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Differential regulation of autophagy during metabolic stress in astrocytes and neurons.

ABSTRACT: Macroautophagy/autophagy is a key homeostatic process that targets cytoplasmic components to the lysosome for breakdown and recycling. Autophagy plays critical roles in glia and neurons that affect development, functionality, and viability of the nervous system. The mechanisms that regulate autophagy in glia and neurons, however, are poorly understood. Here, we define the molecular underpinnings of autophagy in primary cortical astrocytes in response to metabolic stress, and perform a comparative study in primary hippocampal neurons. We find that inducing metabolic stress by nutrient deprivation or pharmacological inhibition of MTOR (mechanistic target of rapamycin kinase) robustly activates autophagy in astrocytes. While both paradigms of metabolic stress dampen MTOR signaling, they affect the autophagy pathway differently. Further, we find that starvation-induced autophagic flux is dependent on the buffering system of the starvation solution. Lastly, starvation conditions that strongly activate autophagy in astrocytes have less pronounced effects on autophagy in neurons. Combined, our study reveals the complexity of regulating autophagy in different paradigms of metabolic stress, as well as in different cell types of the brain. Our findings raise important implications for how neurons and glia may collaborate to maintain homeostasis in the brain.

Abbreviations

ACSF: artificial cerebrospinal fluid; baf A₁: bafilomycin A₁; EBSS: earle's balanced salt solution; GFAP: glial fibrillary acidic protein; Glc: glucose; GM: glial media; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3; MTOR: mechanistic target of rapamycin kinase; p-RPS6: phospho-RPS6; p-ULK1: phospho-ULK1; RPS6: ribosomal protein S6; SQSTM1/p62: sequestosome 1; ULK1: unc-51-like kinase 1.

SUBMITTER: Kulkarni A

PROVIDER: S-EPMC8386594 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCellular identity is determined partly by cell type-specific epigenomic profiles that regulate gene expression. In neuroscience, there is a pressing need to isolate and characterize the epigenomes of specific CNS cell types in health and disease. In this study, we developed an in vivo tagging mouse model (Camk2a-NuTRAP) for paired isolation of neuronal DNA and RNA without cell sorting and then used this model to assess epigenomic regulation, DNA modifications in particular, of gene expression between neurons and glia.ResultsAfter validating the cell-specificity of the Camk2a-NuTRAP model, we performed TRAP-RNA-Seq and INTACT-whole genome oxidative bisulfite sequencing (WGoxBS) to assess the neuronal translatome and epigenome in the hippocampus of young mice (4 months old). WGoxBS findings were validated with enzymatic methyl-Seq (EM-Seq) and nanopore sequencing. Comparing neuronal data to microglial and astrocytic data from NuTRAP models, microglia had the highest global mCG levels followed by astrocytes and then neurons, with the opposite pattern observed for hmCG and mCH. Differentially modified regions between cell types were predominantly found within gene bodies and distal intergenic regions, rather than proximal promoters. Across cell types there was a negative correlation between DNA modifications (mCG, mCH, hmCG) and gene expression at proximal promoters. In contrast, a negative correlation of gene body mCG and a positive relationship between distal promoter and gene body hmCG with gene expression was observed. Furthermore, we identified a neuron-specific inverse relationship between mCH and gene expression across promoter and gene body regions.ConclusionsNeurons, astrocytes, and microglia demonstrate different genome-wide levels of mCG, hmCG, and mCH that are reproducible across analytical methods. However, modification-gene expression relationships are conserved across cell types. Enrichment of differential modifications across cell types in gene bodies and distal regulatory elements, but not proximal promoters, highlights epigenomic patterning in these regions as potentially greater determinants of cell identity. These findings also demonstrate the importance of differentiating between mC and hmC in neuroepigenomic analyses, as up to 30% of what is conventionally interpreted as mCG can be hmCG, which often has a different relationship to gene expression than mCG.

Project description:BackgroundSmall extracellular vesicles (sEVs) mediate intercellular communication by transferring RNA, proteins, and lipids to recipient cells. These cargo molecules are selectively loaded into sEVs and mirror the physiological state of the donor cells. Given that sEVs can cross the blood-brain barrier and their composition can change in neurological disorders, the molecular signatures of sEVs in circulation can be potential disease biomarkers. Characterizing the molecular composition of sEVs from different cell types is an important first step in determining which donor cells contribute to the circulating sEVs.MethodsCell culture supernatants from primary mouse cortical neurons and astrocytes were used to purify sEVs by differential ultracentrifugation and sEVs were characterized using nanoparticle tracking analysis, transmission electron microscopy and western blot. RNA sequencing was used to determine differential expression and loading patterns of miRNAs in sEVs released by primary neurons and astrocytes. Motif analysis was conducted on enriched miRNAs in sEVs and their respective donor cells.ResultsSequencing total cellular RNA, and miRNAs from sEVs isolated from culture media of postnatal mouse cortical neurons and astrocytes revealed a distinct profile between sEVs and their corresponding cells. Though the total number of detected miRNAs in astrocytes was greater than neurons, neurons expressed more sEV-associated miRNAs than astrocytes. Only 20.7% of astrocytic miRNAs were loaded into sEVs, while 41.0% of neuronal miRNAs were loaded into sEVs, suggesting differences in the cellular sorting mechanisms. We identified short RNA sequence motifs, or EXOmotifs, on the miRNAs that were differentially loaded or excluded from sEVs. A sequence motif GUAC was enriched in astrocytic sEVs. miRNAs preferably retained in neurons or astrocytes had a similar RNA motif CACACA, suggesting a cell-type-independent mechanism to maintain cellular miRNAs. mRNAs of five RNA-binding proteins associated with passive or active RNA sorting into sEVs were differentially expressed between neurons and astrocytes, one of which, major vault protein was higher in astrocytes than in neurons and detected in astrocytic sEVs.ConclusionsOur studies suggest differences in RNA sorting into sEVs. These differences in miRNA signatures can be used for determining the cellular sources of sEVs altered in neurological disorders. Video abstract.

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Differential regulation of autophagy during metabolic stress in astrocytes and neurons.

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