The Role of Voltage-Gated Sodium Channels in Pain Signaling

Abstract

Acute pain signaling has a key protective role and is highly evolutionarily conserved. Chronic pain, however, is maladaptive, occurring as a consequence of injury and disease, and is associated with sensitization of the somatosensory nervous system. Primary sensory neurons are involved in both of these processes, and the recent advances in understanding sensory transduction and human genetics are the focus of this review. Voltage-gated sodium channels (VGSCs) are important determinants of sensory neuron excitability: they are essential for the initial transduction of sensory stimuli, the electrogenesis of the action potential, and neurotransmitter release from sensory neuron terminals. Nav1.1, Nav1.6, Nav1.7, Nav1.8, and Nav1.9 are all expressed by adult sensory neurons. The biophysical characteristics of these channels, as well as their unique expression patterns within subtypes of sensory neurons, define their functional role in pain signaling. Changes in the expression of VGSCs, as well as posttranslational modifications, contribute to the sensitization of sensory neurons in chronic pain states. Furthermore, gene variants in Nav1.7, Nav1.8, and Nav1.9 have now been linked to human Mendelian pain disorders and more recently to common pain disorders such as small-fiber neuropathy. Chronic pain affects one in five of the general population. Given the poor efficacy of current analgesics, the selective expression of particular VGSCs in sensory neurons makes these attractive targets for drug discovery. The increasing availability of gene sequencing, combined with structural modeling and electrophysiological analysis of gene variants, also provides the opportunity to better target existing therapies in a personalized manner.

Spinal cord motor neuron plasticity accompanies second‐degree burn injury and chronic pain

“…Injured peripheral nerve afferents have been shown to misexpress sodium channels following burn‐ or mechanical nerve injury. Abnormal sodium channel expression can significantly alter nociceptive excitability associated with painful conditions (Bennett, Clark, Huang, Waxman, & Dib‐Hajj, ; Eaton, ; Ji & Strichartz, ; Samad et al, ; Shields et al, ; Wang et al, ). In the spinal reflex pathway, injured primary afferents may also potentiate changes within the central motor system (Bandaru et al, ; D'Amico, Condliffe, Martins, Bennett, & Gorassini, ; Sjolund, ).…”

Slowing of the Time Course of Acidification Decreases the Acid-Sensing Ion Channel 1a Current Amplitude and Modulates Action Potential Firing in Neurons

“…Nine different Na v subtypes are known, which have distinct kinetics and voltage dependence. Especially some of the Na v subtypes expressed in sensory neurons of the peripheral nervous system deviate in their voltage dependence and kinetics strongly from other Na v s (Bennett et al, 2019). To determine how such differences in Na v current properties affect AP induction, a series of additional simulations with the HH-based neuronal model was carried out, in which the kinetics and voltage dependence FIGURE 7 | Analysis of acid-induced action potential firing in cultured cortical neurons.…”

Using Bifurcation Theory for Exploring Pain

“…Several other ion channels in addition to the four channels addressed in this paper are present in a small DRG neuron as well. For example, Na v 1.9, also plays a role in the pain sensation [15,10,3] and is present in small DRG neurons but was not included in this model. Furthermore, neuron activity is not only a function of electrochemical reactions but also of biochemical reactions that occur within a neuron and at the synapse, where biochemicals in the form of neurotransmitters participate in synaptic transmission.…”

“…We further focus on finding specific kinetic parameters in sodium channels that can shift the bifurcation points. This is because several mutations in sodium channels have been found to be associated with loss or gain of pain sensation [3,15,10]. Most of the previous work in this field is based on experiments and computational modeling.…”