Nav1.7 gain-of-function mutation I228M triggers age-dependent nociceptive insensitivity and C-LTMR dysregulation.

Gain-of-function mutations in Scn9a, which encodes the peripheral sensory neuron-enriched voltage-gated sodium channel Nav1.7, cause paroxysmal extreme pain disorder (PEPD), inherited erythromelalgia (IEM), and small fiber neuropathy (SFN). Conversely, loss-of-function mutations in the gene are linked to congenital insensitivity to pain (CIP). These mutations are evidence for a link between altered sodium conductance and neuronal excitability leading to somatosensory aberrations, pain, or its loss. Our previous work in young adult mice with the Nav1.7 gain-of-function mutation, I228M, showed the expected DRG neuron hyperexcitability, but unexpectedly the mice had normal mechanical and thermal behavioral sensitivity. We now show that with aging both male and female mice with this mutation unexpectedly develop a profound insensitivity to noxious heat and cold, as well skin lesions that span the body. Electrophysiology demonstrates that, in contrast to young mice, aged I228M mouse DRGs have a profound loss of sodium conductance and changes in activation and slow inactivation dynamics, representing a loss-of-function. Through RNA sequencing we explored how these age-related changes may produce the phenotypic changes and found a striking and specific decrease in C-low threshold mechanoreceptor- (cLTMR) associated gene expression, suggesting a potential contribution of this DRG neuron subtype to Nav1.7 dysfunction phenotypes. A GOF mutation in a voltage-gated channel can therefore produce over a prolonged time, highly complex and unexpected alterations in the nervous system beyond excitability changes.

Automated preclinical detection of mechanical pain hypersensitivity and analgesia.

ABSTRACT: The lack of sensitive and robust behavioral assessments of pain in preclinical models has been a major limitation for both pain research and the development of novel analgesics. Here, we demonstrate a novel data acquisition and analysis platform that provides automated, quantitative, and objective measures of naturalistic rodent behavior in an observer-independent and unbiased fashion. The technology records freely behaving mice, in the dark, over extended periods for continuous acquisition of 2 parallel video data streams: (1) near-infrared frustrated total internal reflection for detecting the degree, force, and timing of surface contact and (2) simultaneous ongoing video graphing of whole-body pose. Using machine vision and machine learning, we automatically extract and quantify behavioral features from these data to reveal moment-by-moment changes that capture the internal pain state of rodents in multiple pain models. We show that these voluntary pain-related behaviors are reversible by analgesics and that analgesia can be automatically and objectively differentiated from sedation. Finally, we used this approach to generate a paw luminance ratio measure that is sensitive in capturing dynamic mechanical hypersensitivity over a period and scalable for high-throughput preclinical analgesic efficacy assessment.

DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels.

Videos of animal behavior are used to quantify researcher-defined behaviors of interest to study neural function, gene mutations, and pharmacological therapies. Behaviors of interest are often scored manually, which is time-consuming, limited to few behaviors, and variable across researchers. We created DeepEthogram: software that uses supervised machine learning to convert raw video pixels into an ethogram, the behaviors of interest present in each video frame. DeepEthogram is designed to be general-purpose and applicable across species, behaviors, and video-recording hardware. It uses convolutional neural networks to compute motion, extract features from motion and images, and classify features into behaviors. Behaviors are classified with above 90% accuracy on single frames in videos of mice and flies, matching expert-level human performance. DeepEthogram accurately predicts rare behaviors, requires little training data, and generalizes across subjects. A graphical interface allows beginning-to-end analysis without end-user programming. DeepEthogram's rapid, automatic, and reproducible labeling of researcher-defined behaviors of interest may accelerate and enhance supervised behavior analysis. Code is available at: https://github.com/jbohnslav/deepethogram.

Dissecting the precise nature of itch-evoked scratching.

Itch is a discrete and irritating sensation tightly coupled to a drive to scratch. Acute scratching developed evolutionarily as an adaptive defense against skin irritants, pathogens, or parasites. In contrast, the itch-scratch cycle in chronic itch is harmful, inducing escalating itch and skin damage. Clinically and preclinically, scratching incidence is currently evaluated as a unidimensional motor parameter and believed to reflect itch severity. We propose that scratching, when appreciated as a complex, multidimensional motor behavior, will yield greater insight into the nature of itch and the organization of neural circuits driving repetitive motor patterns. We outline the limitations of standard measurements of scratching in rodent models and present new approaches to observe and quantify itch-evoked scratching. We argue that accurate quantitative measurements of scratching are critical for dissecting the molecular, cellular, and circuit mechanisms underlying itch and for preclinical development of therapeutic interventions for acute and chronic itch disorders.

Two independent mouse lines carrying the Nav1.7 I228M gain-of-function variant display dorsal root ganglion neuron hyperexcitability but a minimal pain phenotype.

ABSTRACT: Small-fiber neuropathy (SFN), characterized by distal unmyelinated or thinly myelinated fiber loss, produces a combination of sensory dysfunction and neuropathic pain. Gain-of-function variants in the sodium channel Nav1.7 that produce dorsal root ganglion (DRG) neuron hyperexcitability are present in 5% to 10% of patients with idiopathic painful SFN. We created 2 independent knock-in mouse lines carrying the Nav1.7 I228M gain-of-function variant, found in idiopathic SFN. Whole-cell patch-clamp and multielectrode array recordings show that Nav1.7 I228M knock-in DRG neurons are hyperexcitable compared with wild-type littermate-control neurons, but despite this, Nav1.7 I228M mice do not display mechanical or thermal hyperalgesia or intraepidermal nerve fiber loss in vivo. Therefore, although these 2 Nav1.7 I228M knock-in mouse lines recapitulate the DRG neuron hyperexcitability associated with gain-of-function mutations in Nav1.7, they do not recapitulate the pain or neuropathy phenotypes seen in patients. We suggest that the relationship between hyperexcitability in sensory neurons and the pain experienced by these patients may be more complex than previously appreciated and highlights the challenges in modelling channelopathy pain disorders in mice.

Gene Expression-Based Screen for Parkinson's Disease Identifies GW8510 as a Neuroprotective Agent.

We carried out a gene expression-based in silico screen in order to identify small molecules with gene-expression profiles that are anticorrelated with a gene-expression profile for Parkinson's disease (PD). We identified the cyclin-dependent kinase 2/5 (CDK2/5) inhibitor GW8510 as our most significant hit and characterized its effects in rodent MN9D cells and in human neuronal cells derived from induced pluripotent stem cells. GW8510 demonstrated neuroprotective ability in MN9D cells in the presence of 1-methyl-4-phenylpyridium (MPP(+)), a widely used neurotoxin model for Parkinson's disease. In order to delineate the nature and extent of GW8510's neuroprotective properties, we studied GW8510 in human neuronal cells in the context of various mechanisms of cellular stress. We found that GW8510 was protective against small-molecule mitochondrial and endoplasmic reticulum stressors. Our findings illustrate an approach to using small-molecule gene expression libraries to identify compounds with therapeutic potential in human diseases.