Itch is thought to represent the peculiar response to stimuli conveyed by somatosensory pathways shared with pain through the activation of specific neurons and receptors. It can occur in association with dermatological, systemic and neurological diseases, or be the side effect of certain drugs. However, some patients suffer from chronic idiopathic itch that is frequently ascribed to psychological distress and for which no biomarker is available to date. We investigated three multigenerational families, one of which diagnosed with joint hypermobility syndrome/Ehlers-Danlos syndrome hypermobility type (JHS/EDS-HT), characterized by idiopathic chronic itch with predominantly proximal distribution. Skin biopsy was performed in all eight affected members and revealed in six of them reduced intraepidermal nerve fibre density consistent with small fibre neuropathy. Whole exome sequencing identified two COL6A5 rare variants co-segregating with chronic itch in eight affected members and absent in non-affected members, and in one unrelated sporadic patient with type 1 painless diabetic neuropathy and chronic itch. Two families and the diabetic patient carried the nonsense c.6814G>T (p.Glu2272*) variant and another family carried the missense c.6486G>C (p.Arg2162Ser) variant. Both variants were predicted as likely pathogenic by in silico analyses. The two variants were rare (minor allele frequency < 0.1%) in 6271 healthy controls and absent in 77 small fibre neuropathy and 167 JHS/EDS-HT patients without itch. Null-allele test on cDNA from patients’ fibroblasts of both families carrying the nonsense variant demonstrated functional haploinsufficiency due to activation of nonsense mediated RNA decay. Immunofluorescence microscopy and western blotting revealed marked disorganization and reduced COL6A5 synthesis, respectively. Indirect immunofluorescence showed reduced COL6A5 expression in the skin of patients carrying the nonsense variant. Treatment with gabapentinoids provided satisfactory itch relief in the patients carrying the mutations. Our findings first revealed an association between COL6A5 gene and familiar chronic itch, suggesting a new contributor to the pathogenesis of neuropathic itch and identifying a new candidate therapeutic target.

Neuropathic pain (NP) is a typical symptom of peripheral nerve disorders, including painful neuropathy. The biological mechanisms that control ion channels are important for many cell activities and are also therapeutic targets. Disruption of the cellular mechanisms that govern ion channel activity can contribute to pain pathophysiology. The voltage-gated sodium channel (VGSC) is the most researched ion channel in terms of NP; however, VGSC impairment is detected in only <20% of painful neuropathy patients. Here, we discuss the potential role of the other peripheral ion channels involved in sensory signaling (transient receptor potential cation channels), neuronal excitation regulation (potassium channels), involuntary action potential generation (hyperpolarization-activated cyclic nucleotide-gated channels), thermal pain (anoctamins), pH modulation (acid sensing ion channels), and neurotransmitter release (calcium channels) related to pain and their prospective role as therapeutic targets for painful neuropathy.

A personalized approach is strongly advocated for treatment selection in Multiple Sclerosis patients due to the high number of available drugs. Machine learning methods proved to be valuable tools in the context of precision medicine. In the present work, we applied machine learning methods to identify a combined clinical and genetic signature of response to fingolimod that could support the prediction of drug response. Two cohorts of fingolimod-treated patients from Italy and France were enrolled and divided into training, validation, and test set. Random forest training and robust feature selection were performed in the first two sets respectively, and the independent test set was used to evaluate model performance. A genetic-only model and a combined clinical–genetic model were obtained. Overall, 381 patients were classified according to the NEDA-3 criterion at 2 years; we identified a genetic model, including 123 SNPs, that was able to predict fingolimod response with an AUROC= 0.65 in the independent test set. When combining clinical data, the model accuracy increased to an AUROC= 0.71. Integrating clinical and genetic data by means of machine learning methods can help in the prediction of response to fingolimod, even though further studies are required to definitely extend this approach to clinical applications

Objective: To investigate the role of known multiple sclerosis (MS)-associated genetic variants in MS familial aggregation, clinical expression, and accuracy of disease prediction in sporadic and familial cases. Methods: A total of 1,443 consecutive patients were screened for MS and familial autoimmune history in a hospital-based Italian cohort. Among them, 461 sporadic and 93 familial probands were genotyped for 107 MS-associated polymorphisms. Their effect sizes were combined to calculate the weighted genetic risk score (wGRS). Results: Family history of MS was reported by 17.2% of probands, and 33.8% reported a familial autoimmune disorder, with autoimmune thyroiditis and psoriasis being the most frequent. No difference in wGRS was observed between sporadic and familial MS cases. In contrast, a lower wGRS was observed in probands with greater familial aggregation (>1 first-degree relative or >2 relatives with MS) (p = 0.03). Also, female probands of familial cases with greater familial aggregation had a lower wGRS than sporadic cases (p = 0.0009) and male probands of familial cases (p = 0.04). An inverse correlation between wGRS and age at onset was observed (p = 0.05). The predictive performance of the genetic model including all known MS variants was modest but greater in sporadic vs familial cases (area under the curve = 0.63 and 0.57). Conclusions: Additional variants outside the known MS-associated loci, rare variants, and/or environmental factors may explain disease occurrence within families; in females, hormonal and epigenetic factors probably have a predominant role in explaining familial aggregation. The inclusion of these additional factors in future versions of aggregated genetic measures could improve their predictive ability.

Multiple sclerosis (MS) is an inflammatory neurodegenerative disorder of the central nervous system (CNS). The migration of immune cells into the CNS is essential for its development, and plasma membrane molecules play an important role in triggering and maintaining the inflammation. We previously identified ninjurin2, a plasma membrane protein encoded by NINJ2 gene, as involved in the occurrence of relapse under Interferon-β treatment in MS patients. The aim of the present study was to investigate the involvement of NINJ2 in inflammatory conditions and in the migration of monocytes through the blood–brain barrier (BBB). We observed that NINJ2 is downregulated in monocytes and in THP-1 cells after stimulation with the pro-inflammatory cytokine LPS, while in hCMEC/D3 cells, which represent a surrogate of the BBB, LPS stimulation increases its expression. We set up a transmigration assay using an hCMEC/D3 transwell-based model, finding a higher transmigration rate of monocytes from MS subjects compared to healthy controls (HCs) in the case of an activated hCMEC/D3 monolayer. Moreover, a positive correlation between NINJ2 expression in monocytes and monocyte migration rate was observed. Overall, our results suggest that ninjurin2 could be involved in the transmigration of immune cells into the CNS in pro-inflammatory conditions. Further experiments are needed to elucidate the exact molecular mechanisms.