Expression profiling by high throughput sequencing
Summary
Background: Hypoxia is pervasive in cancer and other diseases. Cells sense and adapt to hypoxia by activating hypoxia-inducible transcription factors (HIFs), but it is still an outstanding question why cell types differ in their transcriptional response to hypoxia.
Results: Here, we report that HIFs fail to bind CpG dinucleotides that are methylated in their consensus binding sequence, both in in vitro biochemical binding assays and in vivo studies of differentially methylated isogenic cell lines. Based on in silico structural modelling, we show that 5-methylcytosine indeed causes steric hindrance in the HIF binding pocket. A model wherein cell-type-specific methylation landscapes, as laid-down by the differential expression and binding of other transcription factors under normoxia control cell-type-specific hypoxia responses is observed. We also discover ectopic HIF binding sites in repeat regions which are normally methylated. Genetic and pharmacological DNA demethylation, but also cancer-associated DNA hypomethylation, expose these binding sites, inducing HIF-dependent expression of cryptic transcripts. In line with such cryptic transcripts being more prone to cause double-stranded RNA and viral mimicry, we observe low DNA methylation and high cryptic transcript expression in tumours with high immune checkpoint expression, but not in tumours with low immune checkpoint expression, where they would compromise tumour immunotolerance. In a low-immunogenic tumour model, DNA demethylation upregulates cryptic transcript expression in a HIF-dependent manner, causing immune activation and reducing tumour growth.
Conclusions: Our data elucidate the mechanism underlying cell-type specific responses to hypoxia, and suggest DNA methylation and hypoxia to underlie tumour immunotolerance.
Overall design
Examination of differentially expressed genes upon exposure to 0.5% O2 in 3 human cancer cell lines, one demethylated cancer cell line and mouse Hif1b-WT and Hif1b-KO cell lines by RNA-seq
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