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      Tumor-associated nonmyelinating Schwann cell–expressed PVT1 promotes pancreatic cancer kynurenine pathway and tumor immune exclusion

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          Abstract

          One of the major obstacles to treating pancreatic ductal adenocarcinoma (PDAC) is its immunoresistant microenvironment. The functional importance and molecular mechanisms of Schwann cells in PDAC remains largely elusive. We characterized the gene signature of tumor-associated nonmyelinating Schwann cells (TASc) in PDAC and indicated that the abundance of TASc was correlated with immune suppressive tumor microenvironment and the unfavorable outcome of patients with PDAC. Depletion of pancreatic-specific TASc promoted the tumorigenesis of PDAC tumors. TASc-expressed long noncoding RNA (lncRNA) plasmacytoma variant translocation 1 ( PVT1 ) was triggered by the tumor cell–produced interleukin-6. Mechanistically, PVT1 modulated RAF proto-oncogene serine/threonine protein kinase–mediated phosphorylation of tryptophan 2,3-dioxygenase in TASc, facilitating its enzymatic activities in catalysis of tryptophan to kynurenine. Depletion of TASc-expressed PVT1 suppressed PDAC tumor growth. Furthermore, depletion of TASc using a small-molecule inhibitor effectively sensitized PDAC to immunotherapy, signifying the important roles of TASc in PDAC immune resistance.

          Abstract

          Targeting tumor-associated non-myelinating Schwann cells improves response of immunotherapy-refractory pancreatic cancer.

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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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              Highly accurate protein structure prediction with AlphaFold

              John Jumper, Richard Evans, Alexander Pritzel … (2021)
              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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                Author and article information

                Contributors
                Journal
                Title: Science Advances
                Abbreviated Title: Sci. Adv.
                Publisher: American Association for the Advancement of Science (AAAS)
                ISSN (Electronic): 2375-2548
                Publication date Created: February 03 2023
                Publication date (Print): February 03 2023
                Volume: 9
                Issue: 5
                Affiliations
                [1 ]Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
                [2 ]Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX 77030, USA.
                [3 ]Center for Drug Discovery, Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA.
                [4 ]Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA.
                [5 ]Institute of Translational Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201620, China.
                [6 ]Division of Surgical Science, Department of Surgery, Duke University, School of Medicine, Durham, NC 27710, USA.
                [7 ]Department of Breast Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
                [8 ]Department of Pathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
                [9 ]Department of Genitourinary Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
                [10 ]Department of Cancer Systems Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
                [11 ]Department of Cancer Biology, Division of Basic Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
                [12 ]The Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
                [13 ]Graduate Institute of Biomedical Sciences, Research Center for Cancer Biology, and Center for Molecular Medicine, China Medical University, Taichung 404, Taiwan.
                [14 ]Department of Biotechnology, Asia University, Taichung 413, Taiwan.
                [15 ]Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
                [16 ]Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
                [17 ]Center for RNA Interference and Non-Coding RNAs, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
                Article
                DOI: 10.1126/sciadv.add6995
                SO-VID: 7bba96f5-16d3-43dd-a9d6-9b7ce0593256
                Copyright © © 2023
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