Cells adapt to environmental changes, including fluctuations in oxygen levels, through the induction of specific gene expression programs. To identify genes regulated by hypoxia at the transcriptional level, we pulse-labeled HUVEC cells with 4-thiouridine and sequenced nascent transcripts. Then, we searched genome-wide binding profiles from the ENCODE project for factors that correlated with changes in transcription and identified binding of several components of the Sin3A co-repressor complex, including SIN3A, SAP30 and HDAC1/2, proximal to genes repressed by hypoxia. SIN3A interference revealed that it participates in the downregulation of 75% of the hypoxia-repressed genes in endothelial cells. Unexpectedly, it also blunted the induction of 47% of the upregulated genes, suggesting a role for this corepressor in gene induction. In agreement, ChIP-seq experiments showed that SIN3A preferentially localizes to the promoter region of actively transcribed genes and that SIN3A signal was enriched in hypoxia-repressed genes, prior exposure to the stimulus. Importantly, SINA3 occupancy was not altered by hypoxia in spite of changes in H3K27ac signal. In summary, our results reveal a prominent role for SIN3A in the transcriptional response to hypoxia and suggest a model where modulation of the associated histone deacetylase activity, rather than its recruitment, determines the transcriptional output.

Summary The computational identification of the transcription factors (TFs) [more generally, transcription regulators, (TR)] responsible for the co-regulation of a specific set of genes is a common problem found in genomic analysis. Herein, we describe TFEA.ChIP, a tool that makes use of ChIP-seq datasets to estimate and visualize TR enrichment in gene lists representing transcriptional profiles. We validated TFEA.ChIP using a wide variety of gene sets representing signatures of genetic and chemical perturbations as input and found that the relevant TR was correctly identified in 126 of a total of 174 analyzed. Comparison with other TR enrichment tools demonstrates that TFEA.ChIP is an highly customizable package with an outstanding performance. Availability and implementation TFEA.ChIP is implemented as an R package available at Bioconductor https://www.bioconductor.org/packages/devel/bioc/html/TFEA.ChIP.html and github https://github.com/LauraPS1/TFEA.ChIP_downloads. A web-based GUI to the package is also available at https://www.iib.uam.es/TFEA.ChIP/ Supplementary information Supplementary data are available at Bioinformatics online.

BackgroundIntegrating transcriptional profiles results in the identification of gene expression signatures that are more robust than those obtained for individual datasets. However, direct comparison of datasets derived from heterogeneous experimental conditions is not possible and their integration requires the application of specific meta-analysis techniques. The transcriptional response to hypoxia has been the focus of intense research due to its central role in tissue homeostasis and in prevalent diseases. Accordingly, a large number of studies have determined the gene expression profile of hypoxic cells. Yet, in spite of this wealth of information, little effort have been done to integrate these dataset to produce a robust hypoxic signature.ResultsWe applied a formal meta-analysis procedure to a dataset comprising 425 RNAseq samples derived from 42 individual studies including 33 different cell types, to derive a pooled estimate of the effect of hypoxia on gene expression. This approach revealed that a large proportion of the transcriptome (8556 genes out of 20888) is significantly regulated by hypoxia. However, only a small fraction of the differentially expressed genes (1265 genes, 15%) show an effect size that, according to comparisons to gene pathways known to be regulated by hypoxia, is likely to be biologically relevant. By focusing on genes ubiquitously expressed we identified a signature of 291 genes robustly and consistently regulated by hypoxia. Finally, by a applying a moderator analysis we found that endothelial cells show a characteristic gene expression pattern that is significantly different from other cell types.ConclusionBy the application of a formal meta-analysis to hypoxic gene profiles, we have developed a robust gene signature that characterizes the transcriptomic response to low oxygen. In addition to identifying a universal set of hypoxia-responsive genes, we found a set of genes whose regulation is cell-type specific and suggest a unique metabolic response of endothelial cells to reduced oxygen tension.

The identification of transcription factors (TFs) responsible for the co-regulation of specific sets of genes is a common problem in transcriptomics. Herein we describe TFEA.ChIP, a tool to estimate and visualize TF enrichment in gene lists representing transcriptional profiles. To generate the gene sets representing TF targets, we gathered ChIP-Seq experiments from the ENCODE Consortium and GEO datasets and used the correlation between Dnase Hypersensitive Sites across cell lines to generate a database linking TFs with the genes they interact with in each ChIP-Seq experiment. In its current state, TFEA.ChIP covers 327 different transcription factors from 1075 ChIP-Seq experiments, with over 150 cell types being represented. TFEA.ChIP accepts gene sets as well as sorted lists differentially expressed genes to compute enrichment scores for each of the datasets in its internal database using an Fisher's exact association test or a Gene Set Enrichment Analysis. We validated TFEA.ChIP using a wide variety of gene sets representing signatures of genetic and chemical perturbations as input and found that the relevant TF was correctly identified in 103 of a total of 144 analyzed datasets with a median area under the curve (AUC) of 0.86. In depth analysis of an RNAseq dataset, illustrates that the use of ChIP-Seq data instead of PWM-based provides key biological context to interpret the results of the analysis. To facilitate its integration into transcriptome analysis pipelines and allow easy expansion and customization of the TF-gene database, we implemented TFEA.ChIP as an R package that can be downloaded from Bioconductor: https://www.bioconductor.org/packages/devel/bioc/html/TFEA.ChIP.html and github: https://github.com/LauraPS1/TFEA-drafts In addition, make it available to a wide range of researches, we have also developed a web application that runs the package from the server side and enables easy exploratory analysis through interactive graphs: https://www.iib.uam.es/TFEA.ChIP/

Angiogenesis, the main mechanism that allows vascular expansion for tissue regeneration or disease progression, is often triggered by an imbalance between oxygen consumption and demand. Here, by analyzing changes in the transcriptomic profile of endothelial cells (ECs) under hypoxia we uncovered that the repression of cell cycle entry and DNA replication stand as central responses in the early adaptation of ECs to low oxygen tension. Accordingly, hypoxia imposed a restriction in S-phase in ECs that is mediated by Hypoxia-Inducible Factors. Our results indicate that the induction of angiogenesis by hypoxia in Embryoid Bodies generated from murine Stem Cells is accomplished by the compensation of decreased S-phase entry in mature ECs and differentiation of progenitor cells. This conditioning most likely allows an optimum ACOSTA-IBORRA eT Al.

Integrating transcriptional profiles results in identifying gene expression signatures that are more robust than those obtained for individual datasets. However, a direct comparison of datasets derived from heterogeneous experimental conditions is problematic, hence their integration requires applying of specific meta-analysis techniques. The transcriptional response to hypoxia has been the focus of intense research due to its central role in tissue homeostasis and prevalent diseases. Accordingly, many studies have determined the gene expression profile of hypoxic cells. Yet, despite this wealth of information, little effort has been made to integrate these datasets to produce a robust hypoxic signature. We applied a formal meta-analysis procedure to datasets comprising 430 RNA-seq samples from 43 individual studies including 34 different cell types, to derive a pooled estimate of the effect of hypoxia on gene expression in human cell lines grown ingin vitro. This approach revealed that a large proportion of the transcriptome is significantly regulated by hypoxia (8556 out of 20,888 genes identified across studies). However, only a small fraction of the differentially expressed genes (1265 genes, 15%) show an effect size that, according to comparisons to gene pathways known to be regulated by hypoxia, is likely to be biologically relevant. By focusing on genes ubiquitously expressed, we identified a signature of 291 genes robustly and consistently regulated by hypoxia. Overall, we have developed a robust gene signature that characterizes the transcriptomic response of human cell lines exposed to hypoxia in vitro by applying a formal meta-analysis to gene expression profiles.

Molecular gene signatures are useful tools to characterize the physiological state of cell populations, but most have developed under a narrow range of conditions and cell types and are often restricted to a set of gene identities. Focusing on the transcriptional response to hypoxia, we aimed to generate widely applicable classifiers sourced from the results of a meta-analysis of 69 differential expression datasets which included 425 individual RNA-seq experiments from 33 different human cell types exposed to different degrees of hypoxia (0.1–5%$$\hbox {O}_{2}$$ O 2 ) for 2–48 h. The resulting decision trees include both gene identities and quantitative boundaries, allowing for easy classification of individual samples without control or normoxic reference. Each tree is composed of 3–5 genes mostly drawn from a small set of just 8 genes (EGLN1, MIR210HG, NDRG1, ANKRD37, TCAF2, PFKFB3, BHLHE40, and MAFF). In spite of their simplicity, these classifiers achieve over 95% accuracy in cross validation and over 80% accuracy when applied to additional challenging datasets. Our results indicate that the classifiers are able to identify hypoxic tumor samples from bulk RNAseq and hypoxic regions within tumor from spatially resolved transcriptomics datasets. Moreover, application of the classifiers to histological sections from normal tissues suggest the presence of a hypoxic gene expression pattern in the kidney cortex not observed in other normoxic organs. Finally, tree classifiers described herein outperform traditional hypoxic gene signatures when compared against a wide range of datasets. This work describes a set of hypoxic gene signatures, structured as simple decision tress, that identify hypoxic samples and regions with high accuracy and can be applied to a broad variety of gene expression datasets and formats.

Cells adapt to environmental changes, including fluctuations in oxygen levels, through the induction of specific gene expression programs. To identify genes regulated by hypoxia at the transcriptional level, we pulse-labeled HUVEC cells with 4-thiouridine and sequenced nascent transcripts. Then, we searched genome-wide binding profiles from the ENCODE project for factors that correlated with changes in transcription and identified binding of several components of the Sin3A co-repressor complex, including SIN3A, SAP30 and HDAC1/2, proximal to genes repressed by hypoxia. SIN3A interference revealed that it participates in the downregulation of 75% of the hypoxia-repressed genes in endothelial cells. Unexpectedly, it also blunted the induction of 47% of the upregulated genes, suggesting a role for this corepressor in gene induction. In agreement, ChIP-seq experiments showed that SIN3A preferentially localizes to the promoter region of actively transcribed genes and that SIN3A signal was enriched in hypoxia-repressed genes, prior exposure to the stimulus. Importantly, SINA3 occupancy was not altered by hypoxia in spite of changes in H3K27ac signal. In summary, our results reveal a prominent role for SIN3A in the transcriptional response to hypoxia and suggest a model where modulation of the associated histone deacetylase activity, rather than its recruitment, determines the transcriptional output.