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      AEP-cleaved DDX3X induces alternative RNA splicing events to mediate cancer cell adaptation in harsh microenvironments

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          Abstract

          Oxygen and nutrient deprivation are common features of solid tumors. Although abnormal alternative splicing (AS) has been found to be an important driving force in tumor pathogenesis and progression, the regulatory mechanisms of AS that underly the adaptation of cancer cells to harsh microenvironments remain unclear. Here, we found that hypoxia- and nutrient deprivation–induced asparagine endopeptidase (AEP) specifically cleaved DDX3X in a HIF1A-dependent manner. This cleavage yields truncated carboxyl-terminal DDX3X (tDDX3X-C), which translocates and aggregates in the nucleus. Unlike intact DDX3X, nuclear tDDX3X-C complexes with an array of splicing factors and induces AS events of many pre-mRNAs; for example, enhanced exon skipping (ES) in exon 2 of the classic tumor suppressor PRDM2 leads to a frameshift mutation of PRDM2. Intriguingly, the isoform ARRB1-Δexon 13 binds to glycolytic enzymes and regulates glycolysis. By utilizing in vitro assays, glioblastoma organoids, and animal models, we revealed that AEP/tDDX3X-C promoted tumor malignancy via these isoforms. More importantly, high AEP/tDDX3X-C/ ARRB1-Δ exon 13 in cancerous tissues was tightly associated with poor patient prognosis. Overall, our discovery of the effect of AEP-cleaved DDX3X switching on alternative RNA splicing events identifies a mechanism in which cancer cells adapt to oxygen and nutrient shortages and provides potential diagnostic and/or therapeutic targets.

          Abstract

          Abstract

          <p>Hypoxia and nutrient deprivation triggers AEP-specific cleavage of DDX3X, which yields truncated DDX3X that facilitates alternative splicing events and allows cancer cell adaptation.</p>

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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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            GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

            Abstract Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
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              The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

              Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
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                Author and article information

                Contributors
                Journal
                J Clin Invest
                J Clin Invest
                J Clin Invest
                The Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                1 February 2024
                1 February 2024
                1 February 2024
                : 134
                : 3
                : e173299
                Affiliations
                [1 ]Brain Injury Center, Shanghai Institute of Head Trauma and
                [2 ]Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
                [3 ]Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
                [4 ]Department of Neurosurgery, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
                [5 ]Department of Neurosurgery, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China.
                [6 ]Fujian Key Laboratory for Prenatal Diagnosis and Birth Defects, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
                [7 ]Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
                [8 ]Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui, China.
                [9 ]Department of Biliary-Pancreatic Surgery and
                [10 ]Department of General Surgery, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
                [11 ]Department of Orthopedics, Shanghai General Hospital of Shanghai Jiao Tong University, Shanghai, China.
                Author notes
                Address correspondence to: Yingying Lin, Brain Injury Center, Shanghai Institute of Head Trauma, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine. Shanghai 200127, China. Phone: 86.21.68383982; Email: yylin@ 123456sibs.ac.cn . Or to: Jiayi Chen, Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China. Phone: 86.21.64370045; Email: cjy11756@ 123456rjh.com.cn . Or to: Jianwei Ge, Department of Neurosurgery, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Phone: 86.21.68383000; Email: karidgeliya@ 123456126.com .

                Authorship note: WZ, LC, JY, SZ, and JZ contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-5959-489X
                http://orcid.org/0000-0002-6935-5681
                Article
                173299
                10.1172/JCI173299
                10849765
                37988165
                42d2cc71-d54c-497f-b25b-b268a2bd7fc5
                © 2023 Zhang et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 June 2023
                : 14 November 2023
                Funding
                Funded by: National Natural Science Foundation of China, https://doi.org/10.13039/501100001809;
                Award ID: 81772654
                Funded by: National Natural Science Foundation of China, https://doi.org/10.13039/501100001809;
                Award ID: 82273278
                Funded by: National Natural Science Foundation of China, https://doi.org/10.13039/501100001809;
                Award ID: 82272728
                Funded by: National Natural Science Foundation of China, https://doi.org/10.13039/501100001809;
                Award ID: 82002630
                Funded by: Shanghai Association for Science and Technology, https://doi.org/10.13039/100010098;
                Award ID: 201409003000
                Funded by: Shanghai Association for Science and Technology, https://doi.org/10.13039/100010098;
                Award ID: 201409002400
                Funded by: Shanghai Association for Science and Technology, https://doi.org/10.13039/100010098;
                Award ID: 20YF1426200
                Funded by: National Natural Science Foundation of China, https://doi.org/10.13039/501100001809;
                Award ID: 82003222
                Funded by: National Natural Science Foundation of China, https://doi.org/10.13039/501100001809;
                Award ID: 82373202
                Categories
                Research Article

                oncology,proteases
                oncology, proteases

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