Neural and oligodendrocyte precursor cells (NPCs and OPCs) in the subventricular zone (SVZ) of the brain contribute to oligodendrogenesis throughout life, in part due to direct regulation by chemokines. The role of the chemokine fractalkine is well established in microglia; however, the effect of fractalkine on SVZ precursor cells is unknown. We show that murine SVZ NPCs and OPCs express the fractalkine receptor (CX3CR1) and bind fractalkine. Exogenous fractalkine directly enhances OPC and oligodendrocyte genesis from SVZ NPCs in vitro. Infusion of fractalkine into the lateral ventricle of adult NPC lineage-tracing mice leads to increased newborn OPC and oligodendrocyte formation in vivo. We also show that OPCs secrete fractalkine and that inhibition of endogenous fractalkine signaling reduces oligodendrocyte formation in vitro. Finally, we show that fractalkine signaling regulates oligodendrogenesis in cerebellar slices ex vivo. In summary, we demonstrate a novel role for fractalkine signaling in regulating oligodendrocyte genesis from postnatal CNS precursor cells.
The emergence of precision oncology approaches has begun to inform clinical decision-making in diagnostic, prognostic, and treatment contexts. High-throughput technology has enabled machine learning algorithms to use the molecular characteristics of tumors to generate personalized therapies. However, precision oncology studies have yet to develop a predictive biomarker incorporating pan-cancer gene expression profiles to stratify tumors into similar drug sensitivity profiles. Here we show that a neural network with ten hidden layers accurately classifies pancancer cell lines into two distinct chemotherapeutic response groups based on a pan-drug dataset with 89.0% accuracy (AUC = 0.904). Using unsupervised clustering algorithms, we found a cohort of cell line gene expression data from the Genomics of Drug Sensitivity in Cancer could be clustered into two response groups with significant differences in pan-drug chemotherapeutic sensitivity. After applying the Boruta feature selection algorithm to this dataset, a deep learning model was developed to predict chemotherapeutic response groups. The model’s high classification efficacy validates our hypothesis that cell lines with similar gene expression profiles present similar pan-drug chemotherapeutic sensitivity. This finding provides evidence for the potential use of similar combinatorial biomarkers to select potent candidate drugs that maximize therapeutic response and minimize the cytotoxic burden. Future investigations should aim to recursively subcluster cell lines within the response clusters defined in this study to provide a higher resolution of potential patient response to chemotherapeutics.
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