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      Akt enhances the vulnerability of cancer cells to VCP/p97 inhibition-mediated paraptosis

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          Abstract

          Valosin-containing protein (VCP)/p97, an AAA+ ATPase critical for maintaining proteostasis, emerges as a promising target for cancer therapy. This study reveals that targeting VCP selectively eliminates breast cancer cells while sparing non-transformed cells by inducing paraptosis, a non-apoptotic cell death mechanism characterized by endoplasmic reticulum and mitochondria dilation. Intriguingly, oncogenic HRas sensitizes non-transformed cells to VCP inhibition-mediated paraptosis. The susceptibility of cancer cells to VCP inhibition is attributed to the non-attenuation and recovery of protein synthesis under proteotoxic stress. Mechanistically, mTORC2/Akt activation and eIF3d-dependent translation contribute to translational rebound and amplification of proteotoxic stress. Furthermore, the ATF4/DDIT4 axis augments VCP inhibition-mediated paraptosis by activating Akt. Given that hyperactive Akt counteracts chemotherapeutic-induced apoptosis, VCP inhibition presents a promising therapeutic avenue to exploit Akt-associated vulnerabilities in cancer cells by triggering paraptosis while safeguarding normal cells.

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          Most cited references65

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

            We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.
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              The integrated stress response.

              In response to diverse stress stimuli, eukaryotic cells activate a common adaptive pathway, termed the integrated stress response (ISR), to restore cellular homeostasis. The core event in this pathway is the phosphorylation of eukaryotic translation initiation factor 2 alpha (eIF2α) by one of four members of the eIF2α kinase family, which leads to a decrease in global protein synthesis and the induction of selected genes, including the transcription factor ATF4, that together promote cellular recovery. The gene expression program activated by the ISR optimizes the cellular response to stress and is dependent on the cellular context, as well as on the nature and intensity of the stress stimuli. Although the ISR is primarily a pro-survival, homeostatic program, exposure to severe stress can drive signaling toward cell death. Here, we review current understanding of the ISR signaling and how it regulates cell fate under diverse types of stress.
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                Author and article information

                Contributors
                ehkim@unist.ac.kr
                kschoi@ajou.ac.kr
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                13 January 2024
                13 January 2024
                January 2024
                : 15
                : 1
                : 48
                Affiliations
                [1 ]Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, ( https://ror.org/03tzb2h73) Suwon, Republic of Korea
                [2 ]Department of Biomedical Sciences, Ajou University Graduate School of Medicine, ( https://ror.org/03tzb2h73) Suwon, Republic of Korea
                [3 ]Department of Biochemistry and Structural Biology, University of Texas Health at San Antonio, ( https://ror.org/01kd65564) San Antonio, TX USA
                [4 ]Greehey Children’s Cancer Research Institute, University of Texas Health at San Antonio, ( https://ror.org/01kd65564) San Antonio, TX USA
                [5 ]GRID grid.267370.7, ISNI 0000 0004 0533 4667, Asan Institute for Life Sciences, Department of Convergence Medicine, Asan Medical Center, , University of Ulsan College of Medicine, ; Seoul, Korea
                [6 ]Department of Bioengineering, College of Engineering, Hanyang University, ( https://ror.org/046865y68) Seoul, Korea
                [7 ]Department of Biological Sciences, Ulsan National Institute Science and Technology, ( https://ror.org/017cjz748) Ulsan, South Korea
                Author information
                http://orcid.org/0000-0003-0342-2308
                http://orcid.org/0000-0002-0194-4631
                http://orcid.org/0000-0002-4665-5999
                http://orcid.org/0000-0003-0959-5488
                http://orcid.org/0000-0002-5265-7640
                http://orcid.org/0000-0002-9466-4531
                http://orcid.org/0000-0001-7143-7769
                http://orcid.org/0000-0003-2331-0856
                Article
                6434
                10.1038/s41419-024-06434-x
                10787777
                38218922
                4b295044-c73d-468c-a1fa-c6787a6a8823
                © The Author(s) 2024

                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
                : 20 June 2023
                : 19 December 2023
                : 4 January 2024
                Funding
                Funded by: National Research Foundation of Korea (NRF), Mid-career Research Program (NRF-2023R1A2C2006580) & 2020R1A6A1A0343539
                Categories
                Article
                Custom metadata
                © Associazione Differenziamento e Morte Cellulare ADMC 2024

                Cell biology
                cell death,targeted therapies
                Cell biology
                cell death, targeted therapies

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