bioRxivNeurosciencePreprint
m6A modification and prion-like domain proteins converge to dysregulate Neuronal RNA Granules in Alzheimer s disease
Soukayna BoulaassafreiD, Hassan AinaniiD, Abdellatif El KhayariiD, Yuyu SongiD, Rachid El FatimyiD
Abstract
Alzheimers disease (AD) is a deadly neurodegenerative disorder with no cure. It is associated with several dysregulated pathways, including axonal transport. The latter supplies synapses with several essential components, including proteins and mRNAs. A proportion of RNAs in neurons is transported from the soma to neuronal extensions along microtubules in highly organized structures, known as Neuronal RNA Granules (NRGs). NRGs have a heterogeneous composition of coding and non-coding RNAs, RNA-binding proteins (RBPs), and components of translational machinery. In this study, we investigate the potential involvement of NRGs in AD pathogenesis, with a particular focus on the N6-methyladenosine (mA), a key RNA modification, and prion-like domain (PrLD) proteins. Our in-silico analysis revealed that a significant portion of mRNAs in NRGs are likely to be highly methylated. Using transcriptomic data from AD brain, we identify dysregulation of key genes in the mA-methylation pathway (METTL3, FTO, YTHDF2/3, eIF3m) as well as PrLD-containing proteins associated with NRGs (STAU2, YBX1). We further observe aberrant expression of mA-methylated mRNAs within both NRGs and synapses. Gene Ontology analysis highlights disruptions in pathways related to NRGs and synaptic function. Together, our findings suggest that impaired NRGs homeostasis may represent a critical and previously underappreciated contributor to AD pathogenesis. By outlining the potential roles of mA and PrLD proteins in regulating NRGs, this work offers a new conceptual framework to better understand AD and identify NRGs as a potential therapeutic target. Finally, we propose a working model illustrating how dysregulation of NRGs homeostasis may drive neurodegeneration in AD.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 1
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