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A hallmark of several major neurological diseases is neuronal cell death. In addition to this primary pathology, secondary injury is seen in connected brain regions in which neurons not directly affected by the disease are denervated. These transneuronal effects on the network contribute considerably to the clinical symptoms. Since denervated neurons are viable, they are attractive targets for intervention. Therefore, we studied the role of Sphingosine-1-phosphate (S1P)-receptor signaling, the target of Fingolimod (FTY720), in denervation-induced dendritic atrophy. The entorhinal denervation in vitro model was used to assess dendritic changes of denervated mouse dentate granule cells. Live-cell microscopy of GFP-expressing granule cells in organotypic entorhino-hippocampal slice cultures was employed to follow individual dendritic segments for up to 6 weeks after deafferentation. A set of slice cultures was treated with FTY720 or the S1P-receptor (S1PR) antagonist VPC23019. Lesion-induced changes in S1P (mass spectrometry) and S1PR-mRNA levels (laser microdissection and qPCR) were determined. Denervation caused profound changes in dendritic stability. Dendritic elongation and retraction events were markedly increased, resulting in a net reduction of total dendritic length (TDL) during the first 2 weeks after denervation, followed by a gradual recovery in TDL. These changes were accompanied by an increase in S1P and S1PR1- and S1PR3-mRNA levels, and were not observed in slice cultures treated with FTY720 or VPC23019. We conclude that inhibition of S1PR signaling prevents dendritic destabilization and denervation-induced dendrite loss. These results suggest a novel neuroprotective effect for pharmaceuticals targeting neural S1PR pathways.
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.