic this effect. Altogether, these results indicate that GYS1 regulation by HIF plays a central role in the hypoxic accumulation of glycogen. Importantly, we found that hypoxia also upregulates the expression of UTP:glucose-1-phosphate urydylyltransferase (UGP2) and 1,4-α glucan branching enzyme (GBE1), two enzymes involved in the biosynthesis of glycogen. Therefore, hypoxia regulates almost all the enzymes involved in glycogen metabolism in a coordinated fashion, leading to its accumulation. Finally, we demonstrated that abrogation of glycogen synthesis, by knock-down of GYS1 expression, impairs hypoxic preconditioning, suggesting a physiological role for the glycogen accumulated during chronic hypoxia. In summary, our results uncover a novel effect of hypoxia on glucose metabolism, further supporting the central importance of metabolic reprogramming in the cellular adaptation to hypoxia

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

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

Cooperativity of stress-responsive transcription factors in core hypoxia-inducible factor binding regions

This is an open-access article distributed under the terms of the Creative Commons Attribution License.-- et al.The transcriptional response driven by Hypoxia-inducible factor (HIF) is central to the adaptation to oxygen restriction. Despite recent characterization of genome-wide HIF DNA binding locations and hypoxia-regulated transcripts in different cell types, the molecular bases of HIF target selection remain unresolved. Herein, we combined multi-level experimental data and computational predictions to identify sequence motifs that may contribute to HIF target selectivity. We obtained a core set of bona fide HIF binding regions by integrating multiple HIF1 DNA binding and hypoxia expression profiling datasets. This core set exhibits evolutionarily conserved binding regions and is enriched in functional responses to hypoxia. Computational prediction of enriched transcription factor binding sites identified sequence motifs corresponding to several stress-responsive transcription factors, such as activator protein 1 (AP1), cAMP response element-binding (CREB), or CCAAT-enhancer binding protein (CEBP). Experimental validations on HIF-regulated promoters suggest a functional role of the identified motifs in modulating HIF-mediated transcription. Accordingly, transcriptional targets of these factors are over-represented in a sorted list of hypoxia-regulated genes. Altogether, our results implicate cooperativity among stress-responsive transcription factors in fine-tuning the HIF transcriptional response.This work was supported by Ministerio de Ciencia e Innovación (Spanish Ministry of Science and Innovation, MICINN) [grant number SAF2008-03147 to L. del P.], Comunidad Autónoma de Madrid [grant number S-SAL-0311_2006 to L. del P.] and the 7th Research Framework Programme of the European Union [grant number METOXIA project ref. HEALTH-F2-2009-222741] to L. del P. D.V. was a recipient of PhD funding from the Spanish Ministry of Science and Innovation [FPU programme] and the European Molecular Biology Organization [Short-Term Fellowships].Peer reviewe

Enriched TFBSs in core HIF binding regions (Fisher’s exact test).

<p>Enriched sequence motifs in core HIF binding regions, as indicated by statistical analysis (Fisher’s exact test, p<0.05 with no correction for multiple comparisons). For each overrepresented sequence motif/PWM, the table indicates the following: the database collection (PWM collection), the stringency used in <i>in silico</i> TFBS identification (Stringency), the number of hits obtained in the set of core HBRs (Hits), the transcription factor (Tr. Factor) associated to the PWM and the p value of the enrichment (p value). Robust predictions across different stringencies and PWM datasets are highlighted in bold.</p

Transcriptional targets of stress-responsive transcription factors are enriched among core hypoxia-responsive genes.

<p>(<b>A</b>) Gene-set enrichment analysis on a set of 11673 genes sorted by their response to hypoxia according to a meta-analysis of hypoxia gene expression experiments (ref. 13). The graph depicts the normalized enrichment score of 3174 gene sets from the curated collection (C2) of the GSEA molecular signatures database v3.0, that includes sets of transcription factor target genes. Solid circles highlight gene-sets derived from studies on HIF1 (black, ELVIDGE_HYPOXIA_UP and SEMENZA_HIF1_TARGETS), CEBPA/B (purple, GERY_CEBP_TARGETS, HALMOS_CEBPA_TARGETS_UP and TAVOR_CEBPA_TARGETS_UP), CREB1/ATF5 (orange, GHO_ATF5_TARGETS_DN and MCCLUNG_CREB1_TARGETS_UP) and AP1 (green, OZANNE_AP1_TARGETS_UP) transcriptional targets. The vertical blue line corresponds to an FDR-adjusted p-value of 0.05. (<b>B</b>) GSEA analysis of hypoxia-responsive genes (see A) against the GERY_CEBPA_TARGETS (M12338, derived from the GEO dataset GSE2188) gene-set. Hypoxic response is rank-ordered in the horizontal axis (Rank in ordered dataset). Black bars indicate the position of individual targets in the CEBPA gene-set. The graph on top (green curve) represents enrichment scores of CEBPA targets across hypoxia responsive genes, indicating positive correlation between the two. The gradient color bar indicates positive (red) and negative (blue) correlation boundaries.</p

Basal gene expression is not sufficient for HIF transactivation.

<p>(<b>A</b>) A list of well-characterized HIF target genes (from ref. 7) present in individual gene expression profiling (microarray) datasets (see B for GEO IDs) were categorized according to their basal (normoxic) expression level into two groups: Null, no detectable basal expression; Significant, detectable basal expression. In addition, for each microarray experiment, HIF-target genes were further classified into those whose expression was significantly induced by hypoxia (ratio hypoxia/normoxia greater than 2.6Sd above the mean) and non-responsive genes. The graph represents the percentage of HIF target genes in each category that were induced by hypoxia. Each pair of joined dots represents the data from a single microarray experiment. Box and whisker plots represent the distribution of values in each category. **: p<0.01 (Wilkoxon matched test) (<b>B</b>) For each of the indicated microarray datasets (GEO identifiers in horizontal axis), we identified all the genes showing a significant basal (normoxic) expression. Then, we classified them according to their response to hypoxia (“Induced” and “notInduced”, see A) and the presence of conserved RCGTG motifs in their regulatory regions (“HBS” and “NoHBS”, respectively). The graph depicts cumulative percentages (vertical axis) of genes in each of the four combinations of the two categories: no conserved HIF binding motifs and no hypoxic induction (blue, NoHBS_notInduced), no conserved HIF binding motifs but hypoxic induction (green, NoHBS_Induced), conserved HIF binding motifs but no hypoxic induction (yellow, HBS_notInduced) and conserved HIF binding sites and hypoxic induction (red, HBS_Induced).</p