Search bioRxiv⌕ Search

bioRxiv · 10.1101/2023.03.29.534718

Integrative transcriptomic and metabolic analyses of the mammalian hibernating brain identifies a key role for succinate dehydrogenase in ischemic tolerance

Abstract

Ischemic stroke results in a loss of tissue homeostasis and integrity, the underlying pathobiology of which stems primarily from the depletion of cellular energy stores and perturbation of available metabolites1. Hibernation in thirteen-lined ground squirrels (TLGS), Ictidomys tridecemlineatus, provides a natural model of ischemic tolerance as these mammals undergo prolonged periods of critically low cerebral blood flow without evidence of central nervous system (CNS) damage2. Studying the complex interplay of genes and metabolites that unfolds during hibernation may provide novel insights into key regulators of cellular homeostasis during brain ischemia. Herein, we interrogated the molecular profiles of TLGS brains at different time points within the hibernation cycle via RNA sequencing coupled with untargeted metabolomics. We demonstrate that hibernation in TLGS leads to major changes in the expression of genes involved in oxidative phosphorylation and this is correlated with an accumulation of the tricarboxylic acid (TCA) cycle intermediates citrate, cis-aconitate, and -ketoglutarate-KG. Integration of the gene expression and metabolomics datasets led to the identification of succinate dehydrogenase (SDH) as the critical enzyme during hibernation, uncovering a break in the TCA cycle at that level. Accordingly, the SDH inhibitor dimethyl malonate (DMM) was able to rescue the effects of hypoxia on human neuronal cells in vitro and in mice subjected to permanent ischemic stroke in vivo. Our findings indicate that studying the regulation of the controlled metabolic depression that occurs in hibernating mammals may lead to novel therapeutic approaches capable of increasing ischemic tolerance in the CNS.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bernstock, J. D., Willis, C. M., Garcia-Segura, M. E., Gaude, E., Anni, D., Lee, Y.-j., Thomas, L. W., Casey, A., Vicario, N., Leonardi, T., Nicaise, A. M., Gessler, F. A., Izzy, S., Buffelli, M. R., Seidlitz, J., Srinivasan, S., Murphy, M. P., Ashcroft, M., Cambiaghi, M., Hallenbeck, J. M., Peruzzotti-Jametti, L.. 2023-03-30. Integrative transcriptomic and metabolic analyses of the mammalian hibernating brain identifies a key role for succinate dehydrogenase in ischemic tolerance. https://doi.org/10.1101/2023.03.29.534718

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

KEEP EXPLORING

Related preprints

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↗

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↗

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↗