bioRxivGeneticsPreprint
Integrated multiomics reveals inflammation-driven excessive erythrocytosis in subjects with Monge's disease
Priti AzadiD, Andrew B. CaldwelliD, Francisco C. VillafuerteiD, Shamieh BanihaniiD, Dan ZhouiD, Shankar SubramaniamiD, Gabriel George HaddadiD
Abstract
Monges disease, or Chronic Mountain Sickness (CMS), is a chronic high-altitude disorder characterized by hypoxia-induced excessive erythrocytosis (EE), elevating the risk of stroke and myocardial infarction. Using RNA-seq and ATAC-seq, we profiled iPSC-derived erythroid cells from CMS and non-CMS subjects under normoxia and hypoxia to identify statistically significant, disease-associated transcriptional and chromatin accessibility changes. RNA-seq revealed induction of inflammatory, stress, and erythropoiesis programs in CMS even under normoxia, including robust activation of JAK/STAT signaling, upregulation of heme metabolism and VEGF, and accelerated erythrocyte lineage commitment alongside repression of Notch and WNT/{beta}-catenin. Hypoxia amplified this dysregulated state, and critically, activated NF{kappa}B-driven inflammatory signaling together with canonical HIF targets. ATAC-seq revealed pronounced hypoxia-induced changes, with increased accessibility within inflammatory and erythrocyte lineage genes occurring concomitantly with decreased accessibility within pluripotency and ectodermal lineage genes. Pharmacological NF{kappa}B inhibition in CMS cells significantly reduced EE (p-value <0.0001), whereas NF{kappa}B activation in non-CMS cells was sufficient to drive EE (p-value <0.01), confirming the causal role inferred by our multiomics analyses. Collectively, our multiomics and functional experiments substantiate a coordinated chromatin-transcription paradigm favoring an inflammatory axis that, through hypoxia-driven NF{kappa}B activation, accelerates stress-induced erythroid commitment and underlies EE in CMS.
- organism: human
- assay: bulk RNA-seq
- assay: ATAC-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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