bioRxivNeurosciencePreprint
Modeling Huntington disease against age-related genes reveals CXXC4 as an epigenetic target to restore DRD1 striatal neuron activity
M. ArrietaiD, Francesca FarinaiD, Tamara Monteagudo Aboy, Megan L. Mair, Cloé Mendoza, Huy TraniD, Jeff Aaronson, Jim Rosinski, Lisa EllerbyiD, Emmanuel BrouilletiD, Jean‐Michel PeyriniD, Juan BotasiD, Christian NériiD, Lucile MégretiD
Abstract
Neurons adapt gene expression to counter aging, yet the mechanisms by which they harness age- related genes to resist neurodegenerative disease remain elusive. We found that transcriptionalaging inversion (TAGI) in the Drd1-expressing striatal neurons (Drd1 SNs) of Huntingtons disease (HD) knock-in (Hdh) mice exhibits discrete patterns against a TAG-like (TAGL) signature. Strikingly, TAGI dynamics may explain disease progression more accurately than TAGL. Moreover, in Drd1 SNs, genes affected by 3UTR accumulation during aging are predisposed to downregulation in aging and deregulation in Hdh mice. By integrating age-related 3UTR data and Hdh-mice data, we identified a CAG-repeat-dependent network of upregulated genes with compensatory potential. This network features (i) Atad-5, a P CNA unloader that modifies CAG expansion in human HD plasma samples, and (ii) CXXC4 (IDAX), an epigenetic regulator whose early-stage upregulation is lost as behavioral symptoms worsen in Hdh mice. Functionally, CXXC4 reduces the senescence marker p16INK4a and restores glutamate excitability in human HD iPS cell-derived SNs. Collectively, these findings suggest that Drd1 SN resilience capacity against HD relies on discrete age-related patterns and responses transiently activated in early disease stages. The reactivation of specific age-related genes such as CXXC4 may notably restore cortico-striatal function in HD.
- organism: mouse
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: cell type annotation
- research focus: drug response / drug discovery
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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