bioRxivCancer Biology
Hypoxia induces transcriptional and translational downregulation of the type I interferon (IFN) pathway in multiple cancer cell types
A. Miar, E. Arnaiz, E. Bridges, S. Beedie, A. P. Cribbs, D. J. Downes, R. Beagrie, J. Rehwinkel, A. L. Harris
Abstract
Hypoxia is a common phenomenon in solid tumours and is considered a hallmark of cancer. Increasing evidence shows that hypoxia promotes local immune suppression. Type I IFN is involved in supporting cytotoxic T lymphocytes by stimulating the maturation of dendritic cells (DCs) and enhancing their capacity to process and present antigens. However, there is little information about the relationship between hypoxia and the type I interferon (IFN) pathway, which comprises the sensing of double-stranded RNA and DNA (dsRNA/dsDNA), followed by IFN/{beta} secretion and transcription activation of IFN-stimulated genes (ISGs). The aims of this study were to determine both the effect and mechanisms of hypoxia on the I IFN pathway in breast cancer.\n\nThere was a downregulation of the type I IFN pathway expression at mRNA and protein level in cancer cell lines under hypoxia in vitro and in vivo in xenografts. This pathway was suppressed at each level of signalling, from the dsRNA sensors (RIG-I, MDA5), the adaptor (MAVS), transcription factors (IRF3, IRF7, STAT1) and several ISGs (RIG-I, IRF7, STAT1, ADAR-p150). There was also lower IFN secretion under hypoxic conditions. HIF1 and HIF2 regulation of gene expression did not explain most of the effects. However, ATAC-Seq data revealed that in hypoxia peaks with STAT1 and IRF3 motifs had decreased accessibility.\n\nThus hypoxia leads to an overall 50% downregulation of the type I IFN pathway due to repressed transcription and lower chromatin accessibility in a HIF1/2-independent manner, which could contribute to immunosuppression in hypoxic tumours.
- organism: human
- assay: ATAC-seq
- assay: bulk RNA-seq
- research focus: chromatin & epigenetic regulation
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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