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      Somatic GJA4 mutation in intracranial extra-axial cavernous hemangiomas

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          Abstract

          Objective

          Extra-axial cavernous hemangiomas (ECHs) are sporadic and rare intracranial occupational lesions that usually occur within the cavernous sinus. The aetiology of ECHs remains unknown.

          Methods

          Whole-exome sequencing was performed on ECH lesions from 12 patients (discovery cohort) and droplet digital polymerase-chain-reaction (ddPCR) was used to confirm the identified mutation in 46 additional cases (validation cohort). Laser capture microdissection (LCM) was carried out to capture and characterise subgroups of tissue cells. Mechanistic and functional investigations were carried out in human umbilical vein endothelial cells and a newly established mouse model.

          Results

          We detected somatic GJA4 mutation (c.121G>T, p.G41C) in 5/12 patients with ECH in the discovery cohort and confirmed the finding in the validation cohort (16/46). LCM followed by ddPCR revealed that the mutation was enriched in lesional endothelium. In vitro experiments in endothelial cells demonstrated that the GJA4 mutation activated SGK-1 signalling that in turn upregulated key genes involved in cell hyperproliferation and the loss of arterial specification. Compared with wild-type littermates, mice overexpressing the GJA4 mutation developed ECH-like pathological morphological characteristics (dilated venous lumen and elevated vascular density) in the retinal superficial vascular plexus at the postnatal 3 weeks, which were reversed by an SGK1 inhibitor, EMD638683.

          Conclusions

          We identified a somatic GJA4 mutation that presents in over one-third of ECH lesions and proposed that ECHs are vascular malformations due to GJA4-induced activation of the SGK1 signalling pathway in brain endothelial cells.

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          Most cited references37

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            Shear-induced Notch-Cx37-p27 axis arrests endothelial cell cycle to enable arterial specification

            Establishment of a functional vascular network is rate-limiting in embryonic development, tissue repair and engineering. During blood vessel formation, newly generated endothelial cells rapidly expand into primitive plexi that undergo vascular remodeling into circulatory networks, requiring coordinated growth inhibition and arterial-venous specification. Whether the mechanisms controlling endothelial cell cycle arrest and acquisition of specialized phenotypes are interdependent is unknown. Here we demonstrate that fluid shear stress, at arterial flow magnitudes, maximally activates NOTCH signaling, which upregulates GJA4 (commonly, Cx37) and downstream cell cycle inhibitor CDKN1B (p27). Blockade of any of these steps causes hyperproliferation and loss of arterial specification. Re-expression of GJA4 or CDKN1B, or chemical cell cycle inhibition, restores endothelial growth control and arterial gene expression. Thus, we elucidate a mechanochemical pathway in which arterial shear activates a NOTCH-GJA4-CDKN1B axis that promotes endothelial cell cycle arrest to enable arterial gene expression. These insights will guide vascular regeneration and engineering.
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              The mouse retina as an angiogenesis model.

              The mouse retina has been used extensively over the past decades to study both physiologic and pathologic angiogenesis. Over time, various mouse retina models have evolved into well-characterized and robust tools for in vivo angiogenesis research. This article is a review of the angiogenic development of the mouse retina and a discussion of some of the most widely used vascular disease models. From the multitude of studies performed in the mouse retina, a selection of representative works is discussed in more detail regarding their role in advancing the understanding of both the ocular and general mechanisms of angiogenesis.
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                Author and article information

                Journal
                Stroke Vasc Neurol
                Stroke Vasc Neurol
                svnbmj
                svn
                Stroke and Vascular Neurology
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2059-8688
                2059-8696
                December 2023
                18 April 2023
                : 8
                : 6
                : 453-462
                Affiliations
                [1 ] departmentDepartment of Neurosurgery , Ringgold_105738Beijing Tiantan Hospital, Capital Medical University , Beijing, China
                [2 ] Ringgold_608718China National Clinical Research Center for Neurological Diseases , Beijing, China
                [3 ] departmentDepartment of Chemical and Biological Engineering , Ringgold_58207The Hong Kong University of Science and Technology , Hong Kong, China
                [4 ] departmentDivision of Life Science, Center for Systems Biology and Human Health and State Key Laboratory of Molecular Neuroscience , Ringgold_105738The Hong Kong University of Science and Technology , Hong Kong, China
                [5 ] departmentDepartment of Neurosurgery , China-Japan Friendship Hospital , Beijing, China
                [6 ] departmentSchool of Medical Technology , Ringgold_47833Beijing Institute of Technology , Beijing, China
                [7 ] departmentHong Kong Center for Neurodegenerative Diseases , InnoHK , Hong Kong SAR, China
                [8 ] Beijing Neurosurgical Institute, Capital Medical University , Beijing, China
                Author notes
                [Correspondence to ] Dr Yong Cao; caoyong@ 123456bjtth.org ; Mr Jiguang Wang; jgwang@ 123456ust.hk
                Author information
                http://orcid.org/0000-0001-8351-4602
                http://orcid.org/0000-0001-6715-298X
                http://orcid.org/0000-0002-8289-1120
                Article
                svn-2022-002227
                10.1136/svn-2022-002227
                10800255
                37072338
                a7f57a11-f79c-4d18-b9bc-db5322e8494f
                © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 06 December 2022
                : 02 March 2023
                Funding
                Funded by: Hong Kong RGC Fund;
                Award ID: 16102522, C6021-19EF
                Funded by: Department of Science and Technology of Guangdong Province;
                Award ID: 2020A0505090007
                Funded by: Hong Kong ITC Fund;
                Award ID: ITCPD/17-9
                Funded by: Beijing Advanced Innovation Center for Big Data-based Precision Medicine;
                Award ID: PXM2020_014226_000066
                Funded by: Genomics Platform Construction for Chinese Major Brain Disease-AVM;
                Award ID: PXM2019_026280_000002-AVM
                Categories
                Original Research
                1506
                1507
                Custom metadata
                unlocked
                editors-choice

                vascular malformations
                vascular malformations

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