A high-fat diet changes astrocytic metabolism to enhance synaptic plasticity and promote exploratory behavior

Popov, A.; Brazhe, N.; Fedotova, A.; Tiaglik, A.; Bychkov, M.; Morozova, K.; Brazhe, A.; Aronov, D.; Lyukmanova, E.; Lazareva, N.; Li, L.; Verkhratsky, A.; Semyanov, A.

doi:10.1101/2021.11.03.467141

bioRxiv · 10.1101/2021.11.03.467141

A high-fat diet changes astrocytic metabolism to enhance synaptic plasticity and promote exploratory behavior

Popov, A.·Brazhe, N.·Fedotova, A.·Tiaglik, A.·Bychkov, M.·Morozova, K.·Brazhe, A.·Aronov, D.·Lyukmanova, E.·Lazareva, N.·Li, L.·Verkhratsky, A.·Semyanov, A.

View original source ↗Export metadata ↓

Abstract

A high-fat diet (HFD) is generally considered to negatively influence the body, the brain, and cognitive abilities. On the other hand, fat and fatty acids are essential for nourishing and constructing brain tissue. Astrocytes are central for lipolysis and fatty acids metabolism. Here we show that exposure of young mice to one month of HFD elevates lipid content and increases the relative amount of reduced cytochromes in astrocytes but not in neurons. Metabolic changes were paralleled with an enlargement of astrocytic territorial domains due to an increased outgrowth of branches and leaflets. Astrocyte remodeling was associated with an increase in expression of ezrin and with no changes in glial fibrillary acidic protein (GFAP), glutamate transporter-1 (GLT-1), and glutamine synthetase (GS). Such physiological (non-reactive) enlargement of astrocytes in the brain active milieu promoted glutamate clearance and long-term potentiation. These changes translated into improved exploratory behavior. Thus, dietary fat intake is not invariably harmful and might exert beneficial effects depending on the biological context. In BriefA high-fat diet stimulates the metabolism and growth of astrocytes, which improves glutamate clearance, synaptic plasticity, and exploratory behavior in young mice. Thus, dietary fat arguably is an essential component of the diet for children and young adults, supporting the optimal development of the brain. HighlightsO_LIExposure of young mice to a high-fat diet elevated lipid content and increased amount of reduced cytochromes in astrocytes but not in neurons. C_LIO_LIMetabolic changes were paralleled with an enlargement of astrocytic territorial domains due to an increased outgrowth of branches and leaflets. C_LIO_LIAstrocytic enlargement was associated with increased expression of ezrin but not GFAP, hence was not reactive but physiological C_LIO_LIExpansion of astrocytes in the brain active milieu improved glutamate clearance and long-term potentiation. C_LIO_LIThe high-fat diet improved exploratory behavior in young mice. C_LI

Explore related subjects

neuroscience Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Popov, A., Brazhe, N., Fedotova, A., Tiaglik, A., Bychkov, M., Morozova, K., Brazhe, A., Aronov, D., Lyukmanova, E., Lazareva, N., Li, L., Verkhratsky, A., Semyanov, A.. 2021-11-04. A high-fat diet changes astrocytic metabolism to enhance synaptic plasticity and promote exploratory behavior. https://doi.org/10.1101/2021.11.03.467141

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗