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Ren H, Wu Z, Tan J, Tao H, Zou W, Cao Z, Wen B, Cai Z, Du J, Deng Z. Co-delivery Nano System of MS-275 and V-9302 Induces Pyroptosis and Enhances Anti-Tumor Immunity Against Uveal Melanoma. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2404375. [PMID: 38889339 DOI: 10.1002/advs.202404375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 06/10/2024] [Indexed: 06/20/2024]
Abstract
In the treatment of uveal melanoma (UVM), histone deacetylase inhibitors (HDACi) have emerged as a promising epigenetic therapy. However, their clinical efficacy is hindered by the suboptimal pharmacokinetics and the strong self-rescue of tumor cells. To overcome these limitations, reactive oxygen species (ROS)-responsive nanoparticles (NPs) are designed that encapsulate HDACi MS-275 and the glutamine metabolism inhibitor V-9302. Upon reaching the tumor microenvironment, these NPs can disintegrate, thereby releasing MS-275 to increase the level of ROS and V-9302 to reduce the production of glutathione (GSH) related to self-rescue. These synergistic effects lead to a lethal ROS storm and induce cell pyroptosis. When combined with programmed cell death protein 1 monoclonal antibodies (α-PD-1), these NPs facilitate immune cell infiltration, improving anti-tumor immunity, converting "immune-cold" tumors into "immune-hot" tumors, and enhancing immune memory in mice. The findings present a nano-delivery strategy for the co-delivery of epigenetic therapeutics and metabolic inhibitors, which induces pyroptosis in tumors cells and improves the effectiveness of chemotherapy and immunotherapy.
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Affiliation(s)
- Hong Ren
- Department of Ophthalmology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Zhenkai Wu
- Department of Ophthalmology, Changde Hospital, Xiangya School of Medicine, Central South University, Changde, Hunan, 415000, China
- Department of Ophthalmology, The first people's hospital of Changde city, Changde, Hunan, 415000, China
| | - Jia Tan
- Eye Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Hunan Key Laboratory of Ophthalmology and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Hui Tao
- Department of Ophthalmology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Wangyuan Zou
- Department of Anesthesiology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Zheng Cao
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90066, USA
| | - Binyu Wen
- Department of Ophthalmology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Ziyi Cai
- Department of Ophthalmology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Jiaqi Du
- Department of Ophthalmology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Zhihong Deng
- Department of Ophthalmology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
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2
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Barata T, Pereira V, Pires das Neves R, Rocha M. Reconstruction of cell-specific models capturing the influence of metabolism on DNA methylation in cancer. Comput Biol Med 2024; 170:108052. [PMID: 38308868 DOI: 10.1016/j.compbiomed.2024.108052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 01/18/2024] [Accepted: 01/26/2024] [Indexed: 02/05/2024]
Abstract
The imbalance of epigenetic regulatory mechanisms such as DNA methylation, which can promote aberrant gene expression profiles without affecting the DNA sequence, may cause the deregulation of signaling, regulatory, and metabolic processes, contributing to a cancerous phenotype. Since some metabolites are substrates and cofactors of epigenetic regulators, their availability can be affected by characteristic cancer cell metabolic shifts, feeding cancer onset and progression through epigenetic deregulation. Hence, there is a need to study the influence of cancer metabolic reprogramming in DNA methylation to design new effective treatments. In this study, a generic Genome-Scale Metabolic Model (GSMM) of a human cell, integrating DNA methylation or demethylation reactions, was obtained and used for the reconstruction of Genome-Scale Metabolic Models enhanced with Enzymatic Constraints using Kinetic and Omics data (GECKOs) of 31 cancer cell lines. Furthermore, cell-line-specific DNA methylation levels were included in the models, as coefficients of a DNA composition pseudo-reaction, to depict the influence of metabolism over global DNA methylation in each of the cancer cell lines. Flux simulations demonstrated the ability of these models to provide simulated fluxes of exchange reactions similar to the equivalent experimentally measured uptake/secretion rates and to make good functional predictions. In addition, simulations found metabolic pathways, reactions and enzymes directly or inversely associated with the gene promoter methylation. Two potential candidates for targeted cancer epigenetic therapy were identified.
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Affiliation(s)
- Tânia Barata
- CNC - Center for Neuroscience and Cell Biology, University of Coimbra, 3004-517 Coimbra, Portugal; CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-517 Coimbra, Portugal
| | - Vítor Pereira
- Centre of Biological Engineering, University of Minho - Campus de Gualtar, Braga, Portugal; LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Ricardo Pires das Neves
- CNC - Center for Neuroscience and Cell Biology, University of Coimbra, 3004-517 Coimbra, Portugal; CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-517 Coimbra, Portugal; IIIUC-Institute of Interdisciplinary Research, University of Coimbra, 3030-789 Coimbra, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho - Campus de Gualtar, Braga, Portugal; LABBELS - Associate Laboratory, Braga/Guimarães, Portugal; Department of Informatics, University of Minho, Portugal.
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Eames A, Chandrasekaran S. Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth. PNAS NEXUS 2024; 3:pgae013. [PMID: 38292544 PMCID: PMC10825626 DOI: 10.1093/pnasnexus/pgae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 12/28/2023] [Indexed: 02/01/2024]
Abstract
Quiescence, a temporary withdrawal from the cell cycle, plays a key role in tissue homeostasis and regeneration. Quiescence is increasingly viewed as a continuum between shallow and deep quiescence, reflecting different potentials to proliferate. The depth of quiescence is altered in a range of diseases and during aging. Here, we leveraged genome-scale metabolic modeling (GEM) to define the metabolic and epigenetic changes that take place with quiescence deepening. We discovered contrasting changes in lipid catabolism and anabolism and diverging trends in histone methylation and acetylation. We then built a multi-cell type machine learning model that accurately predicts quiescence depth in diverse biological contexts. Using both machine learning and genome-scale flux simulations, we performed high-throughput screening of chemical and genetic modulators of quiescence and identified novel small molecule and genetic modulators with relevance to cancer and aging.
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Affiliation(s)
- Alec Eames
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
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4
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Li D, Jing J, Dong X, Zhang C, Wang J, Wan X. Activating transcription factor 3: A potential therapeutic target for inflammatory pulmonary diseases. Immun Inflamm Dis 2023; 11:e1028. [PMID: 37773692 PMCID: PMC10515505 DOI: 10.1002/iid3.1028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 09/05/2023] [Accepted: 09/09/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND Activating transcription factor 3 (ATF3) is a nuclear protein that is widely expressed in a variety of cells. It is a stress-inducible transcription gene and a member of the activating transcription factor/cAMP responsive element-binding protein (ATF/CREB) family. METHODS The comprehensive literature review was conducted by searching PubMed and Google Scholar. Search terms used were "ATF3", "ATF3 and (ALI or ARDS)", "ATF3 and COPD", "ATF3 and PF", and "ATF3 and Posttranslational modifications". RESULTS Recent studies have shown that ATF3 plays a critical role in many inflammatory pulmonary diseases, including acute lung injury (ALI)/acute respiratory distress syndrome (ARDS), chronic obstructive pulmonary disease (COPD), and pulmonary fibrosis (PF). ATF3 participates in many signaling pathways and complex pathophysiological processes, such as inflammation, immunity, endoplasmic reticulum stress, and cell proliferation. However, the role of ATF3 in current studies is controversial, and there are reports showing that ATF3 plays different roles in different pulmonary diseases. CONCLUSIONS In this review, we first summarized the structure, function, and mechanism of ATF3 in various inflammatory pulmonary diseases. The impact of ATF3 on disease pathogenesis and the clinical implications was particularly focused on, with an overall aim to identify new targets for treating inflammatory pulmonary diseases.
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Affiliation(s)
- Dandan Li
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Juanjuan Jing
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xue Dong
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenyang Zhang
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jia Wang
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xianyao Wan
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
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5
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Ribeiro ML, Sánchez Vinces S, Mondragon L, Roué G. Epigenetic targets in B- and T-cell lymphomas: latest developments. Ther Adv Hematol 2023; 14:20406207231173485. [PMID: 37273421 PMCID: PMC10236259 DOI: 10.1177/20406207231173485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 04/17/2023] [Indexed: 06/06/2023] Open
Abstract
Non-Hodgkin's lymphomas (NHLs) comprise a diverse group of diseases, either of mature B-cell or of T-cell derivation, characterized by heterogeneous molecular features and clinical manifestations. While most of the patients are responsive to standard chemotherapy, immunotherapy, radiation and/or stem cell transplantation, relapsed and/or refractory cases still have a dismal outcome. Deep sequencing analysis have pointed out that epigenetic dysregulations, including mutations in epigenetic enzymes, such as chromatin modifiers and DNA methyltransferases (DNMTs), are prevalent in both B- cell and T-cell lymphomas. Accordingly, over the past decade, a large number of epigenetic-modifying agents have been developed and introduced into the clinical management of these entities, and a few specific inhibitors have already been approved for clinical use. Here we summarize the main epigenetic alterations described in B- and T-NHL, that further supported the clinical development of a selected set of epidrugs in determined diseases, including inhibitors of DNMTs, histone deacetylases (HDACs), and extra-terminal domain proteins (bromodomain and extra-terminal motif; BETs). Finally, we highlight the most promising future directions of research in this area, explaining how bioinformatics approaches can help to identify new epigenetic targets in B- and T-cell lymphoid neoplasms.
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Affiliation(s)
- Marcelo Lima Ribeiro
- Lymphoma Translational Group, Josep Carreras
Leukaemia Research Institute, Badalona, Spain
- Laboratory of Immunopharmacology and Molecular
Biology, Sao Francisco University Medical School, Braganca Paulista,
Brazil
| | - Salvador Sánchez Vinces
- Laboratory of Immunopharmacology and Molecular
Biology, Sao Francisco University Medical School, Braganca Paulista,
Brazil
| | - Laura Mondragon
- T Cell Lymphoma Group, Josep Carreras Leukaemia
Research Institute, IJC. Ctra de Can Ruti, Camí de les Escoles s/n, 08916
Badalona, Barcelona, Spain
| | - Gael Roué
- Lymphoma Translational Group, Josep Carreras
Leukaemia Research Institute, IJC. Ctra de Can Ruti, Camí de les Escoles
s/n, 08916 Badalona, Barcelona, Spain
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6
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González-Arrué N, Inostroza I, Conejeros R, Rivas-Astroza M. Phenotype-specific estimation of metabolic fluxes using gene expression data. iScience 2023; 26:106201. [PMID: 36915687 PMCID: PMC10006673 DOI: 10.1016/j.isci.2023.106201] [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: 09/22/2022] [Revised: 11/30/2022] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
A cell's genome influences its metabolism via the expression of enzyme-related genes, but transcriptome and fluxome are not perfectly correlated as post-transcriptional mechanisms also regulate reaction's kinetics. Here, we addressed the question: given a transcriptome, how unobserved mechanisms of reaction kinetics should be systematically accounted for when inferring the fluxome? To infer the most likely and least biased fluxome, we present Pheflux, a constraint-based model maximizing Shannon's entropy of fluxes per mRNA. Benchmarked against 13C fluxes of yeast and bacteria, Pheflux accurately estimates the carbon core metabolism. We applied Pheflux to thousands of normal and tumor cell transcriptomes obtained from The Cancer Genome Atlas. Pheflux showed statistically significantly higher glucose yields on lactate in breast, kidney, and bronchus-lung tumoral cells than their normal counterparts. Results are consistent with the Warburg effect, a hallmark of cancer metabolism, suggesting that Pheflux can be efficiently used to study the metabolism of eukaryotic cells.
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Affiliation(s)
- Nicolás González-Arrué
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Isidora Inostroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Raúl Conejeros
- Pontificia Universidad Católica de Valparaíso, Escuela de Ingeniería Bioquímica, Valparaíso, 2362803, Chile
| | - Marcelo Rivas-Astroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
- Corresponding author
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7
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Sowers ML, Tang H, Singh VK, Khan A, Mishra A, Restrepo BI, Jagannath C, Zhang K. Multi-OMICs analysis reveals metabolic and epigenetic changes associated with macrophage polarization. J Biol Chem 2022; 298:102418. [PMID: 36030823 PMCID: PMC9525912 DOI: 10.1016/j.jbc.2022.102418] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 12/04/2022] Open
Abstract
Macrophages (MФ) are an essential immune cell for defense and repair that travel to different tissues and adapt based on local stimuli. A critical factor that may govern their polarization is the crosstalk between metabolism and epigenetics. However, simultaneous measurements of metabolites, epigenetics, and proteins (phenotype) have been a major technical challenge. To address this, we have developed a novel triomics approach using mass spectrometry to comprehensively analyze metabolites, proteins, and histone modifications in a single sample. To demonstrate this technique, we investigated the metabolic-epigenetic-phenotype axis following polarization of human blood–derived monocytes into either ‘proinflammatory M1-’ or ‘anti-inflammatory M2-’ MФs. We report here a complex relationship between arginine, tryptophan, glucose, and the citric acid cycle metabolism, protein and histone post-translational modifications, and human macrophage polarization that was previously not described. Surprisingly, M1-MФs had globally reduced histone acetylation levels but high levels of acetylated amino acids. This suggests acetyl-CoA was diverted, in part, toward acetylated amino acids. Consistent with this, stable isotope tracing of glucose revealed reduced usage of acetyl-CoA for histone acetylation in M1-MФs. Furthermore, isotope tracing also revealed MФs uncoupled glycolysis from the tricarboxylic acid cycle, as evidenced by poor isotope enrichment of succinate. M2-MФs had high levels of kynurenine and serotonin, which are reported to have immune-suppressive effects. Kynurenine is upstream of de novo NAD+ metabolism that is a necessary cofactor for Sirtuin-type histone deacetylases. Taken together, we demonstrate a complex interplay between metabolism and epigenetics that may ultimately influence cell phenotype.
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Affiliation(s)
- Mark L Sowers
- Dept. of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX
| | - Hui Tang
- Dept. of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX
| | - Vipul K Singh
- Dept. of Pathology and Genomic Medicine, Center for Molecular and Translational Human Infectious Diseases Research Houston Methodist Research Institute, Weill-Cornell Medicine, Houston, TX
| | - Arshad Khan
- Dept. of Pathology and Genomic Medicine, Center for Molecular and Translational Human Infectious Diseases Research Houston Methodist Research Institute, Weill-Cornell Medicine, Houston, TX
| | - Abhishek Mishra
- Dept. of Pathology and Genomic Medicine, Center for Molecular and Translational Human Infectious Diseases Research Houston Methodist Research Institute, Weill-Cornell Medicine, Houston, TX
| | | | - Chinnaswamy Jagannath
- Dept. of Pathology and Genomic Medicine, Center for Molecular and Translational Human Infectious Diseases Research Houston Methodist Research Institute, Weill-Cornell Medicine, Houston, TX.
| | - Kangling Zhang
- Dept. of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX.
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8
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Chung CH, Chandrasekaran S. A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions. PNAS NEXUS 2022; 1:pgac132. [PMID: 36016709 PMCID: PMC9396445 DOI: 10.1093/pnasnexus/pgac132] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 07/19/2022] [Indexed: 02/06/2023]
Abstract
Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for Escherichia coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two subpopulations of E. coli cells defined by the levels of three proteins (eno, fadB, and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.
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Affiliation(s)
- Carolina H Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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9
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Genome-Wide Analysis of Yeast Metabolic Cycle through Metabolic Network Models Reveals Superiority of Integrated ATAC-seq Data over RNA-seq Data. mSystems 2022; 7:e0134721. [PMID: 35695574 PMCID: PMC9239220 DOI: 10.1128/msystems.01347-21] [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] [Indexed: 11/20/2022] Open
Abstract
Saccharomyces cerevisiae undergoes robust oscillations to regulate its physiology for adaptation and survival under nutrient-limited conditions. Environmental cues can induce rhythmic metabolic alterations in order to facilitate the coordination of dynamic metabolic behaviors. Of such metabolic processes, the yeast metabolic cycle enables adaptation of the cells to varying nutritional status through oscillations in gene expression and metabolite production levels. In this process, yeast metabolism is altered between diverse cellular states based on changing oxygen consumption levels: quiescent (reductive charging [RC]), growth (oxidative [OX]), and proliferation (reductive building [RB]) phases. We characterized metabolic alterations during the yeast metabolic cycle using a variety of approaches. Gene expression levels are widely used for condition-specific metabolic simulations, whereas the use of epigenetic information in metabolic modeling is still limited despite the clear relationship between epigenetics and metabolism. This prompted us to investigate the contribution of epigenomic information to metabolic predictions for progression of the yeast metabolic cycle. In this regard, we determined altered pathways through the prediction of regulated reactions and corresponding model genes relying on differential chromatin accessibility levels. The predicted metabolic alterations were confirmed via data analysis and literature. We subsequently utilized RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data sets in the contextualization of the yeast model. The use of ATAC-seq data considerably enhanced the predictive capability of the model. To the best of our knowledge, this is the first attempt to use genome-wide chromatin accessibility data in metabolic modeling. The preliminary results showed that epigenomic data sets can pave the way for more accurate metabolic simulations. IMPORTANCE Dynamic chromatin organization mediates the emergence of condition-specific phenotypes in eukaryotic organisms. Saccharomyces cerevisiae can alter its metabolic profile via regulation of genome accessibility and robust transcriptional oscillations under nutrient-limited conditions. Thus, both epigenetic information and transcriptomic information are crucial in the understanding of condition-specific metabolic behavior in this organism. Based on genome-wide alterations in chromatin accessibility and transcription, we investigated the yeast metabolic cycle, which is a remarkable example of coordinated and dynamic yeast behavior. In this regard, we assessed the use of ATAC-seq and RNA-seq data sets in condition-specific metabolic modeling. To our knowledge, this is the first attempt to use chromatin accessibility data in the reconstruction of context-specific metabolic models, despite the extensive use of transcriptomic data. As a result of comparative analyses, we propose that the incorporation of epigenetic information is a promising approach in the accurate prediction of metabolic dynamics.
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10
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Assante G, Chandrasekaran S, Ng S, Tourna A, Chung CH, Isse KA, Banks JL, Soffientini U, Filippi C, Dhawan A, Liu M, Rozen SG, Hoare M, Campbell P, Ballard JWO, Turner N, Morris MJ, Chokshi S, Youngson NA. Acetyl-CoA metabolism drives epigenome change and contributes to carcinogenesis risk in fatty liver disease. Genome Med 2022; 14:67. [PMID: 35739588 PMCID: PMC9219160 DOI: 10.1186/s13073-022-01071-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 06/16/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The incidence of non-alcoholic fatty liver disease (NAFLD)-associated hepatocellular carcinoma (HCC) is increasing worldwide, but the steps in precancerous hepatocytes which lead to HCC driver mutations are not well understood. Here we provide evidence that metabolically driven histone hyperacetylation in steatotic hepatocytes can increase DNA damage to initiate carcinogenesis. METHODS Global epigenetic state was assessed in liver samples from high-fat diet or high-fructose diet rodent models, as well as in cultured immortalized human hepatocytes (IHH cells). The mechanisms linking steatosis, histone acetylation and DNA damage were investigated by computational metabolic modelling as well as through manipulation of IHH cells with metabolic and epigenetic inhibitors. Chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) and transcriptome (RNA-seq) analyses were performed on IHH cells. Mutation locations and patterns were compared between the IHH cell model and genome sequence data from preneoplastic fatty liver samples from patients with alcohol-related liver disease and NAFLD. RESULTS Genome-wide histone acetylation was increased in steatotic livers of rodents fed high-fructose or high-fat diet. In vitro, steatosis relaxed chromatin and increased DNA damage marker γH2AX, which was reversed by inhibiting acetyl-CoA production. Steatosis-associated acetylation and γH2AX were enriched at gene clusters in telomere-proximal regions which contained HCC tumour suppressors in hepatocytes and human fatty livers. Regions of metabolically driven epigenetic change also had increased levels of DNA mutation in non-cancerous tissue from NAFLD and alcohol-related liver disease patients. Finally, genome-scale network modelling indicated that redox balance could be a key contributor to this mechanism. CONCLUSIONS Abnormal histone hyperacetylation facilitates DNA damage in steatotic hepatocytes and is a potential initiating event in hepatocellular carcinogenesis.
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Affiliation(s)
- Gabriella Assante
- Institute of Hepatology, Foundation for Liver Research, 111 Coldharbour Lane, London, SE5 9NT, UK
- King's College London, Faculty of Life Sciences and Medicine, London, UK
| | - Sriram Chandrasekaran
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, 48109, USA
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Stanley Ng
- Wellcome Trust Sanger Institute, Cambridge, UK
| | - Aikaterini Tourna
- Institute of Hepatology, Foundation for Liver Research, 111 Coldharbour Lane, London, SE5 9NT, UK
- King's College London, Faculty of Life Sciences and Medicine, London, UK
| | - Carolina H Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kowsar A Isse
- Institute of Hepatology, Foundation for Liver Research, 111 Coldharbour Lane, London, SE5 9NT, UK
- King's College London, Faculty of Life Sciences and Medicine, London, UK
| | - Jasmine L Banks
- UNSW Sydney, Sydney, Australia
- Cellular Bioenergetics Laboratory, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
| | - Ugo Soffientini
- Institute of Hepatology, Foundation for Liver Research, 111 Coldharbour Lane, London, SE5 9NT, UK
- King's College London, Faculty of Life Sciences and Medicine, London, UK
| | - Celine Filippi
- Institute of Liver Studies, King's College Hospital, London, UK
| | - Anil Dhawan
- Institute of Liver Studies, King's College Hospital, London, UK
| | - Mo Liu
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Steven G Rozen
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Matthew Hoare
- CRUK Cambridge Institute, Cambridge, UK
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | | | - J William O Ballard
- Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, Melbourne, VIC, 3086, Australia
| | - Nigel Turner
- UNSW Sydney, Sydney, Australia
- Cellular Bioenergetics Laboratory, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
| | | | - Shilpa Chokshi
- Institute of Hepatology, Foundation for Liver Research, 111 Coldharbour Lane, London, SE5 9NT, UK
- King's College London, Faculty of Life Sciences and Medicine, London, UK
| | - Neil A Youngson
- Institute of Hepatology, Foundation for Liver Research, 111 Coldharbour Lane, London, SE5 9NT, UK.
- King's College London, Faculty of Life Sciences and Medicine, London, UK.
- UNSW Sydney, Sydney, Australia.
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11
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Yadav S, Virk R, Chung CH, Eduardo MB, VanDerway D, Chen D, Burdett K, Gao H, Zeng Z, Ranjan M, Cottone G, Xuei X, Chandrasekaran S, Backman V, Chatterton R, Khan SA, Clare SE. Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer. NPJ Breast Cancer 2022; 8:59. [PMID: 35508495 PMCID: PMC9068822 DOI: 10.1038/s41523-022-00422-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 03/31/2022] [Indexed: 12/13/2022] Open
Abstract
Improved understanding of local breast biology that favors the development of estrogen receptor negative (ER-) breast cancer (BC) would foster better prevention strategies. We have previously shown that overexpression of specific lipid metabolism genes is associated with the development of ER- BC. We now report results of exposure of MCF-10A and MCF-12A cells, and mammary organoids to representative medium- and long-chain polyunsaturated fatty acids. This exposure caused a dynamic and profound change in gene expression, accompanied by changes in chromatin packing density, chromatin accessibility, and histone posttranslational modifications (PTMs). We identified 38 metabolic reactions that showed significantly increased activity, including reactions related to one-carbon metabolism. Among these reactions are those that produce S-adenosyl-L-methionine for histone PTMs. Utilizing both an in-vitro model and samples from women at high risk for ER- BC, we show that lipid exposure engenders gene expression, signaling pathway activation, and histone marks associated with the development of ER- BC.
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Affiliation(s)
- Shivangi Yadav
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Ranya Virk
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208-2850, USA
| | - Carolina H Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - David VanDerway
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208-2850, USA
| | - Duojiao Chen
- Center of for Medical Genomics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Kirsten Burdett
- Department of Preventive Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Hongyu Gao
- Center of for Medical Genomics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Zexian Zeng
- Department of Data Sciences, Dana Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Manish Ranjan
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Gannon Cottone
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Xiaoling Xuei
- Center of for Medical Genomics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Vadim Backman
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208-2850, USA
| | - Robert Chatterton
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Seema Ahsan Khan
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
| | - Susan E Clare
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
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12
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Smith K, Shen F, Lee HJ, Chandrasekaran S. Metabolic signatures of regulation by phosphorylation and acetylation. iScience 2022; 25:103730. [PMID: 35072016 PMCID: PMC8762462 DOI: 10.1016/j.isci.2021.103730] [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: 06/04/2021] [Revised: 12/15/2021] [Accepted: 12/30/2021] [Indexed: 10/31/2022] Open
Abstract
Acetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets in E. coli, S. cerevisiae, and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits.
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Affiliation(s)
- Kirk Smith
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Fangzhou Shen
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ho Joon Lee
- Department of Genetics, Yale University, New Haven, CT 06510, USA.,Yale Center for Genome Analysis, Yale University, New Haven, CT 06510, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.,Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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13
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Mekala JR, Kurappalli RK, Ramalingam P, Moparthi NR. N-acetyl l-aspartate and Triacetin modulate tumor suppressor MicroRNA and class I and II HDAC gene expression induce apoptosis in Glioblastoma cancer cells in vitro. Life Sci 2021; 286:120024. [PMID: 34626605 DOI: 10.1016/j.lfs.2021.120024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 09/27/2021] [Accepted: 09/30/2021] [Indexed: 02/07/2023]
Abstract
Glioblastoma multiforme (GBM), grade IV glioma and is aggressive, malignant primary brain cancer. Altered expression and activity of epigenetic proteins such as histone deacetylases (HDACs) are involved in GBM metastasis. Also, acetates are important to brain metabolites that regulate cell proliferation and apoptosis. Here, we have examined the effect of the acetates on the cell-cycle. U87MG cancer cells treated with N-acetyl l-aspartate (NAA) and sodium acetate have exhibited G1 phase cell-cycle arrest whereas U87MG cells treated with Triacetin (TA), and potassium acetate has induced G2/M cell cycle arrest. We have observed inhibition of histone deacetylase (HDAC) mRNA levels in acetate treated U87MG cells. Interestingly, acetates-treated U87MG cells have shown a significant reduction in the mRNA level of class II HDACs than class I HDACs. Acetate treated cells have exhibited an enhanced expression of various microRNAs such as miR-15b, miR-92, miR-101, miR-155, miR-199, miR-200, miR-223, miR-16, and miR-17 that are involved in the inhibition of cancer cell proliferation, invasion, migration, and angiogenesis. Further, these acetate molecules regulate genes involved in mammalian target of rapamycin complex 2 (mTORC2) such as mammalian stress-activated protein kinase-interacting protein (mSIN1), protein observed with Rictor 2 (Protor 2), and protein kinase C α (PKCα). The present study reveals the possible involvement of the mTORC2 complex during acetate-mediated HDAC inhibition, as well as microRNA modulation. Furthermore, molecular modeling studies were employed to understand the binding mode of these acetate molecules to mTOR, Rapamycin-insensitive companion of mammalian target of rapamycin (Rictor), and HDAC-8 proteins. Thus in this study, we have identified the pivotal role of acetates in the modulation of mTOR complex, epigenetic genes and provide structural as well as functional insights that will help in future drug discovery against GBM cancer therapy.
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Affiliation(s)
- Janaki Ramaiah Mekala
- Functional Genomics and Disease Biology Laboratory, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India; Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India.
| | - Rohil Kumar Kurappalli
- Functional Genomics and Disease Biology Laboratory, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
| | - PrasannaSrinivasan Ramalingam
- Functional Genomics and Disease Biology Laboratory, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
| | - Nageswara Rao Moparthi
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India
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14
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King J, Patel M, Chandrasekaran S. Metabolism, HDACs, and HDAC Inhibitors: A Systems Biology Perspective. Metabolites 2021; 11:792. [PMID: 34822450 PMCID: PMC8620738 DOI: 10.3390/metabo11110792] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/15/2021] [Accepted: 11/17/2021] [Indexed: 01/15/2023] Open
Abstract
Histone deacetylases (HDACs) are epigenetic enzymes that play a central role in gene regulation and are sensitive to the metabolic state of the cell. The cross talk between metabolism and histone acetylation impacts numerous biological processes including development and immune function. HDAC inhibitors are being explored for treating cancers, viral infections, inflammation, neurodegenerative diseases, and metabolic disorders. However, how HDAC inhibitors impact cellular metabolism and how metabolism influences their potency is unclear. Discussed herein are recent applications and future potential of systems biology methods such as high throughput drug screens, cancer cell line profiling, single cell sequencing, proteomics, metabolomics, and computational modeling to uncover the interplay between metabolism, HDACs, and HDAC inhibitors. The synthesis of new systems technologies can ultimately help identify epigenomic and metabolic biomarkers for patient stratification and the design of effective therapeutics.
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Affiliation(s)
- Jacob King
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (J.K.); (M.P.)
| | - Maya Patel
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (J.K.); (M.P.)
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (J.K.); (M.P.)
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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15
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Chung CH, Lin DW, Eames A, Chandrasekaran S. Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms. Metabolites 2021; 11:606. [PMID: 34564422 PMCID: PMC8470976 DOI: 10.3390/metabo11090606] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/01/2021] [Accepted: 09/03/2021] [Indexed: 12/18/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.
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Affiliation(s)
- Carolina H. Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Da-Wei Lin
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Alec Eames
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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16
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Wang X, Dong Y, Zheng Y, Chen Y. Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective. J Genet Genomics 2021; 48:520-530. [PMID: 34362682 DOI: 10.1016/j.jgg.2021.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/07/2021] [Accepted: 05/11/2021] [Indexed: 12/21/2022]
Abstract
Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition (i.e. multiomics data) and analysis (i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network (MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.
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Affiliation(s)
- Xuezhu Wang
- The State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China
| | - Yucheng Dong
- The State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Yang Chen
- The State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China.
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17
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Dissecting Murine Muscle Stem Cell Aging through Regeneration Using Integrative Genomic Analysis. Cell Rep 2021; 32:107964. [PMID: 32726628 PMCID: PMC8025697 DOI: 10.1016/j.celrep.2020.107964] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 04/12/2020] [Accepted: 07/03/2020] [Indexed: 12/19/2022] Open
Abstract
During aging, there is a progressive loss of volume and function in skeletal muscle that impacts mobility and quality of life. The repair of skeletal muscle is regulated by tissue-resident stem cells called satellite cells (or muscle stem cells [MuSCs]), but in aging, MuSCs decrease in numbers and regenerative capacity. The transcriptional networks and epigenetic changes that confer diminished regenerative function in MuSCs as a result of natural aging are only partially understood. Herein, we use an integrative genomics approach to profile MuSCs from young and aged animals before and after injury. Integration of these datasets reveals aging impacts multiple regulatory changes through significant differences in gene expression, metabolic flux, chromatin accessibility, and patterns of transcription factor (TF) binding activities. Collectively, these datasets facilitate a deeper understanding of the regulation tissue-resident stem cells use during aging and healing.
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18
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Han XL, Du J, Zheng YD, Dai JJ, Lin SW, Zhang BY, Zhong FB, Lin ZG, Jiang SQ, Wei W, Fang ZY. CXCL1 Clone Evolution Induced by the HDAC Inhibitor Belinostat Might Be a Favorable Prognostic Indicator in Triple-Negative Breast Cancer. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5089371. [PMID: 33959656 PMCID: PMC8075662 DOI: 10.1155/2021/5089371] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 03/04/2021] [Accepted: 03/25/2021] [Indexed: 02/07/2023]
Abstract
Triple-negative breast cancer (TNBC) is the most lethal subtype of breast cancer due to its lack of treatment options. Patients with TNBC frequently develop resistance to chemotherapy. As epigenetic-based antineoplastic drugs, histone deacetylase inhibitors (HDACis) have achieved particular efficacy in lymphoma but are less efficacious in solid tumors, and the resistance mechanism remains poorly understood. In this study, the GSE129944 microarray dataset from the Gene Expression Omnibus database was downloaded, and fold changes at the transcriptome level of a TNBC line (MDA-MB-231) after treatment with belinostat were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to identify the critical biological processes. Construction and analysis of the protein-protein interaction (PPI) network were performed to screen candidate genes related to cancer prognosis. A total of 465 DEGs were identified, including 240 downregulated and 225 upregulated genes. The cytokine-cytokine receptor pathway was identified as being significantly changed. Furthermore, the expression of CXCL1 was implicated as a favorable factor in the overall survival of breast cancer patients. With in vitro approaches, we also showed that belinostat could induce the expression of CXCL1 in another 2 TNBC cell lines (BT-549 and HCC-1937). We speculate that belinostat-induced CXCL1 expression could be one of the results of the stress clone evolution of cells after HDACi treatment. These findings provide new insights into clone evolution during HDACi treatment, which might guide us to a novel perspective that various mutation-targeted treatments should be implemented during the whole treatment cycle.
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Affiliation(s)
- Xin-le Han
- Biomedical Research Institute, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong 518036, China
| | - Jun Du
- State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Disease Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China
| | - Ya-dan Zheng
- Biomedical Research Institute, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong 518036, China
| | - Jia-jing Dai
- Biomedical Research Institute, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong 518036, China
| | - Su-wen Lin
- Biomedical Research Institute, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong 518036, China
| | - Bing-yue Zhang
- Biomedical Research Institute, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong 518036, China
| | - Fu-bo Zhong
- Biomedical Research Institute, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong 518036, China
| | - Zhe-guang Lin
- Department of Biobank, Peking University Shenzhen Hospital, Shenzhen, Guangdong 518036, China
| | - Shu-qi Jiang
- Department of Biobank, Peking University Shenzhen Hospital, Shenzhen, Guangdong 518036, China
| | - Wei Wei
- Department of Thyroid and Breast Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong 518036, China
| | - Zheng-yu Fang
- Biomedical Research Institute, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong 518036, China
- Department of Biobank, Peking University Shenzhen Hospital, Shenzhen, Guangdong 518036, China
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19
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Zhang S, Gong Y, Li C, Yang W, Li L. Beyond regulations at DNA levels: A review of epigenetic therapeutics targeting cancer stem cells. Cell Prolif 2020; 54:e12963. [PMID: 33314500 PMCID: PMC7848960 DOI: 10.1111/cpr.12963] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 11/10/2020] [Accepted: 11/12/2020] [Indexed: 02/05/2023] Open
Abstract
In the past few years, the paramount role of cancer stem cells (CSCs), in terms of cancer initiation, proliferation, metastasis, invasion and chemoresistance, has been revealed by accumulating studies. However, this level of cellular plasticity cannot be entirely explained by genetic mutations. Research on epigenetic modifications as a complementary explanation for the properties of CSCs has been increasing over the past several years. Notably, therapeutic strategies are currently being developed in an effort to reverse aberrant epigenetic alterations using specific chemical inhibitors. In this review, we summarize the current understanding of CSCs and their role in cancer progression, and provide an overview of epigenetic alterations seen in CSCs. Importantly, we focus on primary cancer therapies that target the epigenetic modification of CSCs by the use of specific chemical inhibitors, such as histone deacetylase (HDAC) inhibitors, DNA methyltransferase (DNMT) inhibitors and microRNA‐based (miRNA‐based) therapeutics.
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Affiliation(s)
- Shunhao Zhang
- State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Sichuan Province, Chengdu, China
| | - Yanji Gong
- State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Temporomandibular Joint, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan Province, China.,State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan Province, China
| | - Chunjie Li
- State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan Province, China
| | - Wenbin Yang
- State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Sichuan Province, Chengdu, China
| | - Longjiang Li
- State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan Province, China
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20
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Suthers PF, Foster CJ, Sarkar D, Wang L, Maranas CD. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metab Eng 2020; 63:13-33. [PMID: 33310118 DOI: 10.1016/j.ymben.2020.11.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
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21
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Chowdhury S, Fong SS. Leveraging genome-scale metabolic models for human health applications. Curr Opin Biotechnol 2020; 66:267-276. [PMID: 33120253 DOI: 10.1016/j.copbio.2020.08.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 08/27/2020] [Accepted: 08/31/2020] [Indexed: 02/07/2023]
Abstract
Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.
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Affiliation(s)
- Shomeek Chowdhury
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Main Street, Richmond, 23284, VA, USA
| | - Stephen S Fong
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Main Street, Richmond, 23284, VA, USA; Chemical and Life Science Engineering, Virginia Commonwealth University, 601 West Main Street, Richmond, 23284, VA, USA.
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22
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Histone deacetylase inhibition by MS-275 potentiates glucose-stimulated insulin secretion without affecting glucose oxidation. Life Sci 2020; 257:118073. [PMID: 32663574 DOI: 10.1016/j.lfs.2020.118073] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 06/29/2020] [Accepted: 07/07/2020] [Indexed: 12/25/2022]
Abstract
AIMS The preservation of pancreatic beta-cell function is crucial for the treatment of type 2 diabetes. Inhibition of class I histone deacetylase (HDAC) has been proved to protect beta-cells from palmitate- or cytokine-induced apoptosis and increase insulin secretion. However, the underlying molecular mechanism is unclear. MAIN METHODS Rat islets were isolated for insulin secretion, real-time PCR, RNA- sequencing, ChIP-PCR, and oxygen consumption rate analysis after treated with the HDAC1 and HDAC3 inhibitor MS-275. KEY FINDINGS MS-275 pretreatment significantly potentiated insulin secretion from rat islets. RNA-sequencing revealed that multiple signaling pathways were involved in MS-275-regulated islet function. Cacna1g and Adcy1 in calcium and cAMP signaling pathways were up-regulated in MS-275-treated islets, which was validated by real-time PCR. The expressions of the two genes displayed a similar increase in islets isolated from mice treated with MS-275. Knockdown of HDAC1 elevated Cacna1g and Adcy1 expressions in islets. ChIP-sequencing analysis showed that the pan-HDAC inhibitor sodium butyrate increased H3K27 acetylation level in the upstream region of Adcy1 and the promoter region of Cacna1g. ChIP-PCR revealed a similar result in MS-275-treated rat islets. However, MS-275 had minor effect on glucose-induced oxygen consumption rate in rat islets. Unlike glucose, MS-275 did not alter the expressions of glucose-sensitive genes such as Glut2 and Gck, but elevated intracellular Ca2+ concentration in beta-cells. SIGNIFICANCE Our findings support the notion that MS-275-potentiated insulin secretion is involved in calcium and cAMP signaling-mediated gene expressions independent of glucose oxidation. Therefore, HDAC inhibition may serve as a therapeutic strategy for type 2 diabetes.
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Campit SE, Meliki A, Youngson NA, Chandrasekaran S. Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling. Bioessays 2020; 42:e2000083. [PMID: 32638413 DOI: 10.1002/bies.202000083] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/23/2020] [Indexed: 12/19/2022]
Abstract
Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism-epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic-epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.
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Affiliation(s)
- Scott E Campit
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Alia Meliki
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI, 48109, USA
| | - Neil A Youngson
- Institute of Hepatology, Foundation for Liver Research, London, SE5 9NT, UK.,Faculty of Life Sciences and Medicine, King's College London, London, WC2R 2LS, UK.,School of Medical Sciences, UNSW Sydney, Sydney, 2052, Australia
| | - Sriram Chandrasekaran
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, 48109, USA.,Center for Bioinformatics and Computational Medicine, Ann Arbor, MI, 48109, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.,Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
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Oruganty K, Campit SE, Mamde S, Lyssiotis CA, Chandrasekaran S. Common biochemical properties of metabolic genes recurrently dysregulated in tumors. Cancer Metab 2020; 8:5. [PMID: 32411371 PMCID: PMC7206696 DOI: 10.1186/s40170-020-0211-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/03/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Tumor initiation and progression are associated with numerous metabolic alterations. However, the biochemical drivers and constraints that contribute to metabolic gene dysregulation are unclear. METHODS Here, we present MetOncoFit, a computational model that integrates 142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topology, and reaction flux. MetOncoFit uses genome-scale metabolic modeling and machine-learning to quantify the relative importance of various metabolic features in predicting cancer metabolic gene expression, copy number variation, and survival data. RESULTS Using MetOncoFit, we performed a meta-analysis of 9 cancer types and over 4500 samples from TCGA, Prognoscan, and COSMIC tumor databases. MetOncoFit accurately predicted enzyme differential expression and its impact on patient survival using the 142 attributes of metabolic enzymes. Our analysis revealed that enzymes with high catalytic activity were frequently upregulated in many tumors and associated with poor survival. Topological analysis also identified specific metabolites that were hot spots of dysregulation. CONCLUSIONS MetOncoFit integrates a broad range of datasets to understand how biochemical and topological features influence metabolic gene dysregulation across various cancer types. MetOncoFit was able to achieve significantly higher accuracy in predicting differential expression, copy number variation, and patient survival than traditional modeling approaches. Overall, MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.
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Affiliation(s)
- Krishnadev Oruganty
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48105 USA
- Present Address: Genpact, New York, NY 10036 USA
| | - Scott Edward Campit
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48105 USA
| | - Sainath Mamde
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48105 USA
| | - Costas A. Lyssiotis
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48105 USA
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48105 USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48105 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
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Abstract
The metabolic activity of a mammalian cell changes dynamically over time and is tied to the changing metabolic demands of cellular processes such as cell differentiation and proliferation. While experimental tools like time-course metabolomics and flux tracing can measure the dynamics of a few pathways, they are unable to infer fluxes at the whole network level. To address this limitation, we have developed the Dynamic Flux Activity (DFA) algorithm, a genome-scale modeling approach that uses time-course metabolomics to predict dynamic flux rewiring during transitions between metabolic states. This chapter provides a protocol for applying DFA to characterize the dynamic metabolic activity of various cancer cell lines.
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Chandrasekaran S. Tying Metabolic Branches With Histone Tails Using Systems Biology. Epigenet Insights 2019; 12:2516865719869683. [PMID: 31448363 PMCID: PMC6689906 DOI: 10.1177/2516865719869683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 07/22/2019] [Indexed: 02/06/2023] Open
Abstract
Histone modifications represent an innate cellular mechanism to link nutritional status to gene expression. Metabolites such as acetyl-CoA and S-adenosyl methionine influence gene expression by serving as substrates for modification of histones. Yet, we lack a predictive model for determining histone modification levels based on cellular metabolic state. The numerous metabolic pathways that intersect with histone marks makes it highly challenging to understand their interdependencies. Here, we highlight new systems biology tools to unravel the impact of nutritional cues and metabolic fluxes on histone modifications.
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Affiliation(s)
- Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.,Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
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