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      Wnt and Notch signaling govern self-renewal and differentiation in a subset of human glioblastoma stem cells

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          Abstract

          In this study, Rajakulendran et al. investigated the role of Wnt/βcatenin signaling in GBM stem cell renewal and fate decisions. They identify new contexts for Wnt modulation for targeting stem cell differentiation and self-renewal in GBM heterogeneity.

          Abstract

          Developmental signal transduction pathways act diversely, with context-dependent roles across systems and disease types. Glioblastomas (GBMs), which are the poorest prognosis primary brain cancers, strongly resemble developmental systems, but these growth processes have not been exploited therapeutically, likely in part due to the extreme cellular and genetic heterogeneity observed in these tumors. The role of Wnt/βcatenin signaling in GBM stem cell (GSC) renewal and fate decisions remains controversial. Here, we report context-specific actions of Wnt/βcatenin signaling in directing cellular fate specification and renewal. A subset of primary GBM-derived stem cells requires Wnt proteins for self-renewal, and this subset specifically relies on Wnt/βcatenin signaling for enhanced tumor burden in xenograft models. In an orthotopic Wnt reporter model, Wnt hi GBM cells (which exhibit high levels of βcatenin signaling) are a faster-cycling, highly self-renewing stem cell pool. In contrast, Wnt lo cells (with low levels of signaling) are slower cycling and have decreased self-renewing potential. Dual inhibition of Wnt/βcatenin and Notch signaling in GSCs that express high levels of the proneural transcription factor ASCL1 leads to robust neuronal differentiation and inhibits clonogenic potential. Our work identifies new contexts for Wnt modulation for targeting stem cell differentiation and self-renewal in GBM heterogeneity, which deserve further exploration therapeutically.

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          Most cited references48

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              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
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                Author and article information

                Journal
                Genes Dev
                Genes Dev
                genesdev
                genesdev
                GAD
                Genes & Development
                Cold Spring Harbor Laboratory Press
                0890-9369
                1549-5477
                1 May 2019
                : 33
                : 9-10
                : 498-510
                Affiliations
                [1 ]Leslie Dan Faculty of Pharmacy, Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario M5S 3M2, Canada;
                [2 ]Developmental and Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada;
                [3 ]Faculty of Medicine, Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 0A4, Canada;
                [4 ]Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada;
                [5 ]St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada;
                [6 ]Toronto Western Hospital, Toronto, Ontario M5T 2S8, Canada;
                [7 ]Institute of Genetics and Molecular Medicine, Edinburgh EH4 2XU, United Kingdom;
                [8 ]Faculty of Medicine, Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada;
                [9 ]Faculty of Medicine, Department of Surgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada;
                [10 ]Division of Neurosurgery The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
                Author notes
                [11]

                Present address: OCAD University, Toronto, Ontario M5T 1W1, Canada.

                [12]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0001-7241-9044
                Article
                8711660
                10.1101/gad.321968.118
                6499328
                30842215
                dcc8a27f-1835-4919-8e18-185eb3453e53
                © 2019 Rajakulendran et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genesdev.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 22 October 2018
                : 19 February 2019
                Page count
                Pages: 13
                Funding
                Funded by: SU2C Canada Cancer Stem Cell Dream Team Research Funding
                Award ID: SU2C-AACR-DT-19-15
                Funded by: Government of Canada , open-funder-registry 10.13039/501100000023;
                Funded by: Genome Canada , open-funder-registry 10.13039/100008762;
                Funded by: Canadian Institute of Health Research
                Award ID: 142434
                Funded by: Ontario Institute for Cancer Research , open-funder-registry 10.13039/501100004203;
                Funded by: Government of Ontario
                Funded by: Canadian Cancer Society Research Institute , open-funder-registry 10.13039/501100000015;
                Funded by: Hospital for Sick Children Foundation
                Funded by: Canadian Institutes for Health Research
                Award ID: CIHR-361837
                Funded by: Brain Canada-Canadian Imperial Bank of Commerce
                Funded by: Brain Cancer Training Awards
                Categories
                Research Paper

                ascl1,glioblastoma,notch,wnt,cancer,differentiation,neuronal,self-renewal

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