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      CO 2 signaling mediates neurovascular coupling in the cerebral cortex

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          Abstract

          Neurovascular coupling is a fundamental brain mechanism that regulates local cerebral blood flow (CBF) in response to changes in neuronal activity. Functional imaging techniques are commonly used to record these changes in CBF as a proxy of neuronal activity to study the human brain. However, the mechanisms of neurovascular coupling remain incompletely understood. Here we show in experimental animal models (laboratory rats and mice) that the neuronal activity-dependent increases in local CBF in the somatosensory cortex are prevented by saturation of the CO 2-sensitive vasodilatory brain mechanism with surplus of exogenous CO 2 or disruption of brain CO 2/HCO 3 transport by genetic knockdown of electrogenic sodium-bicarbonate cotransporter 1 (NBCe1) expression in astrocytes. A systematic review of the literature data shows that CO 2 and increased neuronal activity recruit the same vasodilatory signaling pathways. These results and analysis suggest that CO 2 mediates signaling between neurons and the cerebral vasculature to regulate brain blood flow in accord with changes in the neuronal activity.

          Abstract

          The mechanism of neurovascular coupling ensures that the brain energy supply is sufficient to meet demand. Here the authors show that in this mechanism CO2 plays an important role in neuronal activity-dependent regulation of local brain blood flow.

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          An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex.

          The major cell classes of the brain differ in their developmental processes, metabolism, signaling, and function. To better understand the functions and interactions of the cell types that comprise these classes, we acutely purified representative populations of neurons, astrocytes, oligodendrocyte precursor cells, newly formed oligodendrocytes, myelinating oligodendrocytes, microglia, endothelial cells, and pericytes from mouse cerebral cortex. We generated a transcriptome database for these eight cell types by RNA sequencing and used a sensitive algorithm to detect alternative splicing events in each cell type. Bioinformatic analyses identified thousands of new cell type-enriched genes and splicing isoforms that will provide novel markers for cell identification, tools for genetic manipulation, and insights into the biology of the brain. For example, our data provide clues as to how neurons and astrocytes differ in their ability to dynamically regulate glycolytic flux and lactate generation attributable to unique splicing of PKM2, the gene encoding the glycolytic enzyme pyruvate kinase. This dataset will provide a powerful new resource for understanding the development and function of the brain. To ensure the widespread distribution of these datasets, we have created a user-friendly website (http://web.stanford.edu/group/barres_lab/brain_rnaseq.html) that provides a platform for analyzing and comparing transciption and alternative splicing profiles for various cell classes in the brain. Copyright © 2014 the authors 0270-6474/14/3411929-19$15.00/0.
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            Glial and neuronal control of brain blood flow.

            Blood flow in the brain is regulated by neurons and astrocytes. Knowledge of how these cells control blood flow is crucial for understanding how neural computation is powered, for interpreting functional imaging scans of brains, and for developing treatments for neurological disorders. It is now recognized that neurotransmitter-mediated signalling has a key role in regulating cerebral blood flow, that much of this control is mediated by astrocytes, that oxygen modulates blood flow regulation, and that blood flow may be controlled by capillaries as well as by arterioles. These conceptual shifts in our understanding of cerebral blood flow control have important implications for the development of new therapeutic approaches.
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              Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

              Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.
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                Author and article information

                Contributors
                p.hosford@ucl.ac.uk
                iruminot@cecs.cl
                a.gourine@ucl.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                19 April 2022
                19 April 2022
                2022
                : 13
                : 2125
                Affiliations
                [1 ]GRID grid.83440.3b, ISNI 0000000121901201, Centre for Cardiovascular and Metabolic Neuroscience, Neuroscience, Physiology and Pharmacology, , University College London, ; London, UK
                [2 ]GRID grid.83440.3b, ISNI 0000000121901201, UCL Centre for Advanced Biomedical Imaging, Division of Medicine, , University College London, ; London, UK
                [3 ]GRID grid.418237.b, ISNI 0000 0001 0378 7310, Centro de Estudios Científicos (CECs) & Universidad San Sebastián, ; Valdivia, Chile
                [4 ]GRID grid.7119.e, ISNI 0000 0004 0487 459X, Universidad Austral de Chile, ; Valdivia, Chile
                Author information
                http://orcid.org/0000-0002-3252-4832
                http://orcid.org/0000-0002-4171-3539
                http://orcid.org/0000-0002-6623-4833
                http://orcid.org/0000-0002-3985-384X
                http://orcid.org/0000-0003-3068-491X
                Article
                29622
                10.1038/s41467-022-29622-9
                9019094
                35440557
                cc4b359a-da81-46b5-b6d2-94bae75124b5
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 November 2020
                : 23 March 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 204624
                Award ID: 200893
                Award Recipient :
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                © The Author(s) 2022

                Uncategorized
                astrocyte,blood flow
                Uncategorized
                astrocyte, blood flow

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