Search bioRxivSearch

bioRxiv · 10.1101/264275

Liquefaction of the brain following stroke shares a similar molecular and morphological profile with atherosclerosis and mediates secondary neurodegeneration in an osteopontin dependent mechanism

Abstract

Here we used mouse models of heart and brain ischemia to compare the inflammatory response to ischemia in the heart, a protein rich organ, to the inflammatory response to ischemia in the brain, a lipid rich organ. We report that ischemia-induced inflammation resolves between 1 and 4 weeks in the heart compared to between 8 and 24 weeks in the brain. Importantly, we discovered that a second burst of inflammation occurs in the brain between 4 and 8 weeks following ischemia, which coincided with the appearance of cholesterol crystals within the infarct. This second wave shares a similar cellular and molecular profile with atherosclerosis and is characterized by high levels of osteopontin (OPN) and matrix metalloproteinases (MMPs). In order to test the role of OPN in areas of liquefactive necrosis, OPN-/- mice were subjected to brain ischemia. We found that at 7 weeks following stroke, the expression of pro-inflammatory proteins and MMPs was profoundly reduced in the infarct of the OPN-/- mice, although the number of cholesterol crystals was increased. OPN-/- mice exhibited faster recovery of motor function and a higher number of neuronal nuclei (NeuN) positive cells in the peri-infarct area at 7 weeks following stroke. Based on these findings we propose that the brain liquefies after stroke because phagocytic cells in the infarct are unable to efficiently clear cholesterol rich myelin debris, and that this leads to the perpetuation of an OPN-dependent inflammatory response characterized by high levels of degradative enzymes.\n\nSignificance StatementThe inflammatory response to ischemia in the brain is different to the response to ischemic injury in other organs. In the brain, and for unknown reasons, dead tissue liquefies in response to ischemia by the process of liquefactive necrosis. However, the data we present here demonstrate that there is overlap between the pathophysiology of liquefactive necrosis and atherosclerosis. Specifically, we show that chronic stroke infarcts contain foamy macrophages, cholesterol crystals, high levels of OPN and MMPs, and a similar cytokine profile to atherosclerosis. Therefore, because cholesterol is a central component of myelin, liquefactive necrosis in response to stroke may be caused by an inflammatory response to cholesterol-rich myelin debris that is driven in large part by OPN and MMPs.

Source connections

Explore related subjects

Keep this discovery

BibTeXRIS

Chung, A., Frye, J., Zbesko, J. C., Constantopoulos, E., Hayes, M. I., Figueroa, A. G., Day, W. A., Konhilas, J. P., McKay, B. S., Nguyen, T.-V. V., Doyle, K. P.. 2018-02-16. Liquefaction of the brain following stroke shares a similar molecular and morphological profile with atherosclerosis and mediates secondary neurodegeneration in an osteopontin dependent mechanism. https://doi.org/10.1101/264275

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