Genes for hereditary sensory and autonomic neuropathies: a genotype–phenotype correlation

Hereditary sensory and autonomic neuropathies (HSAN) are clinically and genetically heterogeneous disorders characterized by axonal atrophy and degeneration, exclusively or predominantly affecting the sensory and autonomic neurons. So far, disease-associated mutations have been identified in seven genes: two genes for autosomal dominant (SPTLC1 and RAB7) and five genes for autosomal recessive forms of HSAN (WNK1/HSN2, NTRK1, NGFB, CCT5 and IKBKAP). We performed a systematic mutation screening of the coding sequences of six of these genes on a cohort of 100 familial and isolated patients diagnosed with HSAN. In addition, we screened the functional candidate gene NGFR (p75/NTR) encoding the nerve growth factor receptor. We identified disease-causing mutations in SPTLC1, RAB7, WNK1/HSN2 and NTRK1 in 19 patients, of which three mutations have not previously been reported. The phenotypes associated with mutations in NTRK1 and WNK1/HSN2 typically consisted of congenital insensitivity to pain and anhidrosis, and early-onset ulcero-mutilating sensory neuropathy, respectively. RAB7 mutations were only found in patients with a Charcot-Marie-Tooth type 2B (CMT2B) phenotype, an axonal sensory-motor neuropathy with pronounced ulcero-mutilations. In SPTLC1, we detected a novel mutation (S331F) corresponding to a previously unknown severe and early-onset HSAN phenotype. No mutations were found in NGFB, CCT5 and NGFR. Overall disease-associated mutations were found in 19% of the studied patient group, suggesting that additional genes are associated with HSAN. Our genotype–phenotype correlation study broadens the spectrum of HSAN and provides additional insights for molecular and clinical diagnosis

A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes

Background Human genetic research has implicated functional variants of more than one hundred genes in the modulation of persisting pain. Artificial intelligence and machine-learning techniques may combine this knowledge with results of genetic research gathered in any context, which permits the identification of the key biological processes involved in chronic sensitization to pain. MethodsResultsBased on published evidence, a set of 110 genes carrying variants reported to be associated with modulation of the clinical phenotype of persisting pain in eight different clinical settings was submitted to unsupervised machine-learning aimed at functional clustering. Subsequently, a mathematically supported subset of genes, comprising those most consistently involved in persisting pain, was analysed by means of computational functional genomics in the Gene Ontology knowledgebase. Clustering of genes with evidence for a modulation of persisting pain elucidated a functionally heterogeneous set. The situation cleared when the focus was narrowed to a genetic modulation consistently observed throughout several clinical settings. On this basis, two groups of biological processes, the immune system and nitric oxide signalling, emerged as major players in sensitization to persisting pain, which is biologically highly plausible and in agreement with other lines of pain research. ConclusionsSignificanceThe present computational functional genomics-based approach provided a computational systems-biology perspective on chronic sensitization to pain. Human genetic control of persisting pain points to the immune system as a source of potential future targets for drugs directed against persisting pain. Contemporary machine-learned methods provide innovative approaches to knowledge discovery from previous evidence. We show that knowledge discovery in genetic databases and contemporary machine-learned techniques can identify relevant biological processes involved in Persitent pain.Peer reviewe