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GTP cyclohydrolase (GCH1) governs de novo synthesis of the enzyme cofactor, tetrahydrobiopterin (BH4), which is essential for biogenic amine production, bioactive lipid metabolism and redox coupling of nitric oxide synthases. Overproduction of BH4 via upregulation of GCH1 in sensory neurons is associated with nociceptive hypersensitivity in rodents, and neuron‐specific GCH1 deletion normalizes nociception. The translational relevance is revealed by protective polymorphisms of GCH1 in humans, which are associated with a reduced chronic pain. Because myeloid cells constitute a major non‐neuronal source of BH4 that may contribute to BH4‐dependent phenotypes, we studied here the contribution of myeloid‐derived BH4 to pain and itch in lysozyme M Cre‐mediated GCH1 knockout (LysM‐GCH1−/−) and overexpressing mice (LysM‐GCH1‐HA). Unexpectedly, knockout or overexpression in myeloid cells had no effect on nociceptive behaviour, but LysM‐driven GCH1 knockout reduced, and its overexpression increased the scratching response in Compound 48/80 and hydroxychloroquine‐evoked itch models, which involve histamine and non‐histamine dependent signalling pathways. Mechanistically, GCH1 overexpression increased BH4, nitric oxide and hydrogen peroxide, and these changes were associated with increased release of histamine and serotonin and degranulation of mast cells. LysM‐driven GCH1 knockout had opposite effects, and pharmacologic inhibition of GCH1 provided even stronger itch suppression. Inversely, intradermal BH4 provoked scratching behaviour in vivo and BH4 evoked an influx of calcium in sensory neurons. Together, these loss‐ and gain‐of‐function experiments suggest that itch in mice is contributed by BH4 release plus BH4‐driven mediator release from myeloid immune cells, which leads to activation of itch‐responsive sensory neurons.
Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent self-organizing maps of neuronal networks, swarm intelligence and Minimum Curvilinear Embedding, a cluster structure was found in the input data space comprising serum concentrations of d = 43 different lipid-markers of various classes. The structure coincided largely with the clinical diagnosis, indicating that the data provide a basis for the creation of a biomarker (classifier). This was subsequently assessed using supervised machine-learning, implemented as random forests and computed ABC analysis-based feature selection. Bayesian statistics-based biomarker creation was used to map the diagnostic classes of either MS patients (n = 102) or healthy subjects (n = 301). Eight lipid-markers passed the feature selection and comprised GluCerC16, LPA20:4, HETE15S, LacCerC24:1, C16Sphinganine, biopterin and the endocannabinoids PEA and OEA. A complex classifier or biomarker was developed that predicted MS at a sensitivity, specificity and accuracy of approximately 95% in training and test data sets, respectively. The present successful application of serum lipid marker concentrations to MS data is encouraging for further efforts to establish an MS biomarker based on serum lipidomics.