bioRxivGenomicsPreprint
An Epigenetic Signature of Vulnerable Neurons is Under Selective Pressure Associated with Longevity Across Placental Mammals.
Ghada AbdelhadyiD, Qiao SuiD, Andrew Z. WangiD, Rajee GanesaniD, BaDoi N. PhaniD, Heather H. SestiliiD, Vijay Kiran CherupallyiD, Andreas R. PfenningiD
Abstract
Age is the primary risk factor for neurodegenerative diseases, which are characterized by cell-type-specific vulnerability. Yet brain-aging mechanisms remain unclear given the complex, interacting age-associated pathways across diverse neural cell types. Here, we dissect cell type- cell state-specific aging gene regulatory programs and their contribution to cellular vulnerability by leveraging epigenomics, AI methodology, and natural lifespan diversity across placental mammals. Applying the TACIT method, we associated lifespans of 240 placental mammals to the predicted open chromatin levels of over 3 million orthologous loci across 18 cortical cell types. We identified thousands of lifespan-associated open chromatin regions, enriched near genes associated with hallmarks of aging, which stratified greatly by cell type. For example, regions near mitochondrial genes showed differential selective pressure in long-lived species in energetically-demanding layer V ET neurons, while regions near inflammatory response genes were under selective pressure in glial populations. We next asked whether regions linked to vulnerable or resilient neurons in the human brain were under differential selective pressure in longer lived species. Using an adaptive representation learning approach, we decompose intrinsic aging programs from systemic effects in the prefrontal cortex and define an aging signature predictive of cell-type-specific vulnerability. In Alzheimer's disease, this intrinsic aging signature more strongly predicts vulnerability than systemic effects. Active regions in vulnerable neurons showed lower predicted activity in species with longer lifespans, suggesting selective pressure to down-regulate the vulnerability-associated networks. Overall, our findings argue against a single master regulator of aging, instead implicating different hallmarks across different cell types.
- organism: mouse
- organism: human
- organism: rat
- assay: ChIP-seq
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: cell type annotation
- research focus: benchmarking
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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