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      Graphdiyne oxide nanosheets display selective anti-leukemia efficacy against DNMT3A-mutant AML cells

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          Abstract

          DNA methyltransferase 3 A ( DNMT3A) is the most frequently mutated gene in acute myeloid leukemia (AML). Although chemotherapy agents have improved outcomes for DNMT3A-mutant AML patients, there is still no targeted therapy highlighting the need for further study of how DNMT3A mutations affect AML phenotype. Here, we demonstrate that cell adhesion-related genes are predominantly enriched in DNMT3A-mutant AML cells and identify that graphdiyne oxide (GDYO) display an anti-leukemia effect specifically against these mutated cells. Mechanistically, GDYO directly interacts with integrin β2 (ITGB2) and c-type mannose receptor (MRC2), which facilitate the attachment and cellular uptake of GDYO. Furthermore, GDYO binds to actin and prevents actin polymerization, thus disrupting the actin cytoskeleton and eventually leading to cell apoptosis. Finally, we validate the in vivo safety and therapeutic potential of GDYO against DNMT3A-mutant AML cells. Collectively, these findings demonstrate that GDYO is an efficient and specific drug candidate against DNMT3A-mutant AML.

          Abstract

          DNA methyltransferase 3A, a mutated gene associated with hematologic malignancies in age-related clonal haematopoiesis lacks targeted therapies. Here, the authors screen carbon nanomaterials and find graphdiyne oxide binds to mutant cells and disrupts cellular processes with a therapeutic effect in vitro and in vivo.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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

                Contributors
                chenchy@nanoctr.cn
                axu@zju.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                26 September 2022
                26 September 2022
                2022
                : 13
                : 5657
                Affiliations
                [1 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Center of Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, , Zhejiang University School of Medicine, ; Hangzhou, 310058 China
                [2 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Liangzhu Laboratory, , Zhejiang University Medical Center, ; 1369 West Wenyi Road, Hangzhou, 311121 China
                [3 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Institute of Hematology, , Zhejiang University & Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, ; Hangzhou, 310058 China
                [4 ]GRID grid.419265.d, ISNI 0000 0004 1806 6075, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, , National Center for Nanoscience and Technology of China, ; Beijing, 100190 China
                [5 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; Beijing, 100049 China
                [6 ]The GBA National Institute for Nanotechnology Innovation, Guangzhou, 510700 China
                [7 ]GRID grid.419265.d, ISNI 0000 0004 1806 6075, Laboratory of Theoretical and Computational Nanoscience, CAS Center for Excellence in Nanoscience, , National Center for Nanoscience and Technology, ; Beijing, 100190 China
                [8 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Institute of Brain and Cognition, , Zhejiang University City College School of Medicine, ; Hangzhou, 310015 China
                [9 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, The MOE Frontier Research Center of Brain & Brain-Machine Integration, , Zhejiang University School of Brain Science and Brain Medicine, ; Hangzhou, 310058 China
                [10 ]GRID grid.9227.e, ISNI 0000000119573309, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics and National Center for Nanoscience and Technology of China, , Chinese Academy of Sciences, ; Beijing, 100049 China
                [11 ]GRID grid.9227.e, ISNI 0000000119573309, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Organic Solids, Institute of Chemistry, , Chinese Academy of Sciences, ; Beijing, 100190 China
                Author information
                http://orcid.org/0000-0001-9197-3163
                http://orcid.org/0000-0002-6540-0473
                http://orcid.org/0000-0003-2925-5544
                http://orcid.org/0000-0001-5012-3453
                http://orcid.org/0000-0001-5279-0399
                http://orcid.org/0000-0002-9586-9360
                http://orcid.org/0000-0002-6027-0315
                http://orcid.org/0000-0001-5636-6704
                Article
                33410
                10.1038/s41467-022-33410-w
                9512932
                36163326
                e5ac0273-3c4b-43e3-b151-f90880d9b9be
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 April 2022
                : 16 September 2022
                Categories
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                © The Author(s) 2022

                Uncategorized
                targeted therapies,drug regulation,acute myeloid leukaemia,drug development
                Uncategorized
                targeted therapies, drug regulation, acute myeloid leukaemia, drug development

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