bioRxivNeurosciencePreprint
Sensory neuron dysfunction and hyperexcitability in dorsal root ganglia at disease onset in the SOD1G93A mouse model of ALS.
輝 西浦, Soju SekiiD, Takuhiro Kobayashi, Yoshihiro KitaokaiD, Sou Kawata, Toru YamamotoiD, Kazuaki MiyagawaiD, Toshihiro UchihashiiD, Yosuke Ohtake, Takahide ItokazuiD, Toshihide YamashitaiD, Masayuki OnoiD, Yusuke YokotaiD, Emiko Tanaka IsomuraiD, Susumu TanakaiD
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder traditionally characterized by motor neuron degeneration, but emerging evidence indicates sensory system involvement. Despite reports of sensory abnormalities in some patients, the molecular and functional alterations in dorsal root ganglion (DRG) neurons remain insufficiently characterized. We investigated DRG pathology at disease onset in 12-week-old SOD1G93A mice using an integrated transcriptomic, morphological, and electrophysiological approach. RNA sequencing of lumbar DRG identified 35 differentially expressed genes, predominantly upregulated, enriched in oxidative stress-related and phagosome pathways. Comparative analysis with motor neuron transcriptomes revealed distinct gene expression profiles, indicating sensory neuron-specific molecular responses. Immunohistochemistry demonstrated reduced soma diameter in both A- and C-fiber DRG neurons. Nav channel colocalization increased for Nav1.7 in A fibers and Nav1.8 in both fiber types, whereas Nav1.6 was unchanged. Whole-cell patch-clamp recordings showed depolarized resting membrane potential, increased spike amplitude, and enhanced repetitive firing in A-fiber neurons, consistent with hyperexcitability, while C fibers showed no significant functional changes. These findings demonstrate early molecular, structural, and functional alterations in primary sensory neurons in ALS, supporting pathology beyond motor neurons and identifying sensory neuron excitability as a potential therapeutic target.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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