Transcriptional Response to Hypoxia: The Role of HIF-1-Associated Co-Regulators

Abstract

The Hypoxia Inducible Factor 1 (HIF-1) plays a major role in the cellular response to hypoxia by regulating the expression of many genes involved in adaptive processes that allow cell survival under low oxygen conditions. Adaptation to the hypoxic tumor micro-environment is also critical for cancer cell proliferation and therefore HIF-1 is also considered a valid therapeutical target. Despite the huge progress in understanding regulation of HIF-1 expression and activity by oxygen levels or oncogenic pathways, the way HIF-1 interacts with chromatin and the transcriptional machinery in order to activate its target genes is still a matter of intense investigation. Recent studies have identified several different HIF-1- and chromatin-associated co-regulators that play important roles in the general transcriptional activity of HIF-1, independent of its expression levels, as well as in the selection of binding sites, promoters and target genes, which, however, often depends on cellular context. We review here these co-regulators and examine their effect on the expression of a compilation of well-characterized HIF-1 direct target genes in order to assess the range of their involvement in the transcriptional response to hypoxia. Delineating the mode and the significance of the interaction between HIF-1 and its associated co-regulators may offer new attractive and specific targets for anticancer therapy.

Keywords: HIF-1; cancer; chromatin; hypoxia; transcriptional regulation.

Figure 1

Schematic representation of HIF-1α and its interacting co-regulators. Positive (+) and negative (−) effectors of the p300/CBP-HIF-1α interaction are also shown. Brackets indicate the interacting region of HIF-1α, in cases that this has been experimentally defined. Residues, modification of which is known to affect an interaction, are also indicated. Genes directly regulated by HIF-1 are shown in bold. See Table 1 and text for details and relevant references.

Figure 2

Heatmap of KEGG pathway analysis of common genes between JMJD1A, CDK8, TRIM28, ZMYND8 or NPM1 (as indicated) and LIST A (left panel) or LIST B (right panel). The -log10 FDR values for the various pathways were plotted for each of the overlapping gene sets. An FDR cut-off of 0.05 was used for statistical significance. Clustering was performed using the Nearest Point algorithm with Euclidean distance from SciPy [117]. The heatmaps were plotted using the python Plotly package (Plotly Technologies Inc. Collaborative data science, Montréal, QC, Canada, 2015. https://plot.ly accessed on 31 January 2023).

Figure 3

(A,B): Venn diagrams depicting the overlap between List A (A) or List B (B) genes and the five co-activator-dependent gene sets as indicated. (C,D) Dot plots of KEGG pathway analysis of List A (C) or List B (D) genes that do not overlap with any coregulator-dependent gene sets.