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Sadida HQ, Abdulla A, Marzooqi SA, Hashem S, Macha MA, Akil ASAS, Bhat AA. Epigenetic modifications: Key players in cancer heterogeneity and drug resistance. Transl Oncol 2024; 39:101821. [PMID: 37931371 PMCID: PMC10654239 DOI: 10.1016/j.tranon.2023.101821] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 10/12/2023] [Accepted: 10/31/2023] [Indexed: 11/08/2023] Open
Abstract
Cancer heterogeneity and drug resistance remain pivotal obstacles in effective cancer treatment and management. One major contributor to these challenges is epigenetic modifications - gene regulation that does not involve changes to the DNA sequence itself but significantly impacts gene expression. As we elucidate these phenomena, we underscore the pivotal role of epigenetic modifications in regulating gene expression, contributing to cellular diversity, and driving adaptive changes that can instigate therapeutic resistance. This review dissects essential epigenetic modifications - DNA methylation, histone modifications, and chromatin remodeling - illustrating their significant yet complex contributions to cancer biology. While these changes offer potential avenues for therapeutic intervention due to their reversible nature, the interplay of epigenetic and genetic changes in cancer cells presents unique challenges that must be addressed to harness their full potential. By critically analyzing the current research landscape, we identify knowledge gaps and propose future research directions, exploring the potential of epigenetic therapies and discussing the obstacles in translating these concepts into effective treatments. This comprehensive review aims to stimulate further research and aid in developing innovative, patient-centered cancer therapies. Understanding the role of epigenetic modifications in cancer heterogeneity and drug resistance is critical for scientific advancement and paves the way towards improving patient outcomes in the fight against this formidable disease.
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Affiliation(s)
- Hana Q Sadida
- Laboratory of Precision Medicine in Diabetes, Obesity and Cancer, Department of Population Genetics, Sidra Medicine, Doha 26999, Qatar
| | - Alanoud Abdulla
- Laboratory of Precision Medicine in Diabetes, Obesity and Cancer, Department of Population Genetics, Sidra Medicine, Doha 26999, Qatar
| | - Sara Al Marzooqi
- Laboratory of Precision Medicine in Diabetes, Obesity and Cancer, Department of Population Genetics, Sidra Medicine, Doha 26999, Qatar
| | - Sheema Hashem
- Laboratory of Genomic Medicine, Department of Population Genetics, Sidra Medicine, Doha 26999, Qatar
| | - Muzafar A Macha
- Watson-Crick Centre for Molecular Medicine, Islamic University of Science and Technology, Jammu & Kashmir, India
| | - Ammira S Al-Shabeeb Akil
- Laboratory of Precision Medicine in Diabetes, Obesity and Cancer, Department of Population Genetics, Sidra Medicine, Doha 26999, Qatar.
| | - Ajaz A Bhat
- Laboratory of Precision Medicine in Diabetes, Obesity and Cancer, Department of Population Genetics, Sidra Medicine, Doha 26999, Qatar.
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2
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Ma W, Luo L, Liang K, Liu T, Su J, Wang Y, Li J, Zhou SK, Shyh-Chang N. XAI-enabled neural network analysis of metabolite spatial distributions. Anal Bioanal Chem 2023; 415:2819-2830. [PMID: 37083759 DOI: 10.1007/s00216-023-04694-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 04/22/2023]
Abstract
We used deep neural networks to process the mass spectrometry imaging (MSI) data of mouse muscle (young vs aged) and human cancer (tumor vs normal adjacent) tissues, with the aim of using explainable artificial intelligence (XAI) methods to rapidly identify biomarkers that can distinguish different classes of tissues, from several thousands of metabolite features. We also modified classic neural network architectures to construct a deep convolutional neural network that is more suitable for processing high-dimensional MSI data directly, instead of using dimension reduction techniques, and compared it to seven other machine learning analysis methods' performance in classification accuracy. After ascertaining the superiority of Channel-ResNet10, we used a novel channel selection-based XAI method to identify the key metabolite features that were responsible for its learning accuracy. These key metabolite biomarkers were then processed using MetaboAnalyst for pathway enrichment mapping. We found that Channel-ResNet10 was superior to seven other machine learning methods for MSI analysis, reaching > 98% accuracy in muscle aging and colorectal cancer datasets. We also used a novel channel selection-based XAI method to find that in young and aged muscle tissues, the differentially distributed metabolite biomarkers were especially enriched in the propanoate metabolism pathway, suggesting it as a novel target pathway for anti-aging therapy.
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Affiliation(s)
- Wenwu Ma
- Department of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- State Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Lanfang Luo
- State Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Kun Liang
- State Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Taoyan Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Jiali Su
- State Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Yuefan Wang
- State Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Jun Li
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- Center for Medical Imaging Robotics, Analytic Computing & Learning (MIRACLE), School of Biomedical Engineering &, Suzhou Institute for Advance Research, University of Science and Technology of China, Suzhou, China
| | - S Kevin Zhou
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- Center for Medical Imaging Robotics, Analytic Computing & Learning (MIRACLE), School of Biomedical Engineering &, Suzhou Institute for Advance Research, University of Science and Technology of China, Suzhou, China
| | - Ng Shyh-Chang
- State Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Beijing, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China.
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3
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Peng J, Zhang WJ, Zhang Q, Su YH, Tang LP. The dynamics of chromatin states mediated by epigenetic modifications during somatic cell reprogramming. Front Cell Dev Biol 2023; 11:1097780. [PMID: 36727112 PMCID: PMC9884706 DOI: 10.3389/fcell.2023.1097780] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/05/2023] [Indexed: 01/17/2023] Open
Abstract
Somatic cell reprogramming (SCR) is the conversion of differentiated somatic cells into totipotent or pluripotent cells through a variety of methods. Somatic cell reprogramming also provides a platform to investigate the role of chromatin-based factors in establishing and maintaining totipotency or pluripotency, since high expression of totipotency- or pluripotency-related genes usually require an active chromatin state. Several studies in plants or mammals have recently shed light on the molecular mechanisms by which epigenetic modifications regulate the expression of totipotency or pluripotency genes by altering their chromatin states. In this review, we present a comprehensive overview of the dynamic changes in epigenetic modifications and chromatin states during reprogramming from somatic cells to totipotent or pluripotent cells. In addition, we illustrate the potential role of DNA methylation, histone modifications, histone variants, and chromatin remodeling during somatic cell reprogramming, which will pave the way to developing reliable strategies for efficient cellular reprogramming.
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Affiliation(s)
| | | | | | - Ying Hua Su
- *Correspondence: Ying Hua Su, ; Li Ping Tang,
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4
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Sinniah E, Wu Z, Shen S, Naval-Sanchez M, Chen X, Lim J, Helfer A, Iyer A, Tng J, Lucke AJ, Reid RC, Redd MA, Nefzger CM, Fairlie DP, Palpant NJ. Temporal perturbation of histone deacetylase activity reveals a requirement for HDAC1-3 in mesendoderm cell differentiation. Cell Rep 2022; 39:110818. [PMID: 35584683 DOI: 10.1016/j.celrep.2022.110818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 03/22/2022] [Accepted: 04/20/2022] [Indexed: 11/03/2022] Open
Abstract
Histone deacetylases (HDACs) are a class of enzymes that control chromatin state and influence cell fate. We evaluated the chromatin accessibility and transcriptome dynamics of zinc-containing HDACs during cell differentiation in vitro coupled with chemical perturbation to identify the role of HDACs in mesendoderm cell fate specification. Single-cell RNA sequencing analyses of HDAC expression during human pluripotent stem cell (hPSC) differentiation in vitro and mouse gastrulation in vivo reveal a unique association of HDAC1 and -3 with mesendoderm gene programs during exit from pluripotency. Functional perturbation with small molecules reveals that inhibition of HDAC1 and -3, but not HDAC2, induces mesoderm while impeding endoderm and early cardiac progenitor specification. These data identify unique biological functions of the structurally homologous enzymes HDAC1-3 in influencing hPSC differentiation from pluripotency toward mesendodermal and cardiac progenitor populations.
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Affiliation(s)
- Enakshi Sinniah
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Zhixuan Wu
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Sophie Shen
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Marina Naval-Sanchez
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Xiaoli Chen
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Junxian Lim
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia; ARC Centre of Excellence for Advanced Molecular Imaging, Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Abbigail Helfer
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Abishek Iyer
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia; ARC Centre of Excellence for Advanced Molecular Imaging, Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Jiahui Tng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia; ARC Centre of Excellence for Advanced Molecular Imaging, Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Andrew J Lucke
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia; ARC Centre of Excellence for Advanced Molecular Imaging, Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Robert C Reid
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia; ARC Centre of Excellence for Advanced Molecular Imaging, Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Meredith A Redd
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Christian M Nefzger
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - David P Fairlie
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia; ARC Centre of Excellence for Advanced Molecular Imaging, Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Nathan J Palpant
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
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5
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Jin Y, Liu T, Luo H, Liu Y, Liu D. Targeting Epigenetic Regulatory Enzymes for Cancer Therapeutics: Novel Small-Molecule Epidrug Development. Front Oncol 2022; 12:848221. [PMID: 35419278 PMCID: PMC8995554 DOI: 10.3389/fonc.2022.848221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/04/2022] [Indexed: 11/13/2022] Open
Abstract
Dysregulation of the epigenetic enzyme-mediated transcription of oncogenes or tumor suppressor genes is closely associated with the occurrence, progression, and prognosis of tumors. Based on the reversibility of epigenetic mechanisms, small-molecule compounds that target epigenetic regulation have become promising therapeutics. These compounds target epigenetic regulatory enzymes, including DNA methylases, histone modifiers (methylation and acetylation), enzymes that specifically recognize post-translational modifications, chromatin-remodeling enzymes, and post-transcriptional regulators. Few compounds have been used in clinical trials and exhibit certain therapeutic effects. Herein, we summarize the classification and therapeutic roles of compounds that target epigenetic regulatory enzymes in cancer treatment. Finally, we highlight how the natural compounds berberine and ginsenosides can target epigenetic regulatory enzymes to treat cancer.
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Affiliation(s)
- Ye Jin
- School of Pharmacy, Changchun University of Chinese Medicine, Changchun, China
| | - Tianjia Liu
- School of Pharmacy, Changchun University of Chinese Medicine, Changchun, China
| | - Haoming Luo
- School of Pharmacy, Changchun University of Chinese Medicine, Changchun, China
| | - Yangyang Liu
- Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China
| | - Da Liu
- School of Pharmacy, Changchun University of Chinese Medicine, Changchun, China
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6
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Nussinov R, Tsai CJ, Jang H. Allostery, and how to define and measure signal transduction. Biophys Chem 2022; 283:106766. [PMID: 35121384 PMCID: PMC8898294 DOI: 10.1016/j.bpc.2022.106766] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 12/15/2022]
Abstract
Here we ask: What is productive signaling? How to define it, how to measure it, and most of all, what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream node in a specific cell? These questions have either not been considered or not entirely resolved. The requirements for the signal to propagate downstream to activate (repress) transcription have not been considered either. Yet, the questions are pivotal to clarify, especially in diseases such as cancer where determination of signal propagation can point to cell proliferation and to emerging drug resistance, and to neurodevelopmental disorders, such as RASopathy, autism, attention-deficit/hyperactivity disorder (ADHD), and cerebral palsy. Here we propose a framework for signal transduction from an upstream to a downstream node addressing these questions. Defining cellular processes, experimentally measuring them, and devising powerful computational AI-powered algorithms that exploit the measurements, are essential for quantitative science.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
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7
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Ma S, Shyh-Chang N. The Metabaging Cycle. Cell Prolif 2022; 55:e13197. [PMID: 35106869 PMCID: PMC8891548 DOI: 10.1111/cpr.13197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/09/2022] [Accepted: 01/19/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Shilin Ma
- State Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Beijing, China.,Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Ng Shyh-Chang
- State Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Beijing, China.,Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
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8
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MORC2 Interactome: Its Involvement in Metabolism and Cancer. Biophys Rev 2021; 13:507-514. [PMID: 34471435 DOI: 10.1007/s12551-021-00812-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/31/2021] [Indexed: 12/21/2022] Open
Abstract
Microrchidia 2 (MORC2) is an emerging chromatin modifier with a role in chromatin remodeling and epigenetic regulation. MORC2 is found to be upregulated in most cancers, playing a significant role in tumorigenesis and tumor metastasis. Recent studies have demonstrated that MORC2 is a scaffolding protein, which interacts with the proteins involved in DNA repair, chromatin remodeling, lipogenesis, and glucose metabolism. In this review, we discuss the domain architecture and cellular and subcellular localization of MORC2. Further, we highlight MORC2-specific interacting partners involved in metabolic reprogramming and other pathological functions such as cancer progression and metastasis.
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The Potential of Induced Pluripotent Stem Cells to Treat and Model Alzheimer's Disease. Stem Cells Int 2021; 2021:5511630. [PMID: 34122554 PMCID: PMC8172295 DOI: 10.1155/2021/5511630] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/20/2021] [Accepted: 05/19/2021] [Indexed: 12/13/2022] Open
Abstract
An estimated 6.2 million Americans aged 65 or older are currently living with Alzheimer's disease (AD), a neurodegenerative disease that disrupts an individual's ability to function independently through the degeneration of key regions in the brain, including but not limited to the hippocampus, the prefrontal cortex, and the motor cortex. The cause of this degeneration is not known, but research has found two proteins that undergo posttranslational modifications: tau, a protein concentrated in the axons of neurons, and amyloid precursor protein (APP), a protein concentrated near the synapse. Through mechanisms that have yet to be elucidated, the accumulation of these two proteins in their abnormal aggregate forms leads to the neurodegeneration that is characteristic of AD. Until the invention of induced pluripotent stem cells (iPSCs) in 2006, the bulk of research was carried out using transgenic animal models that offered little promise in their ability to translate well from benchtop to bedside, creating a bottleneck in the development of therapeutics. However, with iPSC, patient-specific cell cultures can be utilized to create models based on human cells. These human cells have the potential to avoid issues in translatability that have plagued animal models by providing researchers with a model that closely resembles and mimics the neurons found in humans. By using human iPSC technology, researchers can create more accurate models of AD ex vivo while also focusing on regenerative medicine using iPSC in vivo. The following review focuses on the current uses of iPSC and how they have the potential to regenerate damaged neuronal tissue, in the hopes that these technologies can assist in getting through the bottleneck of AD therapeutic research.
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Yao Z, Chen Y, Cao W, Shyh-Chang N. Chromatin-modifying drugs and metabolites in cell fate control. Cell Prolif 2020; 53:e12898. [PMID: 32979011 PMCID: PMC7653270 DOI: 10.1111/cpr.12898] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/05/2020] [Accepted: 08/11/2020] [Indexed: 12/13/2022] Open
Abstract
For multicellular organisms, it is essential to produce a variety of specialized cells to perform a dazzling panoply of functions. Chromatin plays a vital role in determining cellular identities, and it dynamically regulates gene expression in response to changing nutrient metabolism and environmental conditions. Intermediates produced by cellular metabolic pathways are used as cofactors or substrates for chromatin modification. Drug analogues of metabolites that regulate chromatin‐modifying enzyme reactions can also regulate cell fate by adjusting chromatin organization. In recent years, there have been many studies about how chromatin‐modifying drug molecules or metabolites can interact with chromatin to regulate cell fate. In this review, we systematically discuss how DNA and histone‐modifying molecules alter cell fate by regulating chromatin conformation and propose a mechanistic model that explains the process of cell fate transitions in a concise and qualitative manner.
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Affiliation(s)
- Ziyue Yao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.,Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yu Chen
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.,Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wenhua Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.,Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Ng Shyh-Chang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.,Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
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