1
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Abecunas C, Kidd AD, Jiang Y, Zong H, Fallahi-Sichani M. Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.28.569098. [PMID: 38076921 PMCID: PMC10705426 DOI: 10.1101/2023.11.28.569098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges in identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tumor lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings via analysis of data from patient-derived tumors and pharmacological screens, and by performing new genetic and pharmacological experiments. Our analysis uncovers new synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating the mechanisms underlying these relationships can inform the development of more precise and context-specific, metabolism-targeted cancer therapies.
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Affiliation(s)
- Cara Abecunas
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109
- Present address: Novartis Institutes for BioMedical Research, Cambridge, MA 02139
| | - Audrey D. Kidd
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908
| | - Ying Jiang
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA 22908
| | - Hui Zong
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA 22908
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22908
| | - Mohammad Fallahi-Sichani
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22908
- Lead contact
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2
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Mathis D, du Toit T, Altinkilic EM, Stojkov D, Urzì C, Voegel CD, Wu V, Zamboni N, Simon HU, Nuoffer JM, Flück CE, Felser A. Mitochondrial dysfunction results in enhanced adrenal androgen production in H295R cells. J Steroid Biochem Mol Biol 2024; 243:106561. [PMID: 38866189 DOI: 10.1016/j.jsbmb.2024.106561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/20/2024] [Accepted: 06/06/2024] [Indexed: 06/14/2024]
Abstract
The role of mitochondria in steroidogenesis is well established. However, the specific effects of mitochondrial dysfunction on androgen synthesis are not fully understood. In this study, we investigate the effects of various mitochondrial and metabolic inhibitors in H295R adrenal cells and perform a comprehensive analysis of steroid and metabolite profiling. We report that mitochondrial complex I inhibition by rotenone shifts cells toward anaerobic metabolism with a concomitant hyperandrogenic phenotype characterized by rapid stimulation of dehydroepiandrosterone (DHEA, 2 h) and slower accumulation of androstenedione and testosterone (24 h). Screening of metabolic inhibitors confirmed DHEA stimulation, which included mitochondrial complex III and mitochondrial pyruvate carrier inhibition. Metabolomic studies revealed truncated tricarboxylic acid cycle with an inverse correlation between citric acid and DHEA production as a common metabolic marker of hyperandrogenic inhibitors. The current study sheds light on a direct interplay between energy metabolism and androgen biosynthesis that could be further explored to identify novel molecular targets for efficient treatment of androgen excess disorders.
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Affiliation(s)
- Déborah Mathis
- University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Therina du Toit
- Department for BioMedical Research, Bern University Hospital, University of Bern, Switzerland; Department of Nephrology and Hypertension, Bern University Hospital, University of Bern, Switzerland
| | - Emre Murat Altinkilic
- Division of Pediatric Endocrinology, Diabetology and Metabolism, Department of Pediatrics, Bern University Hospital, University of Bern, Switzerland; Department for BioMedical Research, Bern University Hospital, University of Bern, Switzerland
| | - Darko Stojkov
- Institute of Pharmacology, University of Bern, Switzerland
| | - Christian Urzì
- University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Switzerland; Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Clarissa D Voegel
- Department of Nephrology and Hypertension, Bern University Hospital, University of Bern, Switzerland
| | - Vincen Wu
- Institute of Molecular Systems Biology, ETH Zurich, Switzerland
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Switzerland; PHRT Swiss Multi Omics Center, Zurich, Switzerland
| | - Hans-Uwe Simon
- Institute of Pharmacology, University of Bern, Switzerland; Institute of Biochemistry, Brandenburg Medical School, Neuruppin, Germany
| | - Jean-Marc Nuoffer
- Division of Pediatric Endocrinology, Diabetology and Metabolism, Department of Pediatrics, Bern University Hospital, University of Bern, Switzerland; Department for BioMedical Research, Bern University Hospital, University of Bern, Switzerland; University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Christa E Flück
- Division of Pediatric Endocrinology, Diabetology and Metabolism, Department of Pediatrics, Bern University Hospital, University of Bern, Switzerland; Department for BioMedical Research, Bern University Hospital, University of Bern, Switzerland
| | - Andrea Felser
- Division of Pediatric Endocrinology, Diabetology and Metabolism, Department of Pediatrics, Bern University Hospital, University of Bern, Switzerland; Department for BioMedical Research, Bern University Hospital, University of Bern, Switzerland.
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3
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Cherkaoui S, Yang L, McBride M, Turn CS, Lu W, Eigenmann C, Allen GE, Panasenko OO, Zhang L, Vu A, Liu K, Li Y, Gandhi OH, Surrey L, Wierer M, White E, Rabinowitz JD, Hogarty MD, Morscher RJ. Reprogramming neuroblastoma by diet-enhanced polyamine depletion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.07.573662. [PMID: 38260457 PMCID: PMC10802427 DOI: 10.1101/2024.01.07.573662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Neuroblastoma is a highly lethal childhood tumor derived from differentiation-arrested neural crest cells1,2. Like all cancers, its growth is fueled by metabolites obtained from either circulation or local biosynthesis3,4. Neuroblastomas depend on local polyamine biosynthesis, with the inhibitor difluoromethylornithine showing clinical activity5. Here we show that such inhibition can be augmented by dietary restriction of upstream amino acid substrates, leading to disruption of oncogenic protein translation, tumor differentiation, and profound survival gains in the TH-MYCN mouse model. Specifically, an arginine/proline-free diet decreases the polyamine precursor ornithine and augments tumor polyamine depletion by difluoromethylornithine. This polyamine depletion causes ribosome stalling, unexpectedly specifically at adenosine-ending codons. Such codons are selectively enriched in cell cycle genes and low in neuronal differentiation genes. Thus, impaired translation of these codons, induced by the diet-drug combination, favors a pro-differentiation proteome. These results suggest that the genes of specific cellular programs have evolved hallmark codon usage preferences that enable coherent translational rewiring in response to metabolic stresses, and that this process can be targeted to activate differentiation of pediatric cancers.
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Affiliation(s)
- Sarah Cherkaoui
- Pediatric Cancer Metabolism Laboratory, Children’s Research Center, University of Zurich, 8032 Zurich, Switzerland
- Division of Oncology, University Children’s Hospital Zurich and Children’s Research Center, University of Zurich, 8032 Zurich, Switzerland
| | - Lifeng Yang
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton University, Princeton, NJ 08544, USA
| | - Matthew McBride
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton University, Princeton, NJ 08544, USA
| | - Christina S. Turn
- Division of Oncology and Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wenyun Lu
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton University, Princeton, NJ 08544, USA
| | - Caroline Eigenmann
- Pediatric Cancer Metabolism Laboratory, Children’s Research Center, University of Zurich, 8032 Zurich, Switzerland
- Division of Oncology, University Children’s Hospital Zurich and Children’s Research Center, University of Zurich, 8032 Zurich, Switzerland
| | - George E. Allen
- Bioinformatics Support Platform, Faculty of Medicine, University of Geneva 1211, Switzerland
- Department of Microbiology and Molecular Medicine, Institute of Genetics and Genomics Geneva, Faculty of Medicine, University of Geneva, 1211 Geneva, Switzerland
| | - Olesya O. Panasenko
- Department of Microbiology and Molecular Medicine, Institute of Genetics and Genomics Geneva, Faculty of Medicine, University of Geneva, 1211 Geneva, Switzerland
- BioCode: RNA to proteins (R2P) Platform, University of Geneva, 1211 Geneva, Switzerland
| | - Lu Zhang
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton University, Princeton, NJ 08544, USA
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ 08901, USA
- Department of Molecular Biology and Biochemistry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Annette Vu
- Division of Oncology and Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kangning Liu
- Division of Oncology and Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Yimei Li
- Division of Oncology and Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Om H. Gandhi
- Division of Oncology and Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Lea Surrey
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Michael Wierer
- Proteomics Research Infrastructure, Panum Institute, Blegdamsvej 3B, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Eileen White
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton University, Princeton, NJ 08544, USA
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ 08901, USA
- Department of Molecular Biology and Biochemistry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Joshua D. Rabinowitz
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton University, Princeton, NJ 08544, USA
| | - Michael D. Hogarty
- Division of Oncology and Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raphael J. Morscher
- Pediatric Cancer Metabolism Laboratory, Children’s Research Center, University of Zurich, 8032 Zurich, Switzerland
- Division of Oncology, University Children’s Hospital Zurich and Children’s Research Center, University of Zurich, 8032 Zurich, Switzerland
- Division of Human Genetics, Medical University Innsbruck, Peter-Mayr-Str. 1, 6020 Innsbruck, Austria
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4
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Guo T, Zhao S, Zhu W, Zhou H, Cheng H. Research progress on the biological basis of Traditional Chinese Medicine syndromes of gastrointestinal cancers. Heliyon 2023; 9:e20653. [PMID: 38027682 PMCID: PMC10643116 DOI: 10.1016/j.heliyon.2023.e20653] [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: 04/30/2023] [Revised: 09/23/2023] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
Gastrointestinal cancers account for 11.6 % of all cancers, and are the second most frequently diagnosed type of cancer worldwide. Traditional Chinese medicine (TCM), together with Western medicine or alone, has unique advantages for the prevention and treatment of cancers, including gastrointestinal cancers. Syndrome differentiation and treatment are basic characteristics of the theoretical system of TCM. TCM syndromes are the result of the differentiation of the syndrome and the basis of treatment. Genomics, transcriptomics, proteomics, metabolomics, intestinal microbiota, and serology, generated around the central law, are used to study the biological basis of TCM syndromes in gastrointestinal cancers. This review summarizes current research on the biological basis of TCM syndrome in gastrointestinal cancers and provides useful references for future research on TCM syndrome in gastrointestinal cancers.
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Affiliation(s)
- Tianhao Guo
- Institute of Health and Regimen, Jiangsu Open University, Nanjing, Jiangsu 210036, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing, Jiangsu 210023, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
| | - Shuoqi Zhao
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing, Jiangsu 210023, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
| | - Wenjian Zhu
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing, Jiangsu 210023, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
| | - Hongguang Zhou
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing, Jiangsu 210023, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
- Departments of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210029, China
| | - Haibo Cheng
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing, Jiangsu 210023, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
- Departments of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210029, China
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5
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Alberghina L. The Warburg Effect Explained: Integration of Enhanced Glycolysis with Heterogeneous Mitochondria to Promote Cancer Cell Proliferation. Int J Mol Sci 2023; 24:15787. [PMID: 37958775 PMCID: PMC10648413 DOI: 10.3390/ijms242115787] [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: 09/29/2023] [Revised: 10/19/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
The Warburg effect is the long-standing riddle of cancer biology. How does aerobic glycolysis, inefficient in producing ATP, confer a growth advantage to cancer cells? A new evaluation of a large set of literature findings covering the Warburg effect and its yeast counterpart, the Crabtree effect, led to an innovative working hypothesis presented here. It holds that enhanced glycolysis partially inactivates oxidative phosphorylation to induce functional rewiring of a set of TCA cycle enzymes to generate new non-canonical metabolic pathways that sustain faster growth rates. The hypothesis has been structured by constructing two metabolic maps, one for cancer metabolism and the other for the yeast Crabtree effect. New lines of investigation, suggested by these maps, are discussed as instrumental in leading toward a better understanding of cancer biology in order to allow the development of more efficient metabolism-targeted anticancer drugs.
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Affiliation(s)
- Lilia Alberghina
- Centre of Systems Biology, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy
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6
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Naake T, Rainer J, Huber W. MsQuality: an interoperable open-source package for the calculation of standardized quality metrics of mass spectrometry data. Bioinformatics 2023; 39:btad618. [PMID: 37812234 PMCID: PMC10580266 DOI: 10.1093/bioinformatics/btad618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 09/08/2023] [Accepted: 10/06/2023] [Indexed: 10/10/2023] Open
Abstract
MOTIVATION Multiple factors can impact accuracy and reproducibility of mass spectrometry data. There is a need to integrate quality assessment and control into data analytic workflows. RESULTS The MsQuality package calculates 43 low-level quality metrics based on the controlled mzQC vocabulary defined by the HUPO-PSI on a single mass spectrometry-based measurement of a sample. It helps to identify low-quality measurements and track data quality. Its use of community-standard quality metrics facilitates comparability of quality assessment and control (QA/QC) criteria across datasets. AVAILABILITY AND IMPLEMENTATION The R package MsQuality is available through Bioconductor at https://bioconductor.org/packages/MsQuality.
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Affiliation(s)
- Thomas Naake
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Johannes Rainer
- Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, Bolzano 39100, Italy
| | - Wolfgang Huber
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
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7
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Martino F, Lupi M, Giraudo E, Lanzetti L. Breast cancers as ecosystems: a metabolic perspective. Cell Mol Life Sci 2023; 80:244. [PMID: 37561190 PMCID: PMC10415483 DOI: 10.1007/s00018-023-04902-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/18/2023] [Accepted: 07/28/2023] [Indexed: 08/11/2023]
Abstract
Breast cancer (BC) is the most frequently diagnosed cancer and one of the major causes of cancer death. Despite enormous progress in its management, both from the therapeutic and early diagnosis viewpoints, still around 700,000 patients succumb to the disease each year, worldwide. Late recurrency is the major problem in BC, with many patients developing distant metastases several years after the successful eradication of the primary tumor. This is linked to the phenomenon of metastatic dormancy, a still mysterious trait of the natural history of BC, and of several other types of cancer, by which metastatic cells remain dormant for long periods of time before becoming reactivated to initiate the clinical metastatic disease. In recent years, it has become clear that cancers are best understood if studied as ecosystems in which the impact of non-cancer-cell-autonomous events-dependent on complex interaction between the cancer and its environment, both local and systemic-plays a paramount role, probably as significant as the cell-autonomous alterations occurring in the cancer cell. In adopting this perspective, a metabolic vision of the cancer ecosystem is bound to improve our understanding of the natural history of cancer, across space and time. In BC, many metabolic pathways are coopted into the cancer ecosystem, to serve the anabolic and energy demands of the cancer. Their study is shedding new light on the most critical aspect of BC management, of metastatic dissemination, and that of the related phenomenon of dormancy and fostering the application of the knowledge to the development of metabolic therapies.
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Affiliation(s)
- Flavia Martino
- Department of Oncology, University of Torino Medical School, Turin, Italy
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy
| | - Mariadomenica Lupi
- Department of Oncology, University of Torino Medical School, Turin, Italy
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy
| | - Enrico Giraudo
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy
- Department of Science and Drug Technology, University of Torino, Turin, Italy
| | - Letizia Lanzetti
- Department of Oncology, University of Torino Medical School, Turin, Italy.
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy.
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8
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Dmitrenko A, Reid M, Zamboni N. Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data. Bioinformatics 2023; 39:7056639. [PMID: 36825815 PMCID: PMC9978579 DOI: 10.1093/bioinformatics/btad096] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 12/13/2022] [Indexed: 02/25/2023] Open
Abstract
MOTIVATION Untargeted metabolomics by mass spectrometry is the method of choice for unbiased analysis of molecules in complex samples of biological, clinical or environmental relevance. The exceptional versatility and sensitivity of modern high-resolution instruments allows profiling of thousands of known and unknown molecules in parallel. Inter-batch differences constitute a common and unresolved problem in untargeted metabolomics, and hinder the analysis of multi-batch studies or the intercomparison of experiments. RESULTS We present a new method, Regularized Adversarial Learning Preserving Similarity (RALPS), for the normalization of multi-batch untargeted metabolomics data. RALPS builds on deep adversarial learning with a three-term loss function that mitigates batch effects while preserving biological identity, spectral properties and coefficients of variation. Using two large metabolomics datasets, we showcase the superior performance of RALPS as compared with six state-of-the-art methods for batch correction. Further, we demonstrate that RALPS scales well, is robust, deals with missing values and can handle different experimental designs. AVAILABILITY AND IMPLEMENTATION https://github.com/zamboni-lab/RALPS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrei Dmitrenko
- ETH Zürich, Institute of Molecular Systems Biology, Zürich 8093, Switzerland.,Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
| | - Michelle Reid
- ETH Zürich, Institute of Molecular Systems Biology, Zürich 8093, Switzerland
| | - Nicola Zamboni
- ETH Zürich, Institute of Molecular Systems Biology, Zürich 8093, Switzerland.,PHRT Swiss Multi-OMICS Center, Switzerland
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9
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Ren R, Horton JR, Chen Q, Yang J, Liu B, Huang Y, Blumenthal RM, Zhang X, Cheng X. Structural basis for transcription factor ZBTB7A recognition of DNA and effects of ZBTB7A somatic mutations that occur in human acute myeloid leukemia. J Biol Chem 2023; 299:102885. [PMID: 36626981 PMCID: PMC9932118 DOI: 10.1016/j.jbc.2023.102885] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/27/2022] [Accepted: 12/29/2022] [Indexed: 01/09/2023] Open
Abstract
ZBTB7A belongs to a small family of transcription factors having three members in humans (7A, 7B, and 7C). They share a BTB/POZ protein interaction domain at the amino end and a zinc-finger DNA-binding domain at the carboxyl end. They control the transcription of a wide range of genes, having varied functions in hematopoiesis, oncogenesis, and metabolism (in particular glycolysis). ZBTB7A-binding profiles at gene promoters contain a consensus G(a/c)CCC motif, followed by a CCCC sequence in some instances. Structural and mutational investigations suggest that DNA-specific contacts with the four-finger tandem array of ZBTB7A are formed sequentially, initiated from ZF1-ZF2 binding to G(a/c)CCC before spreading to ZF3-ZF4, which bind the DNA backbone and the 3' CCCC sequence, respectively. Here, we studied some mutations found in t(8;21)-positive acute myeloid leukemia patients that occur within the ZBTB7A DNA-binding domain. We determined that these mutations generally impair ZBTB7A DNA binding, with the most severe disruptions resulting from mutations in ZF1 and ZF2, and the least from a frameshift mutation in ZF3 that results in partial mislocalization. Information provided here on ZBTB7A-DNA interactions is likely applicable to ZBTB7B/C, which have overlapping functions with ZBTB7A in controlling primary metabolism.
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Affiliation(s)
- Ren Ren
- Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - John R Horton
- Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Qin Chen
- Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jie Yang
- Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Bin Liu
- Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Yun Huang
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, College of Medicine, Texas A&M University, Houston, Texas, USA
| | - Robert M Blumenthal
- Department of Medical Microbiology and Immunology, and Program in Bioinformatics, The University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, USA
| | - Xing Zhang
- Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
| | - Xiaodong Cheng
- Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
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10
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Ng PQ, Saint-Geniez M, Kim LA, Shu DY. Divergent Metabolomic Signatures of TGFβ2 and TNFα in the Induction of Retinal Epithelial-Mesenchymal Transition. Metabolites 2023; 13:213. [PMID: 36837832 PMCID: PMC9966219 DOI: 10.3390/metabo13020213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 01/23/2023] [Accepted: 01/23/2023] [Indexed: 02/04/2023] Open
Abstract
Epithelial-mesenchymal transition (EMT) is a dedifferentiation program in which polarized, differentiated epithelial cells lose their cell-cell adhesions and transform into matrix-producing mesenchymal cells. EMT of retinal pigment epithelial (RPE) cells plays a crucial role in many retinal diseases, including age-related macular degeneration, proliferative vitreoretinopathy, and diabetic retinopathy. This dynamic process requires complex metabolic reprogramming to accommodate the demands of this dramatic cellular transformation. Both transforming growth factor-beta 2 (TGFβ2) and tumor necrosis factor-alpha (TNFα) have the capacity to induce EMT in RPE cells; however, little is known about their impact on the RPE metabolome. Untargeted metabolomics using high-resolution mass spectrometry was performed to reveal the metabolomic signatures of cellular and secreted metabolites of primary human fetal RPE cells treated with either TGFβ2 or TNFα for 5 days. A total of 638 metabolites were detected in both samples; 188 were annotated as primary metabolites. Metabolomics profiling showed distinct metabolomic signatures associated with TGFβ2 and TNFα treatment. Enrichment pathway network analysis revealed alterations in the pentose phosphate pathway, galactose metabolism, nucleotide and pyrimidine metabolism, purine metabolism, and arginine and proline metabolism in TNFα-treated cells compared to untreated control cells, whereas TGFβ2 treatment induced perturbations in fatty acid biosynthesis metabolism, the linoleic acid pathway, and the Notch signaling pathway. These results provide a broad metabolic understanding of the bioenergetic rewiring processes governing TGFβ2- and TNFα-dependent induction of EMT. Elucidating the contributions of TGFβ2 and TNFα and their mechanistic differences in promoting EMT of RPE will enable the identification of novel biomarkers for diagnosis, management, and tailored drug development for retinal fibrotic diseases.
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Affiliation(s)
- Pei Qin Ng
- Department of Plant Science, University of Cambridge, Downing Street, Cambridge CB2 3EA, Cambridgeshire, UK
- Schepens Eye Research Institute of Mass Eye and Ear, Boston, MA 02114, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
- School of Biological Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia
| | - Magali Saint-Geniez
- Schepens Eye Research Institute of Mass Eye and Ear, Boston, MA 02114, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Leo A. Kim
- Schepens Eye Research Institute of Mass Eye and Ear, Boston, MA 02114, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Daisy Y. Shu
- Schepens Eye Research Institute of Mass Eye and Ear, Boston, MA 02114, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
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Shorthouse D, Bradley J, Critchlow SE, Bendtsen C, Hall BA. Heterogeneity of the cancer cell line metabolic landscape. Mol Syst Biol 2022; 18:e11006. [PMID: 36321551 PMCID: PMC9627668 DOI: 10.15252/msb.202211006] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 08/30/2022] [Accepted: 10/07/2022] [Indexed: 11/30/2022] Open
Abstract
The unravelling of the complexity of cellular metabolism is in its infancy. Cancer-associated genetic alterations may result in changes to cellular metabolism that aid in understanding phenotypic changes, reveal detectable metabolic signatures, or elucidate vulnerabilities to particular drugs. To understand cancer-associated metabolic transformation, we performed untargeted metabolite analysis of 173 different cancer cell lines from 11 different tissues under constant conditions for 1,099 different species using mass spectrometry (MS). We correlate known cancer-associated mutations and gene expression programs with metabolic signatures, generating novel associations of known metabolic pathways with known cancer drivers. We show that metabolic activity correlates with drug sensitivity and use metabolic activity to predict drug response and synergy. Finally, we study the metabolic heterogeneity of cancer mutations across tissues, and find that genes exhibit a range of context specific, and more general metabolic control.
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Affiliation(s)
- David Shorthouse
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | | | | | | | - Benjamin A Hall
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
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