1
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Tavakoli N, Fong EJ, Coleman A, Huang YK, Bigger M, Doche ME, Kim S, Lenz HJ, Graham NA, Macklin P, Finley SD, Mumenthaler SM. Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.595756. [PMID: 38826317 PMCID: PMC11142224 DOI: 10.1101/2024.05.24.595756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. This approach reveals the network-wide effects of metabolic perturbations. Our results highlighted hexokinase (HK) as the crucial target, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF-conditioned media exhibited increased sensitivity to HK inhibition, confirming the model predictions. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk.
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Affiliation(s)
- Niki Tavakoli
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Emma J. Fong
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Abigail Coleman
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Yu-Kai Huang
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Mathias Bigger
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | | | - Seungil Kim
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Heinz-Josef Lenz
- Division of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
| | - Nicholas A. Graham
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 46202, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Shannon M. Mumenthaler
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
- Division of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
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2
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Mardinoglu A, Palsson BØ. Genome-scale models in human metabologenomics. Nat Rev Genet 2024:10.1038/s41576-024-00768-0. [PMID: 39300314 DOI: 10.1038/s41576-024-00768-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/29/2024] [Indexed: 09/22/2024]
Abstract
Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.
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Affiliation(s)
- Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK.
| | - Bernhard Ø Palsson
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
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3
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Hasibi R, Michoel T, Oyarzún DA. Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality. NPJ Syst Biol Appl 2024; 10:24. [PMID: 38448436 PMCID: PMC10917767 DOI: 10.1038/s41540-024-00348-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.
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Affiliation(s)
- Ramin Hasibi
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Diego A Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
- School of Informatics, University of Edinburgh, Edinburgh, UK.
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4
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Kaste JA, Shachar-Hill Y. Model validation and selection in metabolic flux analysis and flux balance analysis. Biotechnol Prog 2024; 40:e3413. [PMID: 37997613 PMCID: PMC10922127 DOI: 10.1002/btpr.3413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 11/25/2023]
Abstract
13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.
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Affiliation(s)
- Joshua A.M. Kaste
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Rd, East Lansing, MI 48823
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824
| | - Yair Shachar-Hill
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824
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5
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Ran M, Zhou Y, Guo Y, Huang D, Zhang SL, Tam KY. Cytosolic malic enzyme and glucose-6-phosphate dehydrogenase modulate redox balance in NSCLC with acquired drug resistance. FEBS J 2023; 290:4792-4809. [PMID: 37410361 DOI: 10.1111/febs.16897] [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: 12/27/2022] [Revised: 06/02/2023] [Accepted: 07/04/2023] [Indexed: 07/07/2023]
Abstract
Lung cancer cells often show elevated levels of reactive oxygen species (ROS) and nicotinamide adenine dinucleotide phosphate (NADPH). However, the connections between deregulated redox homeostasis in different subtypes of lung cancer and acquired drug resistance in lung cancer have not yet been fully established. Herein, we analyzed different subtypes of lung cancer data reported in the Cancer Cell Line Encyclopedia (CCLE) database, the Cancer Genome Atlas program (TCGA), and the sequencing data obtained from a gefitinib-resistant non-small-cell lung cancer (NSCLC) cell line (H1975GR). Using flux balance analysis (FBA) model integrated with multiomics data and gene expression profiles, we identified cytosolic malic enzyme 1 (ME1) and glucose-6-phosphate dehydrogenase as the major contributors to the significantly upregulated NADPH flux in NSCLC tissues as compared with normal lung tissues, and gefitinib-resistant NSCLC cell line as compared with the parental cell line. Silencing the gene expression of either of these two enzymes in two osimertinib-resistant NSCLC cell lines (H1975OR and HCC827OR) exhibited strong antiproliferative effects. Our findings not only underscored the pivotal roles of cytosolic ME1 and glucose-6-phosphate dehydrogenase in regulating redox states in NSCLC cells but also provided novel insights into their potential roles in drug-resistant NSCLC cells with disturbed redox states.
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Affiliation(s)
- Maoxin Ran
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, China
| | - Yan Zhou
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, China
| | - Yizhen Guo
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, China
| | - Ding Huang
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, China
| | - Shao-Lin Zhang
- School of Pharmaceutical Sciences, Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Chongqing University, China
| | - Kin Yip Tam
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, China
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6
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Pillai M, Hojel E, Jolly MK, Goyal Y. Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools. NATURE COMPUTATIONAL SCIENCE 2023; 3:301-313. [PMID: 38177938 DOI: 10.1038/s43588-023-00427-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 03/03/2023] [Indexed: 01/06/2024]
Abstract
Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.
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Affiliation(s)
- Maalavika Pillai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Emilia Hojel
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India.
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA.
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7
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Kaste JAM, Shachar-Hill Y. Model Validation and Selection in Metabolic Flux Analysis and Flux Balance Analysis. ARXIV 2023:arXiv:2303.12651v1. [PMID: 36994165 PMCID: PMC10055486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both of these methods use metabolic reaction network models of metabolism operating at steady state, so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. A number of approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to decide on and/or discriminate between alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2-test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how the adoption of robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology in particular.
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Affiliation(s)
- Joshua A M Kaste
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Rd, East Lansing, MI 48823
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824
| | - Yair Shachar-Hill
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824
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8
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Gonçalves IG, García-Aznar JM. Hybrid computational models of multicellular tumour growth considering glucose metabolism. Comput Struct Biotechnol J 2023; 21:1262-1271. [PMID: 36814723 PMCID: PMC9939553 DOI: 10.1016/j.csbj.2023.01.044] [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: 12/01/2022] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023] Open
Abstract
Cancer cells metabolize glucose through metabolic pathways that differ from those used by healthy and differentiated cells. In particular, tumours have been shown to consume more glucose than their healthy counterparts and to use anaerobic metabolic pathways, even under aerobic conditions. Nevertheless, scientists have still not been able to explain why cancer cells evolved to present an altered metabolism and what evolutionary advantage this might provide them. Experimental and computational models have been increasingly used in recent years to understand some of these biological questions. Multicellular tumour spheroids are effective experimental models as they replicate the initial stages of avascular solid tumour growth. Furthermore, these experiments generate data which can be used to calibrate and validate computational studies that aim to simulate tumour growth. Hybrid models are of particular relevance in this field of research because they model cells as individual agents while also incorporating continuum representations of the substances present in the surrounding microenvironment that may participate in intracellular metabolic networks as concentration or density distributions. Henceforth, in this review, we explore the potential of computational modelling to reveal the role of metabolic reprogramming in tumour growth.
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Key Words
- ABM, agent-based model
- ATP, adenosine triphosphate
- CA, cellular automata
- CPM, cellular Potts model
- ECM, extracellular matrix
- FBA, Flux Balance Analysis
- FDG-PET, [18F]-fluorodeoxyglucose-positron emission tomography
- MCTS, multicellular tumour spheroids
- ODEs, ordinary differential equations
- PDEs, partial differential equations
- SBML, Systems Biology Markup Language
- Warburg effect
- agent-based models
- glucose metabolism
- hybrid modelling
- multicellular simulations
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Affiliation(s)
- Inês G. Gonçalves
- Multiscale in Mechanical and Biological Engineering, Department of Mechanical Engineering, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza 50018, Aragon, Spain
| | - José Manuel García-Aznar
- Multiscale in Mechanical and Biological Engineering, Department of Mechanical Engineering, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza 50018, Aragon, Spain
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9
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Cheng CT, Lai JM, Chang PMH, Hong YR, Huang CYF, Wang FS. Identifying essential genes in genome-scale metabolic models of consensus molecular subtypes of colorectal cancer. PLoS One 2023; 18:e0286032. [PMID: 37205704 DOI: 10.1371/journal.pone.0286032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/06/2023] [Indexed: 05/21/2023] Open
Abstract
Identifying essential targets in the genome-scale metabolic networks of cancer cells is a time-consuming process. The present study proposed a fuzzy hierarchical optimization framework for identifying essential genes, metabolites and reactions. On the basis of four objectives, the present study developed a framework for identifying essential targets that lead to cancer cell death and evaluating metabolic flux perturbations in normal cells that have been caused by cancer treatment. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. We applied nested hybrid differential evolution to solve the trilevel MDM problem to identify essential targets in genome-scale metabolic models for five consensus molecular subtypes (CMSs) of colorectal cancer. We used various media to identify essential targets for each CMS and discovered that most targets affected all five CMSs and that some genes were CMS-specific. We obtained experimental data on the lethality of cancer cell lines from the DepMap database to validate the identified essential genes. The results reveal that most of the identified essential genes were compatible with the colorectal cancer cell lines obtained from DepMap and that these genes, with the exception of EBP, LSS, and SLC7A6, could generate a high level of cell death when knocked out. The identified essential genes were mostly involved in cholesterol biosynthesis, nucleotide metabolisms, and the glycerophospholipid biosynthetic pathway. The genes involved in the cholesterol biosynthetic pathway were also revealed to be determinable, if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in the cholesterol biosynthetic pathway became non-essential if such a reaction was induced. Furthermore, the essential gene CRLS1 was revealed as a medium-independent target for all CMSs.
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Affiliation(s)
- Chao-Ting Cheng
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Jin-Mei Lai
- Department of Life Science, College of Science and Engineering, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Peter Mu-Hsin Chang
- Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Ren Hong
- Department of Biochemistry and Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung City, Taiwan
| | - Chi-Ying F Huang
- Institute of Biopharmaceutical Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Biotechnology and Laboratory Science in Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
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10
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Shi T, Yu H, Blair RH. Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization. Stat Appl Genet Mol Biol 2023; 22:sagmb-2022-0054. [PMID: 37988745 DOI: 10.1515/sagmb-2022-0054] [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: 11/05/2022] [Accepted: 09/28/2023] [Indexed: 11/23/2023]
Abstract
Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.
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Affiliation(s)
- Tiange Shi
- University at Buffalo, Biostatistics, Buffalo, USA
| | - Han Yu
- Roswell Park Comprehensive Cancer Center, Biostatistics and Bioinformatics, Buffalo, USA
| | - Rachael Hageman Blair
- University at Buffalo, Biostatistics, Institute for Artificial Intelligence and Data Science, Buffalo, USA
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11
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Ramos JRC, Oliveira GP, Dumas P, Oliveira R. Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis. Bioprocess Biosyst Eng 2022; 45:1889-1904. [DOI: 10.1007/s00449-022-02795-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/30/2022] [Indexed: 11/28/2022]
Abstract
AbstractFlux balance analysis (FBA) is currently the standard method to compute metabolic fluxes in genome-scale networks. Several FBA extensions employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric FBA extension that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric) in the same linear program. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The mechanistic constraints were deduced from a reduced CHO-K1 genome-scale network with 686 metabolites, 788 reactions and 210 degrees of freedom. The non-parametric constraints were obtained by principal component analysis of the flux dataset. The two types of constraints were integrated in the same linear program showing comparable computational cost to standard FBA. The hybrid FBA is shown to significantly improve the specific growth rate prediction under different constraints scenarios. A metabolically efficient cell growth feed targeting minimal byproducts accumulation was designed by hybrid FBA. It is concluded that integrating parametric and nonparametric constraints in the same linear program may be an efficient approach to reduce the solution space and to improve the predictive power of FBA methods when critical mechanistic information is missing.
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12
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Lapin A, Perfahl H, Jain HV, Reuss M. Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth. Sci Rep 2022; 12:12373. [PMID: 35858953 PMCID: PMC9300625 DOI: 10.1038/s41598-022-15767-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/29/2022] [Indexed: 11/09/2022] Open
Abstract
We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.
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Affiliation(s)
- Alexey Lapin
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany
- Institute of Chemical Process Engineering, University Stuttgart, Stuttgart, Germany
| | - Holger Perfahl
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany
| | - Harsh Vardhan Jain
- Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, MN, USA
| | - Matthias Reuss
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany.
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13
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Ng RH, Lee JW, Baloni P, Diener C, Heath JR, Su Y. Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer. Front Oncol 2022; 12:914594. [PMID: 35875150 PMCID: PMC9303011 DOI: 10.3389/fonc.2022.914594] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.
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Affiliation(s)
- Rachel H. Ng
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Jihoon W. Lee
- Medical Scientist Training Program, University of Washington, Seattle, WA, United States
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | | | | | - James R. Heath
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Yapeng Su
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- Herbold Computational Biology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
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14
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Domenzain I, Sánchez B, Anton M, Kerkhoven EJ, Millán-Oropeza A, Henry C, Siewers V, Morrissey JP, Sonnenschein N, Nielsen J. Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. Nat Commun 2022; 13:3766. [PMID: 35773252 PMCID: PMC9246944 DOI: 10.1038/s41467-022-31421-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 06/16/2022] [Indexed: 01/08/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.
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Affiliation(s)
- Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - Benjamín Sánchez
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Mihail Anton
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-412 58, Gothenburg, Sweden
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - Aarón Millán-Oropeza
- Plateforme d'analyse protéomique Paris Sud-Ouest (PAPPSO), INRAE, MICALIS Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Céline Henry
- Plateforme d'analyse protéomique Paris Sud-Ouest (PAPPSO), INRAE, MICALIS Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Verena Siewers
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - John P Morrissey
- School of Microbiology, Environmental Research Institute and APC Microbiome Ireland, University College Cork, T12 K8AF, Cork, Ireland
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.
- BioInnovation Institute, Ole Maaløes Vej 3, 2200, Copenhagen, Denmark.
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15
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Thanamit K, Hoerhold F, Oswald M, Koenig R. Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis. BMC Bioinformatics 2022; 23:226. [PMID: 35689204 PMCID: PMC9188260 DOI: 10.1186/s12859-022-04742-7] [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: 05/03/2021] [Accepted: 05/23/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by 13C tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes. RESULT We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with 13C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods. CONCLUSION Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate.
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Affiliation(s)
- Kulwadee Thanamit
- Systems Biology Research Group, Institute for Infectious Diseases and Infection Control (IIMK), Jena University Hospital, Kollegiengasse 10, 07743, Jena, Germany
| | - Franziska Hoerhold
- Systems Biology Research Group, Institute for Infectious Diseases and Infection Control (IIMK), Jena University Hospital, Kollegiengasse 10, 07743, Jena, Germany
| | - Marcus Oswald
- Systems Biology Research Group, Institute for Infectious Diseases and Infection Control (IIMK), Jena University Hospital, Kollegiengasse 10, 07743, Jena, Germany
| | - Rainer Koenig
- Systems Biology Research Group, Institute for Infectious Diseases and Infection Control (IIMK), Jena University Hospital, Kollegiengasse 10, 07743, Jena, Germany.
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16
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Reustle A, Menig LS, Leuthold P, Hofmann U, Stühler V, Schmees C, Becker M, Haag M, Klumpp V, Winter S, Büttner FA, Rausch S, Hennenlotter J, Fend F, Scharpf M, Stenzl A, Bedke J, Schwab M, Schaeffeler E. Nicotinamide-N-methyltransferase is a promising metabolic drug target for primary and metastatic clear cell renal cell carcinoma. Clin Transl Med 2022; 12:e883. [PMID: 35678045 PMCID: PMC9178377 DOI: 10.1002/ctm2.883] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 04/27/2022] [Accepted: 05/03/2022] [Indexed: 12/16/2022] Open
Abstract
Background The metabolic enzyme nicotinamide‐N‐methyltransferase (NNMT) is highly expressed in various cancer entities, suggesting tumour‐promoting functions. We systematically investigated NNMT expression and its metabolic interactions in clear cell renal cell carcinoma (ccRCC), a prominent RCC subtype with metabolic alterations, to elucidate its role as a drug target. Methods NNMT expression was assessed in primary ccRCC (n = 134), non‐tumour tissue and ccRCC‐derived metastases (n = 145) by microarray analysis and/or immunohistochemistry. Findings were validated in The Cancer Genome Atlas (kidney renal clear cell carcinoma [KIRC], n = 452) and by single‐cell analysis. Expression was correlated with clinicopathological data and survival. Metabolic alterations in NNMT‐depleted cells were assessed by nontargeted/targeted metabolomics and extracellular flux analysis. The NNMT inhibitor (NNMTi) alone and in combination with the inhibitor 2‐deoxy‐D‐glucose for glycolysis and BPTES (bis‐2‐(5‐phenylacetamido‐1,3,4‐thiadiazol‐2‐yl)ethyl‐sulfide) for glutamine metabolism was investigated in RCC cell lines (786‐O, A498) and in two 2D ccRCC‐derived primary cultures and three 3D ccRCC air–liquid interface models. Results NNMT protein was overexpressed in primary ccRCC (p = 1.32 × 10–16) and ccRCC‐derived metastases (p = 3.92 × 10–20), irrespective of metastatic location, versus non‐tumour tissue. Single‐cell data showed predominant NNMT expression in ccRCC and not in the tumour microenvironment. High NNMT expression in primary ccRCC correlated with worse survival in independent cohorts (primary RCC—hazard ratio [HR] = 4.3, 95% confidence interval [CI]: 1.5–12.4; KIRC—HR = 3.3, 95% CI: 2.0–5.4). NNMT depletion leads to intracellular glutamine accumulation, with negative effects on mitochondrial function and cell survival, while not affecting glycolysis or glutathione metabolism. At the gene level, NNMT‐depleted cells upregulate glycolysis, oxidative phosphorylation and apoptosis pathways. NNMTi alone or in combination with 2‐deoxy‐D‐glucose and BPTES resulted in inhibition of cell viability in ccRCC cell lines and primary tumour and metastasis‐derived models. In two out of three patient‐derived ccRCC air–liquid interface models, NNMTi treatment induced cytotoxicity. Conclusions Since efficient glutamine utilisation, which is essential for ccRCC tumours, depends on NNMT, small‐molecule NNMT inhibitors provide a novel therapeutic strategy for ccRCC and act as sensitizers for combination therapies.
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Affiliation(s)
- Anna Reustle
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Lena-Sophie Menig
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Patrick Leuthold
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Viktoria Stühler
- Department of Urology, University Hospital Tuebingen, Tuebingen, Germany
| | - Christian Schmees
- NMI Natural and Medical Sciences Institute at the University of Tuebingen, Reutlingen, Germany
| | - Michael Becker
- Experimental Pharmacology and Oncology GmbH, Berlin-Buch, Germany
| | - Mathias Haag
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Verena Klumpp
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Stefan Winter
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Florian A Büttner
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Steffen Rausch
- Department of Urology, University Hospital Tuebingen, Tuebingen, Germany
| | - Jörg Hennenlotter
- Department of Urology, University Hospital Tuebingen, Tuebingen, Germany
| | - Falko Fend
- Institute of Pathology and Neuropathology, University Hospital Tuebingen, Tuebingen, Germany
| | - Marcus Scharpf
- Institute of Pathology and Neuropathology, University Hospital Tuebingen, Tuebingen, Germany
| | - Arnulf Stenzl
- Department of Urology, University Hospital Tuebingen, Tuebingen, Germany
| | - Jens Bedke
- Department of Urology, University Hospital Tuebingen, Tuebingen, Germany.,German Cancer Consortium (DKTK), Partner Site Tübingen, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany.,German Cancer Consortium (DKTK), Partner Site Tübingen, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Departments of Clinical Pharmacology, Pharmacy and Biochemistry, University of Tuebingen, Tuebingen, Germany.,Cluster of Excellence iFIT (EXC2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tuebingen, Tuebingen, Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany.,Cluster of Excellence iFIT (EXC2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tuebingen, Tuebingen, Germany
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17
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Quantitative metabolic fluxes regulated by trans-omic networks. Biochem J 2022; 479:787-804. [PMID: 35356967 PMCID: PMC9022981 DOI: 10.1042/bcj20210596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 12/21/2022]
Abstract
Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.
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18
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Abstract
The majority of the genes present in bacterial genomes remain poorly characterized, with up to one-third of those that are protein encoding having no definitive function. Transposon insertion sequencing represents a high-throughput technique that can help rectify this deficiency. The technology, however, can only be realistically applied to those species in which high rates of DNA transfer can be achieved. Here, we have developed a number of approaches that overcome this barrier in the autotrophic species Clostridium autoethanogenum by using a mariner-based transposon system. The inherent instability of such systems in the Escherichia coli conjugation donor due to transposition events was counteracted through the incorporation of a conditionally lethal codA marker on the plasmid backbone. Relatively low frequencies of transformation of the plasmid into C. autoethanogenum were circumvented through the use of a plasmid that is conditional for replication coupled with the routine implementation of an Illumina library preparation protocol that eliminates plasmid-based reads. A transposon library was then used to determine the essential genes needed for growth using carbon monoxide as the sole carbon and energy source. IMPORTANCE Although microbial genome sequences are relatively easily determined, assigning gene function remains a bottleneck. Consequently, relatively few genes are well characterized, leaving the function of many as either hypothetical or entirely unknown. High-throughput transposon sequencing can help remedy this deficiency, but is generally only applicable to microbes with efficient DNA transfer procedures. These exclude many microorganisms of importance to humankind either as agents of disease or as industrial process organisms. Here, we developed approaches to facilitate transposon insertion sequencing in the acetogen Clostridium autoethanogenum, a chassis being exploited to convert single-carbon waste gases CO and CO2 into chemicals and fuels at an industrial scale. This allowed the determination of gene essentiality under heterotrophic and autotrophic growth, providing insights into the utilization of CO as a sole carbon and energy source. The strategies implemented are translatable and will allow others to apply transposon insertion sequencing to other microbes where DNA transfer has until now represented a barrier to progress.
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19
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Cipriani C, Pacheco MP, Kishk A, Wachich M, Abankwa D, Schaffner-Reckinger E, Sauter T. Bruceine D Identified as a Drug Candidate against Breast Cancer by a Novel Drug Selection Pipeline and Cell Viability Assay. Pharmaceuticals (Basel) 2022; 15:179. [PMID: 35215292 PMCID: PMC8875459 DOI: 10.3390/ph15020179] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/20/2022] [Accepted: 01/26/2022] [Indexed: 02/06/2023] Open
Abstract
The multi-target effects of natural products allow us to fight complex diseases like cancer on multiple fronts. Unlike docking techniques, network-based approaches such as genome-scale metabolic modelling can capture multi-target effects. However, the incompleteness of natural product target information reduces the prediction accuracy of in silico gene knockout strategies. Here, we present a drug selection workflow based on context-specific genome-scale metabolic models, built from the expression data of cancer cells treated with natural products, to predict cell viability. The workflow comprises four steps: first, in silico single-drug and drug combination predictions; second, the assessment of the effects of natural products on cancer metabolism via the computation of a dissimilarity score between the treated and control models; third, the identification of natural products with similar effects to the approved drugs; and fourth, the identification of drugs with the predicted effects in pathways of interest, such as the androgen and estrogen pathway. Out of the initial 101 natural products, nine candidates were tested in a 2D cell viability assay. Bruceine D, emodin, and scutellarein showed a dose-dependent inhibition of MCF-7 and Hs 578T cell proliferation with IC50 values between 0.7 to 65 μM, depending on the drug and cell line. Bruceine D, extracted from Brucea javanica seeds, showed the highest potency.
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Affiliation(s)
- Claudia Cipriani
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (C.C.); (M.P.P.); (A.K.)
| | - Maria Pires Pacheco
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (C.C.); (M.P.P.); (A.K.)
| | - Ali Kishk
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (C.C.); (M.P.P.); (A.K.)
| | - Maryem Wachich
- Cancer Cell Biology and Drug Discovery Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (M.W.); (D.A.); (E.S.-R.)
| | - Daniel Abankwa
- Cancer Cell Biology and Drug Discovery Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (M.W.); (D.A.); (E.S.-R.)
| | - Elisabeth Schaffner-Reckinger
- Cancer Cell Biology and Drug Discovery Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (M.W.); (D.A.); (E.S.-R.)
| | - Thomas Sauter
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (C.C.); (M.P.P.); (A.K.)
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20
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Cheng CT, Wang TY, Chen PR, Wu WH, Lai JM, Chang PMH, Hong YR, Huang CYF, Wang FS. Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer. BIOLOGY 2021; 10:biology10111115. [PMID: 34827109 PMCID: PMC8614794 DOI: 10.3390/biology10111115] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 01/21/2023]
Abstract
Simple Summary Discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. The computational results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is, and a two-target combination of 5-FU and folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells. Abstract The efficient discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. Early prediction of side effects is key for reducing development costs, increasing drug efficacy, and increasing drug safety. This study developed a fuzzy optimization framework for Identifying AntiCancer Targets (IACT) using constraint-based models. Four objectives were established to evaluate the mortality of treated cancer cells and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Fuzzy set theory was applied to evaluate potential side effects and investigate the magnitude of metabolic deviations in perturbed cells compared with their normal counterparts. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. A nested hybrid differential evolution algorithm with a hierarchical fitness function was applied to solve multilevel IACT problems. The results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is. A clinical antimetabolite drug 5-fluorouracil (5-FU) has been used to inhibit synthesis of deoxythymidine-5′-triphosphate for treatment of colorectal cancer. The computational results reveal that a two-target combination of 5-FU and a folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.
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Affiliation(s)
- Chao-Ting Cheng
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Tsun-Yu Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Pei-Rong Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Wu-Hsiung Wu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Jin-Mei Lai
- Department of Life Science, Fu-Jen Catholic University, New Taipei City 24205, Taiwan;
| | - Peter Mu-Hsin Chang
- Department of Oncology, Taipei Veterans General Hospital, Taipei 11217, Taiwan;
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11211, Taiwan
| | - Yi-Ren Hong
- Department of Biochemistry, Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung City 80708, Taiwan;
| | - Chi-Ying F. Huang
- Institute of Biopharmaceutical Sciences, National Yang Ming Chiao Tung University, Taipei 11211, Taiwan;
- Department of Biotechnology and Laboratory Science in Medicine, National Yang Ming Chiao Tung University, Taipei 11211, Taiwan
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
- Correspondence: ; Tel.: +886-5-2720411 (ext. 33404)
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21
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Campos TL, Korhonen PK, Hofmann A, Gasser RB, Young ND. Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications. Biotechnol Adv 2021; 54:107822. [PMID: 34461202 DOI: 10.1016/j.biotechadv.2021.107822] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 08/17/2021] [Accepted: 08/24/2021] [Indexed: 12/17/2022]
Abstract
The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.
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Affiliation(s)
- Tulio L Campos
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia; Bioinformatics Core Facility, Instituto Aggeu Magalhães, Fundação Oswaldo Cruz (IAM-Fiocruz), Recife, Pernambuco, Brazil
| | - Pasi K Korhonen
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Andreas Hofmann
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.
| | - Neil D Young
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.
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22
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Herrmann HA, Rusz M, Baier D, Jakupec MA, Keppler BK, Berger W, Koellensperger G, Zanghellini J. Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer. Cancers (Basel) 2021; 13:4130. [PMID: 34439283 PMCID: PMC8391396 DOI: 10.3390/cancers13164130] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/23/2021] [Accepted: 08/03/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. METHODS Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. RESULTS Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. CONCLUSIONS Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.
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Affiliation(s)
- Helena A. Herrmann
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
| | - Mate Rusz
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
| | - Dina Baier
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
| | - Michael A. Jakupec
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
| | - Bernhard K. Keppler
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
| | - Walter Berger
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
- Institute of Cancer Research and Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
- Vienna Metabolomics Center (VIME), University of Vienna, 1090 Vienna, Austria
- Research Network Chemistry Meets Microbiology, University of Vienna, 1090 Vienna, Austria
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
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23
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Fedrizzi T, Ciani Y, Lorenzin F, Cantore T, Gasperini P, Demichelis F. Fast mutual exclusivity algorithm nominates potential synthetic lethal gene pairs through brute force matrix product computations. Comput Struct Biotechnol J 2021; 19:4394-4403. [PMID: 34429855 PMCID: PMC8369001 DOI: 10.1016/j.csbj.2021.08.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 12/12/2022] Open
Abstract
Mutual Exclusivity analysis of genomic aberrations contributes to the exploration of potential synthetic lethal (SL) relationships thus guiding the nomination of specific cancer cells vulnerabilities. When multiple classes of genomic aberrations and large cohorts of patients are interrogated, exhaustive genome-wide analyses are not computationally feasible with commonly used approaches. Here we present Fast Mutual Exclusivity (FaME), an algorithm based on matrix multiplication that employs a logarithm-based implementation of the Fisher's exact test to achieve fast computation of genome-wide mutual exclusivity tests; we show that brute force testing for mutual exclusivity of hundreds of millions of aberrations combinations can be performed in few minutes. We applied FaME to allele-specific data from whole exome experiments of 27 TCGA studies cohorts, detecting both mutual exclusivity of point mutations, as well as allele-specific copy number signals that span sets of contiguous cytobands. We next focused on a case study involving the loss of tumor suppressors and druggable genes while exploiting an integrated analysis of both public cell lines loss of function screens data and patients' transcriptomic profiles. FaME algorithm implementation as well as allele-specific analysis output are publicly available at https://github.com/demichelislab/FaME.
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Affiliation(s)
- Tarcisio Fedrizzi
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Yari Ciani
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Francesca Lorenzin
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Thomas Cantore
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Paola Gasperini
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Francesca Demichelis
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Al-Saud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10021, USA
- The Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
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24
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Aromolaran O, Aromolaran D, Isewon I, Oyelade J. Machine learning approach to gene essentiality prediction: a review. Brief Bioinform 2021; 22:6219158. [PMID: 33842944 DOI: 10.1093/bib/bbab128] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/04/2021] [Accepted: 03/17/2021] [Indexed: 12/17/2022] Open
Abstract
Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions. SHORT ABSTRACT Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.
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Affiliation(s)
- Olufemi Aromolaran
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Damilare Aromolaran
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Itunuoluwa Isewon
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Jelili Oyelade
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
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25
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Towards the routine use of in silico screenings for drug discovery using metabolic modelling. Biochem Soc Trans 2021; 48:955-969. [PMID: 32369553 PMCID: PMC7329353 DOI: 10.1042/bst20190867] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 04/01/2020] [Accepted: 04/06/2020] [Indexed: 12/12/2022]
Abstract
Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.
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26
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Dusad V, Thiel D, Barahona M, Keun HC, Oyarzún DA. Opportunities at the Interface of Network Science and Metabolic Modeling. Front Bioeng Biotechnol 2021; 8:591049. [PMID: 33569373 PMCID: PMC7868444 DOI: 10.3389/fbioe.2020.591049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 12/22/2020] [Indexed: 12/17/2022] Open
Abstract
Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.
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Affiliation(s)
- Varshit Dusad
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Denise Thiel
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Hector C. Keun
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Diego A. Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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27
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Dugourd A, Kuppe C, Sciacovelli M, Gjerga E, Gabor A, Emdal KB, Vieira V, Bekker‐Jensen DB, Kranz J, Bindels E, Costa AS, Sousa A, Beltrao P, Rocha M, Olsen JV, Frezza C, Kramann R, Saez‐Rodriguez J. Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses. Mol Syst Biol 2021; 17:e9730. [PMID: 33502086 PMCID: PMC7838823 DOI: 10.15252/msb.20209730] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 12/18/2020] [Accepted: 12/21/2020] [Indexed: 01/07/2023] Open
Abstract
Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.
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Affiliation(s)
- Aurelien Dugourd
- Faculty of Medicine, and Heidelberg University HospitalInstitute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Faculty of MedicineJoint Research Centre for Computational Biomedicine (JRC‐COMBINE)RWTH Aachen UniversityAachenGermany
- Faculty of MedicineInstitute of Experimental Medicine and Systems BiologyRWTH Aachen UniversityAachenGermany
- Division of Nephrology and Clinical ImmunologyFaculty of MedicineRWTH Aachen UniversityAachenGermany
| | - Christoph Kuppe
- Faculty of MedicineInstitute of Experimental Medicine and Systems BiologyRWTH Aachen UniversityAachenGermany
- Division of Nephrology and Clinical ImmunologyFaculty of MedicineRWTH Aachen UniversityAachenGermany
- Department of Internal Medicine, Nephrology and TransplantationErasmus Medical CenterRotterdamThe Netherlands
| | - Marco Sciacovelli
- MRC Cancer UnitHutchison/MRC Research CentreUniversity of CambridgeCambridgeUK
| | - Enio Gjerga
- Faculty of Medicine, and Heidelberg University HospitalInstitute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Faculty of MedicineJoint Research Centre for Computational Biomedicine (JRC‐COMBINE)RWTH Aachen UniversityAachenGermany
| | - Attila Gabor
- Faculty of Medicine, and Heidelberg University HospitalInstitute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Kristina B. Emdal
- Faculty of Health and Medical SciencesProteomics ProgramNovo Nordisk Foundation Center for Protein ResearchUniversity of CopenhagenCopenhagenDenmark
| | - Vitor Vieira
- Centre of Biological EngineeringUniversity of Minho ‐ Campus de GualtarBragaPortugal
| | - Dorte B. Bekker‐Jensen
- Faculty of Health and Medical SciencesProteomics ProgramNovo Nordisk Foundation Center for Protein ResearchUniversity of CopenhagenCopenhagenDenmark
| | - Jennifer Kranz
- Faculty of MedicineInstitute of Experimental Medicine and Systems BiologyRWTH Aachen UniversityAachenGermany
- Department of Urology and Pediatric UrologySt. Antonius Hospital EschweilerAcademic Teaching Hospital of RWTH AachenEschweilerGermany
- Department of Urology and Kidney TransplantationMartin Luther UniversityHalle (Saale)Germany
| | | | - Ana S.H. Costa
- MRC Cancer UnitHutchison/MRC Research CentreUniversity of CambridgeCambridgeUK
- Present address:
Cold Spring Harbor LaboratoryCold Spring HarborNYUSA
| | - Abel Sousa
- Institute for Research and Innovation in Health (i3s)PortoPortugal
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)HinxtonUK
| | - Pedro Beltrao
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)HinxtonUK
| | - Miguel Rocha
- Centre of Biological EngineeringUniversity of Minho ‐ Campus de GualtarBragaPortugal
| | - Jesper V. Olsen
- Faculty of Health and Medical SciencesProteomics ProgramNovo Nordisk Foundation Center for Protein ResearchUniversity of CopenhagenCopenhagenDenmark
| | - Christian Frezza
- MRC Cancer UnitHutchison/MRC Research CentreUniversity of CambridgeCambridgeUK
| | - Rafael Kramann
- Faculty of MedicineInstitute of Experimental Medicine and Systems BiologyRWTH Aachen UniversityAachenGermany
- Division of Nephrology and Clinical ImmunologyFaculty of MedicineRWTH Aachen UniversityAachenGermany
- Department of Internal Medicine, Nephrology and TransplantationErasmus Medical CenterRotterdamThe Netherlands
| | - Julio Saez‐Rodriguez
- Faculty of Medicine, and Heidelberg University HospitalInstitute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Faculty of MedicineJoint Research Centre for Computational Biomedicine (JRC‐COMBINE)RWTH Aachen UniversityAachenGermany
- Molecular Medicine Partnership Unit, European Molecular Biology LaboratoryHeidelberg UniversityHeidelbergGermany
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28
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29
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Paudel BB, Lewis JE, Hardeman KN, Hayford CE, Robbins CJ, Stauffer PE, Codreanu SG, Sherrod SD, McLean JA, Kemp ML, Quaranta V. An Integrative Gene Expression and Mathematical Flux Balance Analysis Identifies Targetable Redox Vulnerabilities in Melanoma Cells. Cancer Res 2020; 80:4565-4577. [PMID: 33060170 PMCID: PMC8456778 DOI: 10.1158/0008-5472.can-19-3588] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 04/22/2020] [Accepted: 07/06/2020] [Indexed: 12/14/2022]
Abstract
Melanomas harboring BRAF mutations can be treated with BRAF inhibitors (BRAFi), but responses are varied and tumor recurrence is inevitable. Here we used an integrative approach of experimentation and mathematical flux balance analyses in BRAF-mutated melanoma cells to discover that elevated antioxidant capacity is linked to BRAFi sensitivity in melanoma cells. High levels of antioxidant metabolites in cells with reduced BRAFi sensitivity confirmed this conclusion. By extending our analyses to other melanoma subtypes in The Cancer Genome Atlas, we predict that elevated redox capacity is a general feature of melanomas, not previously observed. We propose that redox vulnerabilities could be exploited for therapeutic benefits and identify unsuspected combination targets to enhance the effects of BRAFi in any melanoma, regardless of mutational status. SIGNIFICANCE: An integrative bioinformatics, flux balance analysis, and experimental approach identify targetable redox vulnerabilities and show the potential for modulation of cancer antioxidant defense to augment the benefits of existing therapies in melanoma.
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Affiliation(s)
- B. Bishal Paudel
- Department of Biochemistry, Vanderbilt University, Nashville, TN.,Quantitative Systems Biology Center (QSBC), Vanderbilt University, Nashville, TN.,Department of Biomedical Engineering, University of Virginia, Charlottesville, VA
| | - Joshua E. Lewis
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
| | - Keisha N. Hardeman
- Department of Biochemistry, Vanderbilt University, Nashville, TN.,Quantitative Systems Biology Center (QSBC), Vanderbilt University, Nashville, TN
| | - Corey E. Hayford
- Quantitative Systems Biology Center (QSBC), Vanderbilt University, Nashville, TN.,Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN
| | - Charles J. Robbins
- Department of Biochemistry, Vanderbilt University, Nashville, TN.,Quantitative Systems Biology Center (QSBC), Vanderbilt University, Nashville, TN
| | - Philip E. Stauffer
- Quantitative Systems Biology Center (QSBC), Vanderbilt University, Nashville, TN.,Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN
| | - Simona G. Codreanu
- Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN
| | - Stacy D. Sherrod
- Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN
| | - John A. McLean
- Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN
| | - Melissa L. Kemp
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
| | - Vito Quaranta
- Department of Biochemistry, Vanderbilt University, Nashville, TN.,Quantitative Systems Biology Center (QSBC), Vanderbilt University, Nashville, TN
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30
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Cruz-Gil S, Fernández LP, Sánchez-Martínez R, Gómez de Cedrón M, Ramírez de Molina A. Non-Coding and Regulatory RNAs as Epigenetic Remodelers of Fatty Acid Homeostasis in Cancer. Cancers (Basel) 2020; 12:E2890. [PMID: 33050166 PMCID: PMC7599548 DOI: 10.3390/cancers12102890] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/05/2020] [Accepted: 10/07/2020] [Indexed: 02/06/2023] Open
Abstract
Cancer cells commonly display metabolic fluctuations. Together with the Warburg effect and the increased glutaminolysis, alterations in lipid metabolism homeostasis have been recognized as a hallmark of cancer. Highly proliferative cancer cells upregulate de novo synthesis of fatty acids (FAs) which are required to support tumor progression by exerting multiple roles including structural cell membrane composition, regulators of the intracellular redox homeostasis, ATP synthesis, intracellular cell signaling molecules, and extracellular mediators of the tumor microenvironment. Epigenetic modifications have been shown to play a crucial role in human development, but also in the initiation and progression of complex diseases. The study of epigenetic processes could help to design new integral strategies for the prevention and treatment of metabolic disorders including cancer. Herein, we first describe the main altered intracellular fatty acid processes to support cancer initiation and progression. Next, we focus on the most important regulatory and non-coding RNAs (small noncoding RNA-sncRNAs-long non-coding RNAs-lncRNAs-and other regulatory RNAs) which may target the altered fatty acids pathway in cancer.
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Affiliation(s)
| | | | | | - Marta Gómez de Cedrón
- Correspondence: (M.G.d.C.); (A.R.d.M.); Tel.: +34-67-213-49-21 (A.R.d.M.); Fax: +34-91-830-59-61 (A.R.d.M.)
| | - Ana Ramírez de Molina
- Laboratory of Molecular Oncology, IMDEA-Food Institute, CEI UAM + CSIC, 28049 Madrid, Spain; (S.C.-G.); (L.P.F.); (R.S.-M.)
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31
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Long Non-Coding RNA and mRNA Profiling in Early-Stage Bovine Embryos Treated with Glutathione. Antioxidants (Basel) 2020; 9:antiox9050402. [PMID: 32397280 PMCID: PMC7278749 DOI: 10.3390/antiox9050402] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 04/20/2020] [Accepted: 05/03/2020] [Indexed: 01/13/2023] Open
Abstract
We measured differential expression profiles of genes and long non-coding RNA (lncRNA) using RNA sequencing in bovine embryos with or without glutathione (GSH) treatment. Bovine embryos fertilized in vitro were treated with GSH to blastocyst. Embryos at the 8-16-cell and morula stages were collected, with embryos without GSH treatment as the control. RNA was isolated, amplified, and sequenced. Differentially expressed genes (DEGs) and lncRNAs (DElncRNAs) were identified and bioinformatic analyses carried out. Transcript levels were confirmed using quantitative RT-PCR. A total of 4100 DEGs were identified, of which 3952 were in GSH-treated morulae and 884 in untreated morulae. More gene ontology (GO) terms were associated with GSH treatment than with control conditions. KEGG analysis showed that glutathione metabolism, citrate cycle, and metabolic pathways involving glycine, serine, and threonine were observed only in GSH-treated embryos. Among 4273 DElncRNAs identified, 59 were potentially important in GSH-treated embryo development, including 14 involved in glutathione metabolism. The 59 DElncRNAs co-expressed with protein-coding mRNAs involved similar GO terms and pathways as the DEGs. This appears to be the first comprehensive profiling of DEGs and DElncRNAs in bovine embryos fertilized in vitro with or without GSH, and the first systematic screen of potential lncRNAs in bovine embryos.
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32
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Robaina-Estévez S, Nikoloski Z. Flux-based hierarchical organization of Escherichia coli's metabolic network. PLoS Comput Biol 2020; 16:e1007832. [PMID: 32310959 PMCID: PMC7192501 DOI: 10.1371/journal.pcbi.1007832] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 04/30/2020] [Accepted: 03/30/2020] [Indexed: 11/18/2022] Open
Abstract
Biological networks across scales exhibit hierarchical organization that may constrain network function. Yet, understanding how these hierarchies arise due to the operational constraint of the networks and whether they impose limits to molecular phenotypes remains elusive. Here we show that metabolic networks include a hierarchy of reactions based on a natural flux ordering that holds for every steady state. We find that the hierarchy of reactions is reflected in experimental measurements of transcript, protein and flux levels of Escherichia coli under various growth conditions as well as in the catalytic rate constants of the corresponding enzymes. Our findings point at resource partitioning and a fine-tuning of enzyme levels in E. coli to respect the constraints imposed by the network structure at steady state. Since reactions in upper layers of the hierarchy impose an upper bound on the flux of the reactions downstream, the hierarchical organization of metabolism due to the flux ordering has direct applications in metabolic engineering.
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Affiliation(s)
- Semidán Robaina-Estévez
- Systems Biology and Mathematical Modeling Group. Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Bioinformatics Group, University of Potsdam, Potsdam, Germany
- Ronin Institute for Independent Scholarship, Montclair, New Jersey, United States of America
- * E-mail:
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group. Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Bioinformatics Group, University of Potsdam, Potsdam, Germany
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33
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Khodaee S, Asgari Y, Totonchi M, Karimi-Jafari MH. iMM1865: A New Reconstruction of Mouse Genome-Scale Metabolic Model. Sci Rep 2020; 10:6177. [PMID: 32277147 PMCID: PMC7148337 DOI: 10.1038/s41598-020-63235-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 03/26/2020] [Indexed: 11/12/2022] Open
Abstract
Since the first in silico generation of a genome-scale metabolic (GSM) model for Haemophilus influenzae in 1999, the GSM models have been reconstructed for various organisms including human and mouse. There are two important strategies for generating a GSM model: in the bottom-up approach, individual genomic and biochemical components are integrated to build a GSM model. Alternatively, the orthology-based strategy uses a previously reconstructed model of a reference organism to infer a GSM model of a target organism. Following the update and development of the metabolic network of reference organism, the model of the target organism can also be updated to eliminate defects. Here, we presented iMM1865 model as an orthology-based reconstruction of a GSM model for Mus musculus based on the last flux-consistent version of the human metabolic network, Recon3D. We proposed two versions of the new mouse model, iMM1865 and min-iMM1865, with the same number of gene-associated reactions but different subsets of non-gene-associated reactions. A third extended but flux-inconsistent model (iMM3254) was also created based on the extended version of Recon3D. Compared to the previously published mouse models, both versions of iMM1865 include more comprehensive annotations of metabolites and reactions with no dead-end metabolites and blocked reactions. We evaluated functionality of the models using 431 metabolic objective functions. iMM1865 and min-iMM1865 passed 93% and 87% of the tests, respectively, while iMM1415 and MMR (another available mouse GSM) passed 80% and 84% of the tests, respectively. Three versions of tissue-specific embryo heart models were also reconstructed from each of iMM1865 and min-iMM1865 using mCADRE algorithm with different thresholds on expression-based scores. The ability of corresponding GSM and embryo heart models to predict essential genes was assessed across experimentally derived lethal and viable gene sets. Our analysis revealed that tissue-specific models render much better predictions than GSM models.
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Affiliation(s)
- Saeideh Khodaee
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Yazdan Asgari
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Totonchi
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
- Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
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34
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Quantitative Connection between Cell Size and Growth Rate by Phospholipid Metabolism. Cells 2020; 9:cells9020391. [PMID: 32046235 PMCID: PMC7072380 DOI: 10.3390/cells9020391] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 02/04/2020] [Indexed: 11/16/2022] Open
Abstract
The processes involved in cell growth are extremely complicated even for a single cell organism such as Escherichia coli, while the relationship between growth rate and cell size is simple. We aimed to reveal the systematic link between them from the aspect of the genome-scale metabolic network. Since the growth rate reflects metabolic rates of bacteria and the cell size relates to phospholipid synthesis, a part of bacterial metabolic networks, we calculated the cell length from the cardiolipin synthesis rate, where the cardiolipin synthesis reaction is able to represent the phospholipid metabolism of Escherichia coli in the exponential growth phase. Combined with the flux balance analysis, it enables us to predict cell length and to examine the quantitative relationship between cell length and growth rate. By simulating bacteria growing in various nutrient media with the flux balance analysis and calculating the corresponding cell length, we found that the increase of the synthesis rate of phospholipid, the cell width, and the protein fraction in membranes caused the increase of cell length with growth rate. Different tendencies of phospholipid synthesis rate changing with growth rate result in different relationships between cell length and growth rate. The effects of gene deletions on cell size and growth rate are also examined. Knocking out the genes, such as Δ tktA, Δ tktB, Δ yqaB, Δ pgm, and Δ cysQ, affects growth rate largely while affecting cell length slightly. Results of this method are in good agreement with experiments.
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Abstract
The integration of high-throughput data to build predictive computational models of cellular metabolism is a major challenge of systems biology. These models are needed to predict cellular responses to genetic and environmental perturbations. Typically, this response involves both metabolic regulations related to the kinetic properties of enzymes and a genetic regulation affecting their concentrations. Thus, the integration of the transcriptional regulatory information is required to improve the accuracy and predictive ability of metabolic models. Integrative modeling is of primary importance to guide the search for various applications such as discovering novel potential drug targets to develop efficient therapeutic strategies for various diseases. In this paper, we propose an integrative predictive model based on techniques combining semantic web, probabilistic modeling, and constraint-based modeling methods. We applied our approach to human cancer metabolism to predict in silico the growth response of specific cancer cells under approved drug effects. Our method has proven successful in predicting the biomass rates of human liver cancer cells under drug-induced transcriptional perturbations.
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Alden K, Cosgrove J, Coles M, Timmis J. Using Emulation to Engineer and Understand Simulations of Biological Systems. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:302-315. [PMID: 29994223 DOI: 10.1109/tcbb.2018.2843339] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Modeling and simulation techniques have demonstrated success in studying biological systems. As the drive to better capture biological complexity leads to more sophisticated simulators, it becomes challenging to perform statistical analyses that help translate predictions into increased understanding. These analyses may require repeated executions and extensive sampling of high-dimensional parameter spaces: analyses that may become intractable due to time and resource limitations. Significant reduction in these requirements can be obtained using surrogate models, or emulators, that can rapidly and accurately predict the output of an existing simulator. We apply emulation to evaluate and enrich understanding of a previously published agent-based simulator of lymphoid tissue organogenesis, showing an ensemble of machine learning techniques can reproduce results obtained using a suite of statistical analyses within seconds. This performance improvement permits incorporation of previously intractable analyses, including multi-objective optimization to obtain parameter sets that yield a desired response, and Approximate Bayesian Computation to assess parametric uncertainty. To facilitate exploitation of emulation in simulation-focused studies, we extend our open source statistical package, spartan, to provide a suite of tools for emulator development, validation, and application. Overcoming resource limitations permits enriched evaluation and refinement, easing translation of simulator insights into increased biological understanding.
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Rawls KD, Blais EM, Dougherty BV, Vinnakota KC, Pannala VR, Wallqvist A, Kolling GL, Papin JA. Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes. Toxicol Sci 2019; 172:279-291. [PMID: 31501904 PMCID: PMC6876259 DOI: 10.1093/toxsci/kfz197] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Context-specific GEnome-scale metabolic Network REconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically-exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to four toxicants (acetaminophen, carbon tetrachloride, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hour. TIMBR predictions were compared with paired fresh and spent media metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure. Here, we identified changes in the TCA metabolites citrate and alpha-ketoglutarate along with changes in carbohydrate metabolism and interruptions in ATP production and the TCA Cycle. Where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.
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Affiliation(s)
- Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
| | - Edik M Blais
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
| | - Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
| | - Kalyan C Vinnakota
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, Maryland 20817
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
| | - Venkat R Pannala
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, Maryland 20817
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
- Department of Medicine, Division of Infectious Diseases and International Health
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
- Department of Medicine, Division of Infectious Diseases and International Health
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia 22908
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Dai Z, Yang S, Xu L, Hu H, Liao K, Wang J, Wang Q, Gao S, Li B, Lai L. Identification of Cancer-associated metabolic vulnerabilities by modeling multi-objective optimality in metabolism. Cell Commun Signal 2019; 17:124. [PMID: 31601242 PMCID: PMC6785927 DOI: 10.1186/s12964-019-0439-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 09/10/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Cancer cells undergo global reprogramming of cellular metabolism to satisfy demands of energy and biomass during proliferation and metastasis. Computational modeling of genome-scale metabolic models is an effective approach for designing new therapeutics targeting dysregulated cancer metabolism by identifying metabolic enzymes crucial for satisfying metabolic goals of cancer cells, but nearly all previous studies neglect the existence of metabolic demands other than biomass synthesis and trade-offs between these contradicting metabolic demands. It is thus necessary to develop computational models covering multiple metabolic objectives to study cancer metabolism and identify novel metabolic targets. METHODS We developed a multi-objective optimization model for cancer cell metabolism at genome-scale and an integrated, data-driven workflow for analyzing the Pareto optimality of this model in achieving multiple metabolic goals and identifying metabolic enzymes crucial for maintaining cancer-associated metabolic phenotypes. Using this workflow, we constructed cell line-specific models for a panel of cancer cell lines and identified lists of metabolic targets promoting or suppressing cancer cell proliferation or the Warburg Effect. The targets were then validated using knockdown and over-expression experiments in cultured cancer cell lines. RESULTS We found that the multi-objective optimization model correctly predicted phenotypes including cell growth rates, essentiality of metabolic genes and cell line specific sensitivities to metabolic perturbations. To our surprise, metabolic enzymes promoting proliferation substantially overlapped with those suppressing the Warburg Effect, suggesting that simply targeting the overlapping enzymes may lead to complicated outcomes. We also identified lists of metabolic enzymes important for maintaining rapid proliferation or high Warburg Effect while having little effect on the other. The importance of these enzymes in cancer metabolism predicted by the model was validated by their association with cancer patient survival and knockdown and overexpression experiments in a variety of cancer cell lines. CONCLUSIONS These results confirm this multi-objective optimization model as a novel and effective approach for studying trade-off between metabolic demands of cancer cells and identifying cancer-associated metabolic vulnerabilities, and suggest novel metabolic targets for cancer treatment.
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Affiliation(s)
- Ziwei Dai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Shiyu Yang
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Liyan Xu
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Hongrong Hu
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Kun Liao
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jianghuang Wang
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Qian Wang
- Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Shuaishi Gao
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Bo Li
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China. .,Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China. .,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
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Gatto F, Ferreira R, Nielsen J. Pan-cancer analysis of the metabolic reaction network. Metab Eng 2019; 57:51-62. [PMID: 31526853 DOI: 10.1016/j.ymben.2019.09.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 07/29/2019] [Accepted: 09/10/2019] [Indexed: 12/25/2022]
Abstract
Metabolic reprogramming is considered a hallmark of malignant transformation. However, it is not clear whether the network of metabolic reactions expressed by cancers of different origin differ from each other or from normal human tissues. In this study, we reconstructed functional and connected genome-scale metabolic models for 917 primary tumor samples across 13 types based on the probability of expression for 3765 reference metabolic genes in the sample. This network-centric approach revealed that tumor metabolic networks are largely similar in terms of accounted reactions, despite diversity in the expression of the associated genes. On average, each network contained 4721 reactions, of which 74% were core reactions (present in >95% of all models). Whilst 99.3% of the core reactions were classified as housekeeping also in normal tissues, we identified reactions catalyzed by ARG2, RHAG, SLC6 and SLC16 family gene members, and PTGS1 and PTGS2 as core exclusively in cancer. These findings were subsequently replicated in an independent validation set of 3388 genome-scale metabolic models. The remaining 26% of the reactions were contextual reactions. Their inclusion was dependent in one case (GLS2) on the absence of TP53 mutations and in 94.6% of cases on differences in cancer types. This dependency largely resembled differences in expression patterns in the corresponding normal tissues, with some exceptions like the presence of the NANP-encoded reaction in tumors not from the female reproductive system or of the SLC5A9-encoded reaction in kidney-pancreatic-colorectal tumors. In conclusion, tumors expressed a metabolic network virtually overlapping the matched normal tissues, raising the possibility that metabolic reprogramming simply reflects cancer cell plasticity to adapt to varying conditions thanks to redundancy and complexity of the underlying metabolic networks. At the same time, the here uncovered exceptions represent a resource to identify selective liabilities of tumor metabolism.
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Affiliation(s)
- Francesco Gatto
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Raphael Ferreira
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark.
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40
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Cui C, Shi B, Shi J, Zhou Y, Cui Q. Defining the Importance Score of Human MicroRNAs and Their Single Nucleotide Mutants Using Random Forest Regression and Sequence Data. ADVANCED THEORY AND SIMULATIONS 2019. [DOI: 10.1002/adts.201900083] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Chunmei Cui
- Department of Biomedical InformaticsDepartment of Physiology and PathophysiologyCenter for Noncoding RNA MedicineMOE Key Lab of Cardiovascular SciencesSchool of Basic Medical SciencesPeking University 38 Xueyuan Rd Beijing 100191 China
| | - Bing Shi
- Department of CardiologyBeijing Military General Hospital Beijing 100700 China
| | - Jiangcheng Shi
- Department of Biomedical InformaticsDepartment of Physiology and PathophysiologyCenter for Noncoding RNA MedicineMOE Key Lab of Cardiovascular SciencesSchool of Basic Medical SciencesPeking University 38 Xueyuan Rd Beijing 100191 China
| | - Yuan Zhou
- Department of Biomedical InformaticsDepartment of Physiology and PathophysiologyCenter for Noncoding RNA MedicineMOE Key Lab of Cardiovascular SciencesSchool of Basic Medical SciencesPeking University 38 Xueyuan Rd Beijing 100191 China
| | - Qinghua Cui
- Department of Biomedical InformaticsDepartment of Physiology and PathophysiologyCenter for Noncoding RNA MedicineMOE Key Lab of Cardiovascular SciencesSchool of Basic Medical SciencesPeking University 38 Xueyuan Rd Beijing 100191 China
- Center of BioinformaticsKey Laboratory for Neuro‐Information of Ministry of EducationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of China Chengdu 610054 China
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41
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Ren X, Deschênes JS, Tremblay R, Peres S, Jolicoeur M. A kinetic metabolic study of lipid production in Chlorella protothecoides under heterotrophic condition. Microb Cell Fact 2019; 18:113. [PMID: 31253148 PMCID: PMC6598345 DOI: 10.1186/s12934-019-1163-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 06/19/2019] [Indexed: 11/13/2022] Open
Abstract
Background Microalgae have been proposed as potential platform to produce lipid-derived products, such as biofuels. Knowledge on the intracellular carbon flow distribution may identify key metabolic processes during lipid synthesis thus refining culture/genetic strategies to maximize cell lipid productivity. A kinetic metabolic model simulating cell metabolic behavior and lipid production was first applied in the microalgae platform Chlorella protothecoides under heterotrophic condition. It combines both physiology and flux information in a kinetic approach. Cell nutrition, growth, lipid production and almost 30 metabolic intermediates covering central carbon metabolism were included and simulated. Results Model simulations were shown to adequately agree with experimental data, which is suggesting that the proposed model copes with Chlorella protothecoides cells’ biology. The dynamic metabolic flux analysis using the model showed a reversible starch flux from accumulation to decomposing when glucose reached depletion, while net lipid flux shows a quasi-constant rate. The sensitive flux parameters on starch and lipid metabolism suggested that starch synthesis is the major competing pathway that affects lipid accumulation in C. protothecoides. Flux analysis also demonstrated that high lipid yield under heterotrophic condition is accompanied with high lipid flux and low TCA activity. Meanwhile, the dynamic flux distribution also suggests a relatively constant ratio of glucose distributed to biomass, lipid, starch, nucleotides as well as pentose phosphate pathway. Conclusion The model described not only experimental data, but also unraveled intracellular carbon flow distribution and identify key metabolic processes during lipid synthesis. Most of the metabolic kinetics also showed statistical significance for metabolic mechanism. Therefore, this study unravels the mechanisms of the glucose impact on the dynamic carbon flux distribution, thus improving our understanding of the links between carbon fluxes and lipid metabolism in C. protothecoides. Electronic supplementary material The online version of this article (10.1186/s12934-019-1163-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiaojie Ren
- Colin Ratledge Center for Microbial Lipids, School of Agriculture Engineering and Food Science, Shandong University of Technology, Zibo, China.,Research Laboratory in Applied Metabolic Engineering, Department of Chemical Engineering, École Polytechnique de Montreal, Centre-ville Station, P.O. Box 6079, Montreal, H3C 3A7, QC, Canada
| | | | - Réjean Tremblay
- Université du Québec à Rimouski, 310 allée des Ursulines, Rimouski, QC, G5L 3A1, Canada
| | - Sabine Peres
- LRI, Université Paris-Sud, CNRS, Université Paris-Saclay, 91405, Orsay, France.,MaIAGE, INRA, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Mario Jolicoeur
- Research Laboratory in Applied Metabolic Engineering, Department of Chemical Engineering, École Polytechnique de Montreal, Centre-ville Station, P.O. Box 6079, Montreal, H3C 3A7, QC, Canada.
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42
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Richelle A, Chiang AWT, Kuo CC, Lewis NE. Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions. PLoS Comput Biol 2019; 15:e1006867. [PMID: 30986217 PMCID: PMC6483243 DOI: 10.1371/journal.pcbi.1006867] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 04/25/2019] [Accepted: 02/13/2019] [Indexed: 12/26/2022] Open
Abstract
Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.
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Affiliation(s)
- Anne Richelle
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
| | - Austin W. T. Chiang
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
| | - Chih-Chung Kuo
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Nathan E. Lewis
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
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43
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Sarkar D, Mueller TJ, Liu D, Pakrasi HB, Maranas CD. A diurnal flux balance model of Synechocystis sp. PCC 6803 metabolism. PLoS Comput Biol 2019; 15:e1006692. [PMID: 30677028 PMCID: PMC6364703 DOI: 10.1371/journal.pcbi.1006692] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 02/05/2019] [Accepted: 12/03/2018] [Indexed: 11/26/2022] Open
Abstract
Phototrophic organisms such as cyanobacteria utilize the sun's energy to convert atmospheric carbon dioxide into organic carbon, resulting in diurnal variations in the cell's metabolism. Flux balance analysis is a widely accepted constraint-based optimization tool for analyzing growth and metabolism, but it is generally used in a time-invariant manner with no provisions for sequestering different biomass components at different time periods. Here we present CycleSyn, a periodic model of Synechocystis sp. PCC 6803 metabolism that spans a 12-hr light/12-hr dark cycle by segmenting it into 12 Time Point Models (TPMs) with a uniform duration of two hours. The developed framework allows for the flow of metabolites across TPMs while inventorying metabolite levels and only allowing for the utilization of currently or previously produced compounds. The 12 TPMs allow for the incorporation of time-dependent constraints that capture the cyclic nature of cellular processes. Imposing bounds on reactions informed by temporally-segmented transcriptomic data enables simulation of phototrophic growth as a single linear programming (LP) problem. The solution provides the time varying reaction fluxes over a 24-hour cycle and the accumulation/consumption of metabolites. The diurnal rhythm of metabolic gene expression driven by the circadian clock and its metabolic consequences is explored. Predicted flux and metabolite pools are in line with published studies regarding the temporal organization of phototrophic growth in Synechocystis PCC 6803 paving the way for constructing time-resolved genome-scale models (GSMs) for organisms with a circadian clock. In addition, the metabolic reorganization that would be required to enable Synechocystis PCC 6803 to temporally separate photosynthesis from oxygen-sensitive nitrogen fixation is also explored using the developed model formalism.
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Affiliation(s)
- Debolina Sarkar
- Department of Chemical Engineering, Pennsylvania State University,
University Park, Pennsylvania, United States of America
| | - Thomas J. Mueller
- Department of Chemical Engineering, Pennsylvania State University,
University Park, Pennsylvania, United States of America
| | - Deng Liu
- Department of Biology, Washington University, St. Louis, Missouri, United
States of America
| | - Himadri B. Pakrasi
- Department of Biology, Washington University, St. Louis, Missouri, United
States of America
| | - Costas D. Maranas
- Department of Chemical Engineering, Pennsylvania State University,
University Park, Pennsylvania, United States of America
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Zeng P, Chen J, Meng Y, Zhou Y, Yang J, Cui Q. Defining Essentiality Score of Protein-Coding Genes and Long Noncoding RNAs. Front Genet 2018; 9:380. [PMID: 30356729 PMCID: PMC6189311 DOI: 10.3389/fgene.2018.00380] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 08/27/2018] [Indexed: 12/16/2022] Open
Abstract
Measuring the essentiality of genes is critically important in biology and medicine. Here we proposed a computational method, GIC (Gene Importance Calculator), which can efficiently predict the essentiality of both protein-coding genes and long noncoding RNAs (lncRNAs) based on only sequence information. For identifying the essentiality of protein-coding genes, GIC outperformed well-established computational scores. In an independent mouse lncRNA dataset, GIC also achieved an exciting performance (AUC = 0.918). In contrast, the traditional computational methods are not applicable to lncRNAs. Moreover, we explored several potential applications of GIC score. Firstly, we revealed a correlation between gene GIC score and research hotspots of genes. Moreover, GIC score can be used to evaluate whether a gene in mouse is representative for its homolog in human by dissecting its cross-species difference. This is critical for basic medicine because many basic medical studies are performed in animal models. Finally, we showed that GIC score can be used to identify candidate genes from a transcriptomics study. GIC is freely available at http://www.cuilab.cn/gic/.
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Affiliation(s)
- Pan Zeng
- School of Basic Medical Sciences, MOE Key Lab of Cardiovascular Sciences, Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Centre for Noncoding RNA Medicine, Peking University, Beijing, China
| | - Ji Chen
- School of Basic Medical Sciences, MOE Key Lab of Cardiovascular Sciences, Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Centre for Noncoding RNA Medicine, Peking University, Beijing, China
| | - Yuhong Meng
- School of Basic Medical Sciences, MOE Key Lab of Cardiovascular Sciences, Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Centre for Noncoding RNA Medicine, Peking University, Beijing, China
| | - Yuan Zhou
- School of Basic Medical Sciences, MOE Key Lab of Cardiovascular Sciences, Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Centre for Noncoding RNA Medicine, Peking University, Beijing, China
| | - Jichun Yang
- School of Basic Medical Sciences, MOE Key Lab of Cardiovascular Sciences, Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Centre for Noncoding RNA Medicine, Peking University, Beijing, China
| | - Qinghua Cui
- School of Basic Medical Sciences, MOE Key Lab of Cardiovascular Sciences, Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Centre for Noncoding RNA Medicine, Peking University, Beijing, China
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Wu WH, Chien CY, Wu YH, Wu HH, Lai JM, Chang PMH, Huang CYF, Wang FS. Inferring oncoenzymes in a genome-scale metabolic network for hepatocytes using bilevel optimization framework. J Taiwan Inst Chem Eng 2018. [DOI: 10.1016/j.jtice.2018.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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46
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Pyatnitskiy MA, Karpov DS, Moshkovskii SA. [Searching for essential genes in cancer genomes]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2018; 64:303-314. [PMID: 30135277 DOI: 10.18097/pbmc20186404303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The concept of essential genes, whose loss of functionality leads to cell death, is one of the fundamental concepts of genetics and is important for fundamental and applied research. This field is particularly promising in relation to oncology, since the search for genetic vulnerabilities of cancer cells allows us to identify new potential targets for antitumor therapy. The modern biotechnology capacities allow carrying out large-scale projects for sequencing somatic mutations in tumors, as well as directly interfering the genetic apparatus of cancer cells. They provided accumulation of a considerable body of knowledge about genetic variants and corresponding phenotypic manifestations in tumors. In the near future this knowledge will find application in clinical practice. This review describes the main experimental and computational approaches to the search for essential genes, concentrating on the application of these methods in the field of molecular oncology.
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Affiliation(s)
- M A Pyatnitskiy
- Institute of Biomedical Chemistry, Moscow, Russia; Higher School of Economics, Moscow, Russia
| | - D S Karpov
- Institute of Biomedical Chemistry, Moscow, Russia; Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - S A Moshkovskii
- Institute of Biomedical Chemistry, Moscow, Russia; Pirogov Russian National Research Medical University, Moscow, Russia
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The glutathione redox system is essential to prevent ferroptosis caused by impaired lipid metabolism in clear cell renal cell carcinoma. Oncogene 2018; 37:5435-5450. [PMID: 29872221 DOI: 10.1038/s41388-018-0315-z] [Citation(s) in RCA: 222] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 03/22/2018] [Accepted: 04/08/2018] [Indexed: 12/20/2022]
Abstract
Metabolic reprogramming is a prominent feature of clear cell renal cell carcinoma (ccRCC). Here we investigated metabolic dependencies in a panel of ccRCC cell lines using nutrient depletion, functional RNAi screening and inhibitor treatment. We found that ccRCC cells are highly sensitive to the depletion of glutamine or cystine, two amino acids required for glutathione (GSH) synthesis. Moreover, silencing of enzymes of the GSH biosynthesis pathway or glutathione peroxidases, which depend on GSH for the removal of cellular hydroperoxides, selectively reduced viability of ccRCC cells but did not affect the growth of non-malignant renal epithelial cells. Inhibition of GSH synthesis triggered ferroptosis, an iron-dependent form of cell death associated with enhanced lipid peroxidation. VHL is a major tumour suppressor in ccRCC and loss of VHL leads to stabilisation of hypoxia inducible factors HIF-1α and HIF-2α. Restoration of functional VHL via exogenous expression of pVHL reverted ccRCC cells to an oxidative metabolism and rendered them insensitive to the induction of ferroptosis. VHL reconstituted cells also exhibited reduced lipid storage and higher expression of genes associated with oxidiative phosphorylation and fatty acid metabolism. Importantly, inhibition of β-oxidation or mitochondrial ATP-synthesis restored ferroptosis sensitivity in VHL reconstituted cells. We also found that inhibition of GSH synthesis blocked tumour growth in a MYC-dependent mouse model of renal cancer. Together, our data suggest that reduced fatty acid metabolism due to inhibition of β-oxidation renders renal cancer cells highly dependent on the GSH/GPX pathway to prevent lipid peroxidation and ferroptotic cell death.
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Guo FB, Dong C, Hua HL, Liu S, Luo H, Zhang HW, Jin YT, Zhang KY. Accurate prediction of human essential genes using only nucleotide composition and association information. Bioinformatics 2018; 33:1758-1764. [PMID: 28158612 PMCID: PMC7110051 DOI: 10.1093/bioinformatics/btx055] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 01/25/2017] [Indexed: 12/20/2022] Open
Abstract
Motivation Previously constructed classifiers in predicting eukaryotic essential genes integrated a variety of features including experimental ones. If we can obtain satisfactory prediction using only nucleotide (sequence) information, it would be more promising. Three groups recently identified essential genes in human cancer cell lines using wet experiments and it provided wonderful opportunity to accomplish our idea. Here we improved the Z curve method into the λ-interval form to denote nucleotide composition and association information and used it to construct the SVM classifying model. Results Our model accurately predicted human gene essentiality with an AUC higher than 0.88 both for 5-fold cross-validation and jackknife tests. These results demonstrated that the essentiality of human genes could be reliably reflected by only sequence information. We re-predicted the negative dataset by our Pheg server and 118 genes were additionally predicted as essential. Among them, 20 were found to be homologues in mouse essential genes, indicating that some of the 118 genes were indeed essential, however previous experiments overlooked them. As the first available server, Pheg could predict essentiality for anonymous gene sequences of human. It is also hoped the λ-interval Z curve method could be effectively extended to classification issues of other DNA elements. Availability and Implementation http://cefg.uestc.edu.cn/Pheg. Contact fbguo@uestc.edu.cn. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Feng-Biao Guo
- School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Chuan Dong
- School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong-Li Hua
- School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuo Liu
- School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Luo
- Department of Physics, Tianjin University, Tianjin, China
| | - Hong-Wan Zhang
- School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan-Ting Jin
- School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Kai-Yue Zhang
- School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
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Brunk E, Sahoo S, Zielinski DC, Altunkaya A, Dräger A, Mih N, Gatto F, Nilsson A, Gonzalez GAP, Aurich MK, Prlić A, Sastry A, Danielsdottir AD, Heinken A, Noronha A, Rose PW, Burley SK, Fleming RM, Nielsen J, Thiele I, Palsson BO. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol 2018; 36:272-281. [PMID: 29457794 PMCID: PMC5840010 DOI: 10.1038/nbt.4072] [Citation(s) in RCA: 408] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Accepted: 01/10/2018] [Indexed: 12/14/2022]
Abstract
Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.
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Affiliation(s)
- Elizabeth Brunk
- Department of Bioengineering, University of California San Diego CA 92093
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Swagatika Sahoo
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | | | - Ali Altunkaya
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Andreas Dräger
- Applied Bioinformatics Group, Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, 72076 Tübingen, Germany
| | - Nathan Mih
- Department of Bioengineering, University of California San Diego CA 92093
| | - Francesco Gatto
- Department of Bioengineering, University of California San Diego CA 92093
- Department of Biology and Biological Engineering, Chalmers University of Technology, Sweden
| | - Avlant Nilsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Sweden
| | | | - Maike Kathrin Aurich
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Andreas Prlić
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anand Sastry
- Department of Bioengineering, University of California San Diego CA 92093
| | - Anna D. Danielsdottir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Alberto Noronha
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Peter W. Rose
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Stephen K. Burley
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, and Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ronan M.T. Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Jens Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
- Department of Biology and Biological Engineering, Chalmers University of Technology, Sweden
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego CA 92093
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
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50
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Banos DT, Trébulle P, Elati M. Integrating transcriptional activity in genome-scale models of metabolism. BMC SYSTEMS BIOLOGY 2017; 11:134. [PMID: 29322933 PMCID: PMC5763306 DOI: 10.1186/s12918-017-0507-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Genome-scale metabolic models provide an opportunity for rational approaches to studies of the different reactions taking place inside the cell. The integration of these models with gene regulatory networks is a hot topic in systems biology. The methods developed to date focus mostly on resolving the metabolic elements and use fairly straightforward approaches to assess the impact of genome expression on the metabolic phenotype. Results We present here a method for integrating the reverse engineering of gene regulatory networks into these metabolic models. We applied our method to a high-dimensional gene expression data set to infer a background gene regulatory network. We then compared the resulting phenotype simulations with those obtained by other relevant methods. Conclusions Our method outperformed the other approaches tested and was more robust to noise. We also illustrate the utility of this method for studies of a complex biological phenomenon, the diauxic shift in yeast.
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Affiliation(s)
- Daniel Trejo Banos
- UMR 8030 Génomique Métabolique / Laboratoire iSSB CEA-CNRS-UEVE, Genopole campus 1, 5 rue Henri Desbruères, Cedex Évry, 91030, France
| | - Pauline Trébulle
- UMR 8030 Génomique Métabolique / Laboratoire iSSB CEA-CNRS-UEVE, Genopole campus 1, 5 rue Henri Desbruères, Cedex Évry, 91030, France.,Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, 78350, France
| | - Mohamed Elati
- UMR 8030 Génomique Métabolique / Laboratoire iSSB CEA-CNRS-UEVE, Genopole campus 1, 5 rue Henri Desbruères, Cedex Évry, 91030, France. .,Université Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Lille, F-59000, France.
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