Investigating genotype–phenotype relationship of extreme neuropathic pain disorders in a UK national cohort

Abstract

The aims of our study were to use whole genome sequencing in a cross-sectional cohort of patients to identify new variants in genes implicated in neuropathic pain, to determine prevalence of known pathogenic variants, and understand the relationship between pathogenic variants and clinical presentation. Patients with extreme neuropathic pain phenotypes (both sensory loss and gain) were recruited from secondary care clinics in the UK, and underwent whole genome sequencing as part of National Institute for Health and Care Research (NIHR) Bioresource Rare Diseases project. A multi-disciplinary team assessed pathogenicity of rare variants in genes previously known to cause neuropathic pain disorders and exploratory analysis of research candidate genes was completed. Association testing for genes carrying rare variants was completed using the gene-wise approach of the combined burden and variance-component test SKAT-O. Patch clamp analysis was performed on transfected HEK293T cells for research candidate variants of genes encoding ion channels. The results include the following: (1) Medically actionable variants were found in 12% of study participants (205 recruited), including known pathogenic variants: SCN9A(ENST00000409672.1): c.2544T>C, p.Ile848Thr that causes inherited erythromelalgia, and SPTLC1(ENST00000262554.2):c.340T>G, p.Cys133Tr variant that causes hereditary sensory neuropathy type-1. (2) Clinically relevant variants were most common in voltage-gated sodium channels (Nav). (3) SCN9A(ENST00000409672.1):c.554G>A, pArg185His variant was more common in non-freezing cold injury participants than controls, and causes a gain of function of NaV1.7 after cooling (the environmental trigger for non-freezing cold injury). (4) Rare variant association testing showed a significant difference in distribution for genes NGF, KIF1A, SCN8A, TRPM8, KIF1A, TRPA1 and the regulatory regions of genes SCN11A, FLVCR1, KIF1A, and SCN9A between European participants with neuropathic pain and controls. (5) The TRPA1(ENST00000262209.4):c.515C>T, p.Ala172Val variant identified in participants with episodic somatic pain disorder demonstrated gain of channel function to agonist stimulation. Whole genome sequencing identified clinically relevant variants in over 10% of participants with extreme neuropathic pain phenotypes. The majority of these variants were found in ion channels. Combining genetic analysis with functional validation can lead to better understanding as to how rare variants in ion channels lead to sensory neuron hyper-excitability, and how cold, as an environmental trigger, interacts with the gain of function NaV1.7 p.Arg185His variant. Our findings highlight the role of ion channel variants in the pathogenesis of extreme neuropathic pain disorders, likely mediated through changes in sensory neuron excitability and interaction with environmental triggers.

A Machine Learning Enhanced EMS Mutagenesis Probability Map for Efficient Identification of Causal Mutations inCaenorhabditis elegans

“…Our preceding genetic screens isolated the E251K missense mutation within the motor domain of OSM-3 kinesin. This mutation parallels a pathogenic variant, E253K, identified in the KIF1A kinesin (37), mutations within which give rise to a spectrum of neurological disorders collectively recognized as KIF1A-Associated Neurological Disorder (KAND). The E253K mutation in KIF1A is hypothesized to destabilize the structure of the back door, thereby suppressing γ-phosphate release and subsequent ATP binding within the motor domain (38, 39).…”

Predicting “pain genes”: multi-modal data integration using probabilistic classifiers and interaction networks

“…We used a labelled dataset of known genes, out of which we have labelled 429 genes found in the Pain Genes Database (PGD) ( LaCroix-Fralish et al (2007 )) and Dolorisk Priority Group ( Themistocleous et al (2023 )) as Pain (P) and the remaining as Non-Pain (NP) genes (Figure 1B). Six classifiers, including random forest (RF), AdaBoost (Ada), gradient boost (GB), Gradient Boosted Trees (XGBoost); and Stacking and Voting ensembles were trained to classify pain/non-pain genes based on multiomics, genomic and network topological data (Figure 2) ( Ho ( 1995 ); Schapire ( 2013 ); Friedman ( 2001 ); Chen and Guestrin ( 2016 )).…”

“…The network can be annotated using information on known pain-associated genes from several sources: Dolorisk Priority Group, Human Pain Genetics Database, and the Pain Genes Database ( Themistocleous et al (2023 ); Meloto et al (2018 ); LaCroix-Fralish et al (2007 )). In addition, users can enrich these networks by using data from pain-focused gene expression studies to highlight genes that change expression in each condition or pairs of genes showing correlated expression patterns across different experiments.…”

“…Together, 429 “pain genes” from gold-standard, experimentally validated lists were used, including those from the Pain Genes Database (PGD) ( LaCroix-Fralish et al (2007 )) and from the Dolorisk Priority Group ( Themistocleous et al (2023 )) to label genes as Pain (P), the remaining known genes were labelled as Non-Pain (NP) genes. The PGD contains pain-related phenotypes (both acute and injury-induced) of transgenic mice, whereas the Dolorisk Priority Group includes genes shown to have a causal (Tier 1) role in human neuropathic pain based on casual variants in multiple, independent families.…”

Use of Mexiletine in Children: A Minireview

“…26 The emergence of new genetic entities linked to ion channel mutations in epilepsy and pain should also prompt clinicians to reconsider the usefulness of drugs acting on ion channels such as mexiletine for personalized therapeutic management. 41,42…”