Adult T cell leukemia/lymphoma (ATLL) is a frequently incurable disease associated with the human lymphotropic virus type I (HTLV-I). RNAi screening of ATLL lines revealed that their proliferation depends on BATF3 and IRF4, which cooperatively drive ATLL-specific gene expression. HBZ, the only HTLV-I encoded transcription factor that is expressed in all ATLL cases, binds to an ATLL-specific BATF3 super-enhancer and thereby regulates the expression of BATF3 and its downstream targets, including MYC. Inhibitors of bromodomain-and-extra-terminal-domain (BET) chromatin proteins collapsed the transcriptional network directed by HBZ and BATF3, and were consequently toxic for ATLL cell lines, patient samples, and xenografts. Our study demonstrates that the HTLV-I oncogenic retrovirus exploits a regulatory module that can be attacked therapeutically with BET inhibitors.
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http://dx.doi.org/10.1016/j.ccell.2018.06.014 | DOI Listing |
Sci Rep
December 2024
Department of Thyroid Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Although CCL17 has been reported to exert a vital role in many cancers, the related studies in the thyroid carcinoma have never reported. As a chemokine, CCL17 plays a positive role by promoting the infiltration of immune cells into the tumor microenviroment (TME) to influence tumor invasion and metastasis. Therefore, this study is aimed to investigate the association of CCL17 level with potential prognostic value on tumor immunity in the thyroid carcinoma (THCA) based on the bioinformatics analysis.
View Article and Find Full Text PDFNat Commun
December 2024
State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Biomedical Pioneering Innovative Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), Peking University, 100871, Beijing, China.
Deciphering how noncoding DNA determines gene expression is critical for decoding the functional genome. Understanding the transcription effects of noncoding genetic variants are still major unsolved problems, which is critical for downstream applications in human genetics and precision medicine. Here, we integrate regulatory-specific neural networks and tissue-specific gradient-boosting trees to build SVEN: a hybrid sequence-oriented architecture that can accurately predict tissue-specific gene expression level and quantify the tissue-specific transcriptomic impacts of structural variants across more than 350 tissues and cell lines.
View Article and Find Full Text PDFNat Commun
December 2024
State Key Laboratory of Plant Diversity and Specialty Crops, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
Fruit ripening is a highly-orchestrated process that requires the fine-tuning and precise control of gene expression, which is mainly governed by phytohormones, epigenetic modifiers, and transcription factors. How these intrinsic regulators coordinately modulate the ripening remains elusive. Here we report the identification and characterization of FvALKBH10B as an N-methyladenosine (mA) RNA demethylase necessary for the normal ripening of strawberry (Fragaria vesca) fruit.
View Article and Find Full Text PDFJ Agric Food Chem
December 2024
Agronomy College, Guizhou University, Huaxi, 550025 Guiyang, Guizhou, P. R. China.
Safflower ( L.) is a valuable oil crop due to its bioactive ingredients and high linoleic acid content, which contribute to its antioxidant properties and potential for preventing atherosclerosis. Current research on safflower focuses on understanding the biosynthesis of seed oil through omics strategies, yet there is a lack of comprehensive knowledge of the dynamic changes in lipids and the regulatory mechanisms during seed development.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA 02215, United States.
microRNAs (miRNAs) are central post-transcriptional gene expression regulators in healthy and diseased states. Despite decades of effort, deciphering miRNA targets remains challenging, leading to an incomplete miRNA interactome and partially elucidated miRNA functions. Here, we introduce microT-CNN, an avant-garde deep convolutional neural network model that moves the needle by integrating hundreds of tissue-matched (in-)direct experiments from 26 distinct cell types, corresponding to a unique training and evaluation set of >60 000 miRNA binding events and ~30 000 unique miRNA-gene target pairs.
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