Background Multiple Sclerosis (MS) is a chronic inflammatory disease and a leading cause of progressive neurological disability among young adults. DNA methylation, which intersects genes and environment to control cellular functions on a molecular level, may provide insights into MS pathogenesis. Methods We measured DNA methylation in CD4 + T cells ( n = 31), CD8 + T cells ( n = 28), CD14 + monocytes ( n = 35) and CD19 + B cells ( n = 27) from relapsing-remitting (RRMS), secondary progressive (SPMS) patients and healthy controls (HC) using Infinium HumanMethylation450 arrays. Monocyte ( n = 25) and whole blood ( n = 275) cohorts were used for validations. Findings B cells from MS patients displayed most significant differentially methylated positions (DMPs), followed by monocytes, while only few DMPs were detected in T cells. We implemented a non-parametric combination framework (omicsNPC) to increase discovery power by combining evidence from all four cell types. Identified shared DMPs co-localized at MS risk loci and clustered into distinct groups. Functional exploration of changes discriminating RRMS and SPMS from HC implicated lymphocyte signaling, T cell activation and migration. SPMS-specific changes, on the other hand, implicated myeloid cell functions and metabolism. Interestingly, neuronal and neurodegenerative genes and pathways were also specifically enriched in the SPMS cluster. Interpretation We utilized a statistical framework (omicsNPC) that combines multiple layers of evidence to identify DNA methylation changes that provide new insights into MS pathogenesis in general, and disease progression, in particular. Fund This work was supported by the Swedish Research Council, Stockholm County Council, AstraZeneca, European Research Council, Karolinska Institutet and Margaretha af Ugglas Foundation.
AMPA-type glutamate receptors mediate fast, excitatory neurotransmission in the brain, and their concentrations at synapses are important determinants of synaptic strength. We investigated the post-transcriptional regulation of GluA2, the calcium-impermeable AMPA receptor subunit, by examining the subcellular distribution of its mRNA and evaluating its translational regulation by microRNA in cultured mouse hippocampal neurons. Using computational approaches, we identified a conserved microRNA-124 (miR-124) binding site in the 3'UTR of GluA2 and demonstrated that miR-124 regulated the translation of GluA2 mRNA reporters in a sequence-specific manner in luciferase assays. While we hypothesized that this regulation might occur in dendrites, our biochemical and fluorescent in situ hybridization (FISH) data indicate that GluA2 mRNA does not localize to dendrites or synapses of mouse hippocampal neurons. In contrast, we detected significant concentrations of miR-124 in dendrites. Overexpression of miR-124 in dissociated neurons results in a 30% knockdown of GluA2 protein, as measured by immunoblot and quantitative immunocytochemistry, without producing any changes in GluA2 mRNA concentrations. While total GluA2 concentrations are reduced, we did not detect any changes in the concentration of synaptic GluA2. We conclude from these results that miR-124 interacts with GluA2 mRNA in the cell body to downregulate translation. Our data support a model in which GluA2 is translated in the cell body and subsequently transported to neuronal dendrites and synapses, and suggest that synaptic GluA2 concentrations are modified primarily by regulated protein trafficking rather than by regulated local translation.
Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To fill this gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pair-rule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues.
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