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      Role of pericytes in the development of cerebral cavernous malformations

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          Summary

          Cerebral cavernous malformation (CCM) is caused by loss-of-function mutations in CCM1, CCM2, or CCM3 genes of endothelial cells. It is characterized by pericyte deficiency. However, the role of pericytes in CCMs is not yet clarified. We found pericytes in Cdh5Cre ERT2 ; Ccm1 fl/fl ( Ccm1 ECKO ) mice had a high expression of PDGFRβ. The inhibition of pericyte function by CP-673451 aggravated the CCM lesion development. RNA-sequencing analysis revealed the molecular traits of pericytes, such as highly expressed ECM-related genes, especially Fn1. Furthermore, KLF4 coupled with phosphorylated SMAD3 (pSMAD3) promoted the transcription of fibronectin in the pericytes of CCM lesions. RGDS peptide, an inhibitor of fibronectin, decreased the lesion area in the cerebella and retinas of Ccm1 ECKO mice. Also, human CCM lesions had abundant fibronectin deposition, and pSMAD3- and KLF4-positive pericytes. These findings indicate that pericytes are essential for CCM lesion development, and fibronectin intervention may provide a novel target for therapeutic intervention in such patients.

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          • •

            We presented the first evidence that blockage of pericytes could aggravate CCMs

          • •

            We evaluated the genetic profiles of pericytes in Ccm1 ECKO mice

          • •

            We further showed that inhibition of fibronectin could prohibit the development of CCMs

          Abstract

          Neuroscience; Neuroanatomy; Clinical neuroscience

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          Michael Love, Wolfgang Huber, Simon Anders (2014)
          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)
            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)
              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
              Article 0 comments Cited 10430 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Changbin Shi
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 19 November 2022
                Publication date Collection: 22 December 2022
                Publication date (Electronic): 19 November 2022
                Volume: 25
                Issue: 12
                Electronic Location Identifier: 105642
                Affiliations
                [1 ]Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, 23 Youzheng Street, Nangang District, Harbin, Heilongjiang Province 150001, PRC China
                [2 ]Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, TianJin 300070, PRC China
                Author notes
                [∗ ]Corresponding author changbinshi@ 123456hotmail.com
                [3]

                Lead contact

                Article
                Publisher Item ID: S2589-0042(22)01914-9 Publisher ID: 105642
                DOI: 10.1016/j.isci.2022.105642
                PMC ID: 9713377
                SO-VID: bc6eb3fa-467d-4a6c-9814-df40230d5343
                Copyright © © 2022 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 5 July 2022
                Date revision received : 20 September 2022
                Date accepted : 17 November 2022
                Categories
                Subject: Article

                Keywords: neuroscience,neuroanatomy,clinical neuroscience
                Data availability:
                Keywords: neuroscience, neuroanatomy, clinical neuroscience

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