Disease Models & MechanismsNeurosciencePeer-reviewed journal article
A conserved epilepsy-associated gene co-expression module identifies increased metabolic rate as a shared pathomechanism
Jingyi LongiD, Spencer G. JonesiD, Ana SernaiD, Boyd van ReijmersdaliD, Franziska KampshoffiD, Sara AibariD, Patrik VerstrekeniD, Martijn A. HuyneniD, Kevin LüthyiD, Mireia Coll-TanéiD, Annette SchenckiD
Abstract
Epilepsy is a mechanistically complex, incompletely understood neurological disorder. To uncover novel converging mechanisms in epilepsy, we used Drosophila whole-brain single-cell RNA-sequencing to refine and characterize a previously proposed human epilepsy-associated gene co-expression network (GCN). We identified a conserved co-expressed module of 26 genes, which comprises fly orthologs of 13 epilepsy-associated genes and integrates synaptic and metabolic functions. Over one-third of the Drosophila pan-neuronal knockdown models targeting this module exhibited altered seizure-like behaviors in response to mechanical or heat stress. These recapitulated seizures associated with four epilepsy-associated genes, identified two novel epilepsy candidate genes, and three genes knockdown of which conferred seizure protection. Most knockdown models with altered seizure susceptibility showed changes in metabolic rate and levels of phosphorylated adenosine monophosphate-activated protein kinase (AMPK), a key regulator of cellular energy homeostasis. Enhancing AMPK activity increased seizure resistance in a dose-dependent manner. Our findings show that Drosophila single-cell expression data and behavior can aid functional validation of human GCNs and highlight a role for metabolism in modifying seizure susceptibility. SUMMARY STATEMENTIntegrating Drosophila single-cell RNA-sequencing data with seizure-like behavior and metabolic rate assays, we functionally characterized a human epilepsy-associated gene network, revealing metabolic regulation as a critical factor underlying seizure susceptibilities.
- organism: Drosophila
- assay: scRNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
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- 0.00
- Field percentile
- 11th percentile
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