A dual role of microbiota type 17 immunity in tissue repair and pain.

In this commentary, we discuss the findings of Enamorado et al. who have, for the first time, demonstrated that immunity to the microbiota enhances repair of cutaneous sensory nerves and epithelial tissues following skin injury. Commensal-specific IL-17 producing CD4+ T helper cells have direct contact with injured sensory neurons, inducing multiple epithelial and neuronal repair genes. We speculate that an altered balance of T cell populations in the skin of people with chronic neuropathic pain may contribute to a reduction in neuronal repair and the consequent decease in intraepidermal nerve fibre density and persistent pain.

Nerve-myeloid cell interactions in persistent human pain: a reappraisal using updated cell subset classifications.

ABSTRACT: The past 20 years have seen a dramatic shift in our understanding of the role of the immune system in initiating and maintaining pain. Myeloid cells, including macrophages, dendritic cells, Langerhans cells, and mast cells, are increasingly implicated in bidirectional interactions with nerve fibres in rodent pain models. However, our understanding of the human setting is still poor. High-dimensional functional analyses have substantially changed myeloid cell classifications, with recently described subsets such as epidermal dendritic cells and DC3s unveiling new insight into how myeloid cells interact with nerve fibres. However, it is unclear whether this new understanding has informed the study of human chronic pain. In this article, we perform a scoping review investigating neuroimmune interactions between myeloid cells and peripheral nerve fibres in human chronic pain conditions. We found 37 papers from multiple pain states addressing this aim in skin, cornea, peripheral nerve, endometrium, and tumour, with macrophages, Langerhans cells, and mast cells the most investigated. The directionality of results between studies was inconsistent, although the clearest pattern was an increase in macrophage frequency across conditions, phases, and tissues. Myeloid cell definitions were often outdated and lacked correspondence with the stated cell types of interest; overreliance on morphology and traditional structural markers gave limited insight into the functional characteristics of investigated cells. We therefore critically reappraise the existing literature considering contemporary myeloid cell biology and advocate for the application of established and emerging high-dimensional proteomic and transcriptomic single-cell technologies to clarify the role of specific neuroimmune interactions in chronic pain.

Machine learning analysis of the UK Biobank reveals prognostic and diagnostic immune biomarkers for polyneuropathy and neuropathic pain in diabetes.

AIMS: We assessed the health data of 11,047 people with diabetes in the UK Biobank to rank 329 risk factors for diabetic polyneuropathy (DPN) and DPN with chronic neuropathic pain without a priori assumption. METHODS: The Integrated Disease Explanation and Risk Scoring (IDEARS) platform applies machine learning algorithms to multimodal data to determine individual disease risk, and rank risk factor importance using mean SHapley Additive exPlanations (SHAP) score. RESULTS: IDEARS models showed discriminative performances with AUC > 0.64. Lower socioeconomic status, being overweight, poor overall health, cystatin C, HbA1C, and immune activation marker, C-reactive protein (CRP), predict DPN risk. Neutrophils and monocytes were higher in males and lymphocytes lower in females with diabetes that develop DPN. Neutrophil-to-Lymphocyte Ratio (NLR) was increased and IGF-1 levels decreased in people with type 2 diabetes that later develop DPN. CRP was significantly elevated in those with DPN and chronic neuropathic pain compared to DPN without pain. CONCLUSIONS: Lifestyle factors and blood biomarkers predict the later development of DPN and may relate to DPN pathomechanisms. Our results are consistent with DPN as a disease involving systemic inflammation. We advocate for the use of these biomarkers clinically to predict future DPN risk and improve early diagnosis.