Walsh, D. M. | bioRxiv Science

bioRxiv2018

MicroRNA-132 provides neuroprotection for tauopathies via multiple signaling pathways

MicroRNAs (miRNA) regulate fundamental biological processes, including neuronal plasticity, stress response, and survival. Here we describe a neuroprotective function of miR-132, the miRNA most significantly down-regulated in Alzheimers disease. miR-132 protects mouse and human wild-type neurons and more vulnerable Tau-mutant primary neurons against amyloid {beta}-peptide (A{beta}) and glutamate excitotoxicity. It lowers the levels of total, phosphorylated, acetylated, and cleaved forms of Tau implicated in tauopathies, promotes neurite elongation and branching, and reduces neuronal death. Similarly, miR-132 attenuates PHF Tau pathology and neurodegeneration and enhances long-term potentiation in the P301S Tau transgenic mice. The neuroprotective effects are mediated by direct regulation of the Tau modifiers acetyltransferase EP300, kinase GSK3{beta}, RNA-binding protein Rbfox1, and proteases Calpain 2 and Caspases 3/7. These data suggest miR-132 as a master regulator of neuronal health and indicate that miR-132 supplementation could be of therapeutic benefit for the treatment of Tau-associated neurodegenerative disorders.

neuroscience↗

bioRxiv2016

INFERENCE OF CELL TYPE COMPOSITION FROM HUMAN BRAIN TRANSCRIPTOMIC DATASETS ILLUMINATES THE EFFECTS OF AGE, MANNER OF DEATH, DISSECTION, AND PSYCHIATRIC DIAGNOSIS

Psychiatric illness is unlikely to arise from pathology occurring uniformly across all cell types in affected brain regions. Despite this, transcriptomic analyses of the human brain have typically been conducted using macro-dissected tissue due to the difficulty of performing single-cell type analyses with donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary cortical cell types in previous publications. Using this database, we predicted the relative cell type composition for 833 human cortical samples using microarray or RNA-Seq data from the Pritzker Consortium (GSE92538) or publicly-available databases (GSE53987, GSE21935, GSE21138, CommonMind Consortium). These predictions were generated by averaging normalized expression levels across transcripts specific to each cell type using our R-package BrainInABlender (validated and publicly-released: https://github.com/hagenaue/BrainInABlender). Using this method, we found that the principal components of variation in the datasets strongly correlated with the neuron to glia ratio of the samples.\n\nThis variability was not simply due to dissection - the relative balance of brain cell types appeared to be influenced by a variety of demographic, pre- and post-mortem variables. Prolonged hypoxia around the time of death predicted increased astrocytic and endothelial gene expression, illustrating vascular upregulation. Aging was associated with decreased neuronal gene expression. Red blood cell gene expression was reduced in individuals who died following systemic blood loss. Subjects with Major Depressive Disorder had decreased astrocytic gene expression, mirroring previous morphometric observations. Subjects with Schizophrenia had reduced red blood cell gene expression, resembling the hypofrontality detected in fMRI experiments. Finally, in datasets containing samples with especially variable cell content, we found that controlling for predicted sample cell content while evaluating differential expression improved the detection of previously-identified psychiatric effects. We conclude that accounting for cell type can greatly improve the interpretability of transcriptomic data.

bioinformatics↗