1
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Hashemi S, Laitinen R, Nikoloski Z. Models and molecular mechanisms for trade-offs in the context of metabolism. Mol Ecol 2024; 33:e16879. [PMID: 36773330 DOI: 10.1111/mec.16879] [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: 08/10/2022] [Revised: 01/19/2023] [Accepted: 02/07/2023] [Indexed: 02/13/2023]
Abstract
Accumulating evidence for trade-offs involving metabolic traits has demonstrated their importance in the evolution of organisms. Metabolic models with different levels of complexity have already been considered when investigating mechanisms that explain various metabolic trade-offs. Here we provide a systematic review of modelling approaches that have been used to study and explain trade-offs between: (i) the kinetic properties of individual enzymes, (ii) rates of metabolic reactions, (iii) the rate and yield of metabolic pathways and networks, (iv) different metabolic objectives in single organisms and in metabolic communities, and (v) metabolic concentrations. In providing insights into the mechanisms underlying these five types of metabolic trade-offs obtained from constraint-based metabolic modelling, we emphasize the relationship of metabolic trade-offs to the classical black box Y-model that provides a conceptual explanation for resource acquisition-allocation trade-offs. In addition, we identify several pressing concerns and offer a perspective for future research in the identification and manipulation of metabolic trade-offs by relying on the toolbox provided by constraint-based metabolic modelling for single organisms and microbial communities.
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Affiliation(s)
- Seirana Hashemi
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Roosa Laitinen
- Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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2
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Rao X, Li D, Su Z, Nomura CT, Chen S, Wang Q. A smart RBS library and its prediction model for robust and accurate fine-tuning of gene expression in Bacillus species. Metab Eng 2024; 81:1-9. [PMID: 37951459 DOI: 10.1016/j.ymben.2023.11.002] [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: 08/15/2023] [Revised: 10/17/2023] [Accepted: 11/05/2023] [Indexed: 11/14/2023]
Abstract
Bacillus species, such as Bacillus subtilis and Bacillus licheniformis, are important industrial bacteria. However, there is a lack of standardized and predictable genetic tools for convenient and reproducible assembly of genetic modules in Bacillus species to realize their full potential. In this study, we constructed a Ribosome Binding Site (RBS) library in B. licheniformis, which provides incremental regulation of expression levels over a 104-fold range. Additionally, we developed a model to quantify the resulting translation rates. We successfully demonstrated the robust expression of various target genes using the RBS library and showed that the model accurately predicts the translation rates of arbitrary coding genes. Importantly, we also extended the use of the RBS library and prediction model to B. subtilis, B. thuringiensis, and B. amyloliquefacie. The versatility of the RBS library and its prediction model enables quantification of biological behavior, facilitating reliable forward engineering of gene expression.
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Affiliation(s)
- Xiaolan Rao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Environmental Microbial Technology Center of Hubei Province, Hubei University, Wuhan 430062, PR China
| | - Dian Li
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Environmental Microbial Technology Center of Hubei Province, Hubei University, Wuhan 430062, PR China
| | - Zhaowei Su
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Environmental Microbial Technology Center of Hubei Province, Hubei University, Wuhan 430062, PR China
| | | | - Shouwen Chen
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Environmental Microbial Technology Center of Hubei Province, Hubei University, Wuhan 430062, PR China.
| | - Qin Wang
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Environmental Microbial Technology Center of Hubei Province, Hubei University, Wuhan 430062, PR China.
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3
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Griesemer M, Navid A. Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites. Microorganisms 2023; 11:2149. [PMID: 37763993 PMCID: PMC10536367 DOI: 10.3390/microorganisms11092149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/07/2023] [Accepted: 08/16/2023] [Indexed: 09/29/2023] Open
Abstract
Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the "chassis" organism's metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.
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Affiliation(s)
| | - Ali Navid
- Lawrence Livermore National Laboratory, Biosciences & Biotechnology Division, Physical & Life Sciences Directorate, Livermore, CA 94550, USA
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4
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Achreja A, Yu T, Mittal A, Choppara S, Animasahun O, Nenwani M, Wuchu F, Meurs N, Mohan A, Jeon JH, Sarangi I, Jayaraman A, Owen S, Kulkarni R, Cusato M, Weinberg F, Kweon HK, Subramanian C, Wicha MS, Merajver SD, Nagrath S, Cho KR, DiFeo A, Lu X, Nagrath D. Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer. Nat Metab 2022; 4:1119-1137. [PMID: 36131208 DOI: 10.1038/s42255-022-00636-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/09/2022] [Indexed: 11/08/2022]
Abstract
Recurrent loss-of-function deletions cause frequent inactivation of tumour suppressor genes but often also involve the collateral deletion of essential genes in chromosomal proximity, engendering dependence on paralogues that maintain similar function. Although these paralogues are attractive anticancer targets, no methodology exists to uncover such collateral lethal genes. Here we report a framework for collateral lethal gene identification via metabolic fluxes, CLIM, and use it to reveal MTHFD2 as a collateral lethal gene in UQCR11-deleted ovarian tumours. We show that MTHFD2 has a non-canonical oxidative function to provide mitochondrial NAD+, and demonstrate the regulation of systemic metabolic activity by the paralogue metabolic pathway maintaining metabolic flux compensation. This UQCR11-MTHFD2 collateral lethality is confirmed in vivo, with MTHFD2 inhibition leading to complete remission of UQCR11-deleted ovarian tumours. Using CLIM's machine learning and genome-scale metabolic flux analysis, we elucidate the broad efficacy of targeting MTHFD2 despite distinct cancer genetic profiles co-occurring with UQCR11 deletion and irrespective of stromal compositions of tumours.
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Affiliation(s)
- Abhinav Achreja
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Tao Yu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Anjali Mittal
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Srinadh Choppara
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Olamide Animasahun
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Minal Nenwani
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Fulei Wuchu
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Noah Meurs
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Aradhana Mohan
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Jin Heon Jeon
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Itisam Sarangi
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Anusha Jayaraman
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Sarah Owen
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Reva Kulkarni
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Michele Cusato
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Frank Weinberg
- Hematology and Oncology, University of Illinois, Chicago, IL, USA
| | - Hye Kyong Kweon
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Chitra Subramanian
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Max S Wicha
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Sofia D Merajver
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Sunitha Nagrath
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Kathleen R Cho
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA
| | - Analisa DiFeo
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA
| | - Xiongbin Lu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
- Melvin & Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Deepak Nagrath
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA.
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA.
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA.
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
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Lee MK, Mohamad MS, Choon YW, Mohd Daud K, Nasarudin NA, Ismail MA, Ibrahim Z, Napis S, Sinnott RO. Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli. J Integr Bioinform 2020; 17:jib-2019-0073. [PMID: 32374287 PMCID: PMC7734505 DOI: 10.1515/jib-2019-0073] [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: 08/30/2019] [Accepted: 01/18/2020] [Indexed: 11/15/2022] Open
Abstract
The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite’s production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.
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Affiliation(s)
- Mee K Lee
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Mohd Saberi Mohamad
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu Kelantan, Malaysia.,Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, Lock Bag 100, 17600 Jeli Kelantan, Malaysia
| | - Yee Wen Choon
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Kauthar Mohd Daud
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Nurul Athirah Nasarudin
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Mohd Arfian Ismail
- Soft Computing and Intelligent System Research Group, Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, 26300 Kuantan, Pahang, Malaysia
| | - Zuwairie Ibrahim
- Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Suhaimi Napis
- Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Richard O Sinnott
- School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC 3052 Australia
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6
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Daud KM, Mohamad MS, Zakaria Z, Hassan R, Shah ZA, Deris S, Ibrahim Z, Napis S, Sinnott RO. A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout. Comput Biol Med 2019; 113:103390. [PMID: 31450056 DOI: 10.1016/j.compbiomed.2019.103390] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 08/15/2019] [Accepted: 08/15/2019] [Indexed: 01/06/2023]
Abstract
Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate.
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Affiliation(s)
- Kauthar Mohd Daud
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
| | - Mohd Saberi Mohamad
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100, Kota Bharu, Kelantan, Malaysia; Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, Lock Bag 100, 17600, Jeli, Kelantan, Malaysia.
| | - Zalmiyah Zakaria
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
| | - Rohayanti Hassan
- Software Engineering Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
| | - Zuraini Ali Shah
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
| | - Safaai Deris
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100, Kota Bharu, Kelantan, Malaysia; Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, Lock Bag 100, 17600, Jeli, Kelantan, Malaysia
| | - Zuwairie Ibrahim
- Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Suhaimi Napis
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
| | - Richard O Sinnott
- School of Computing and Information Systems, Melbourne School of Engineering, University of Melbourne, Victoria, 3010, Australia
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Vijayakumar S, Conway M, Lió P, Angione C. Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling. Brief Bioinform 2019; 19:1218-1235. [PMID: 28575143 DOI: 10.1093/bib/bbx053] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Indexed: 11/13/2022] Open
Abstract
Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.
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Affiliation(s)
| | - Max Conway
- Computer Laboratory, University of Cambridge, UK
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, UK
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Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine. BIOMED RESEARCH INTERNATIONAL 2019; 2019:8304260. [PMID: 31281846 PMCID: PMC6590590 DOI: 10.1155/2019/8304260] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/07/2019] [Accepted: 05/20/2019] [Indexed: 01/06/2023]
Abstract
In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.
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Navid A, Jiao Y, Wong SE, Pett-Ridge J. System-level analysis of metabolic trade-offs during anaerobic photoheterotrophic growth in Rhodopseudomonas palustris. BMC Bioinformatics 2019; 20:233. [PMID: 31072303 PMCID: PMC6509789 DOI: 10.1186/s12859-019-2844-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 04/24/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Living organisms need to allocate their limited resources in a manner that optimizes their overall fitness by simultaneously achieving several different biological objectives. Examination of these biological trade-offs can provide invaluable information regarding the biophysical and biochemical bases behind observed cellular phenotypes. A quantitative knowledge of a cell system's critical objectives is also needed for engineering of cellular metabolism, where there is interest in mitigating the fitness costs that may result from human manipulation. RESULTS To study metabolism in photoheterotrophs, we developed and validated a genome-scale model of metabolism in Rhodopseudomonas palustris, a metabolically versatile gram-negative purple non-sulfur bacterium capable of growing phototrophically on various carbon sources, including inorganic carbon and aromatic compounds. To quantitatively assess trade-offs among a set of important biological objectives during different metabolic growth modes, we used our new model to conduct an 8-dimensional multi-objective flux analysis of metabolism in R. palustris. Our results revealed that phototrophic metabolism in R. palustris is light-limited under anaerobic conditions, regardless of the available carbon source. Under photoheterotrophic conditions, R. palustris prioritizes the optimization of carbon efficiency, followed by ATP production and biomass production rate, in a Pareto-optimal manner. To achieve maximum carbon fixation, cells appear to divert limited energy resources away from growth and toward CO2 fixation, even in the presence of excess reduced carbon. We also found that to achieve the theoretical maximum rate of biomass production, anaerobic metabolism requires import of additional compounds (such as protons) to serve as electron acceptors. Finally, we found that production of hydrogen gas, of potential interest as a candidate biofuel, lowers the cellular growth rates under all circumstances. CONCLUSIONS Photoheterotrophic metabolism of R. palustris is primarily regulated by the amount of light it can absorb and not the availability of carbon. However, despite carbon's secondary role as a regulating factor, R. palustris' metabolism strives for maximum carbon efficiency, even when this increased efficiency leads to slightly lower growth rates.
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Affiliation(s)
- Ali Navid
- Physics and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550 USA
| | - Yongqin Jiao
- Physics and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550 USA
| | - Sergio Ernesto Wong
- Physics and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550 USA
| | - Jennifer Pett-Ridge
- Physics and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550 USA
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10
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Vijayakumar S, Conway M, Lió P, Angione C. Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives. Methods Mol Biol 2018; 1716:389-408. [PMID: 29222764 DOI: 10.1007/978-1-4939-7528-0_18] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Genome-scale metabolic models are valuable tools for assessing the metabolic potential of living organisms. Being downstream of gene expression, metabolism is increasingly being used as an indicator of the phenotypic outcome for drugs and therapies. We here present a review of the principal methods used for constraint-based modelling in systems biology, and explore how the integration of multi-omic data can be used to improve phenotypic predictions of genome-scale metabolic models. We believe that the large-scale comparison of the metabolic response of an organism to different environmental conditions will be an important challenge for genome-scale models. Therefore, within the context of multi-omic methods, we describe a tutorial for multi-objective optimization using the metabolic and transcriptomics adaptation estimator (METRADE), implemented in MATLAB. METRADE uses microarray and codon usage data to model bacterial metabolic response to environmental conditions (e.g., antibiotics, temperatures, heat shock). Finally, we discuss key considerations for the integration of multi-omic networks into metabolic models, towards automatically extracting knowledge from such models.
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Affiliation(s)
- Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK
| | - Max Conway
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK.
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11
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Huang Z, Lee DY, Yoon S. Quantitative intracellular flux modeling and applications in biotherapeutic development and production using CHO cell cultures. Biotechnol Bioeng 2017; 114:2717-2728. [DOI: 10.1002/bit.26384] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 06/07/2017] [Accepted: 07/12/2017] [Indexed: 12/23/2022]
Affiliation(s)
- Zhuangrong Huang
- Department of Chemical Engineering, University of Massachusetts Lowell; One University Avenue; Lowell Massachusetts
| | - Dong-Yup Lee
- Department of Chemical and Biomolecular Engineering; National University of Singapore; Singapore Singapore
- Bioprocessing Technology Institute; Agency for Science, Technology and Research (A*STAR); Singapore Singapore
| | - Seongkyu Yoon
- Department of Chemical Engineering, University of Massachusetts Lowell; One University Avenue; Lowell Massachusetts
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12
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Wu HQ, Cheng ML, Lai JM, Wu HH, Chen MC, Liu WH, Wu WH, Chang PMH, Huang CYF, Tsou AP, Shiao MS, Wang FS. Flux balance analysis predicts Warburg-like effects of mouse hepatocyte deficient in miR-122a. PLoS Comput Biol 2017; 13:e1005618. [PMID: 28686599 PMCID: PMC5536358 DOI: 10.1371/journal.pcbi.1005618] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Revised: 07/31/2017] [Accepted: 06/12/2017] [Indexed: 12/31/2022] Open
Abstract
The liver is a vital organ involving in various major metabolic functions in human body. MicroRNA-122 (miR-122) plays an important role in the regulation of liver metabolism, but its intrinsic physiological functions require further clarification. This study integrated the genome-scale metabolic model of hepatocytes and mouse experimental data with germline deletion of Mir122a (Mir122a–/–) to infer Warburg-like effects. Elevated expression of MiR-122a target genes in Mir122a–/–mice, especially those encoding for metabolic enzymes, was applied to analyze the flux distributions of the genome-scale metabolic model in normal and deficient states. By definition of the similarity ratio, we compared the flux fold change of the genome-scale metabolic model computational results and metabolomic profiling data measured through a liquid-chromatography with mass spectrometer, respectively, for hepatocytes of 2-month-old mice in normal and deficient states. The Ddc gene demonstrated the highest similarity ratio of 95% to the biological hypothesis of the Warburg effect, and similarity of 75% to the experimental observation. We also used 2, 6, and 11 months of mir-122 knockout mice liver cell to examined the expression pattern of DDC in the knockout mice livers to show upregulated profiles of DDC from the data. Furthermore, through a bioinformatics (LINCS program) prediction, BTK inhibitors and withaferin A could downregulate DDC expression, suggesting that such drugs could potentially alter the early events of metabolomics of liver cancer cells. For almost a century, researchers have known that cancer cells have an abnormal metabolism and utilize glucose differently than normal cells do. Aerobic glycolysis or the Warburg effect in cancer cells involves elevated glucose uptake with lactic acid production in the presence of oxygen. MicroRNAs have recently been discovered to be key metabolic regulators that mediate the fine tuning of genes that are involved directly or indirectly in cancer metabolism. MicroRNA-122 (miR-122) plays an important role in the regulation of liver metabolism, but its intrinsic physiological functions require further clarification. This study integrated the genome-scale metabolic modeling (GSMM) of hepatocytes and mouse experimental data with germline deletion of Mir122a (Mir122a–/–) to infer Warburg-like effects. In silico and in vivo observations indicated that DDC overexpression induced Warburg effect in hepatocyte. Furthermore, through a bioinformatics prediction, BTK inhibitors and withaferin A could downregulate DDC expression, suggesting that such drugs could potentially alter the early events of metabolomics of liver cancer cells.
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Affiliation(s)
- Hua-Qing Wu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Mei-Ling Cheng
- Department of Biomedical Sciences, Chang Gung University, Tao-Yuan, Taiwan
- Metabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Tao-Yuan, Taiwan
- Clinical Phenome Center, Chang Gung Memorial Hospital, Linkou, Tao-Yuan, Taiwan
| | - Jin-Mei Lai
- Department of Life Science, Fu-Jen Catholic University, New Taipei City, Taiwan
| | - Hsuan-Hui Wu
- Institute of Biopharmaceutical Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Meng-Chun Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Wen-Huan Liu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Wu-Hsiung Wu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Peter Mu-Hsin Chang
- Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming University, Taipei, Taiwan
| | - Chi-Ying F. Huang
- Institute of Biopharmaceutical Sciences, National Yang-Ming University, Taipei, Taiwan
- Department of Biotechnology and Laboratory Science in Medicine, National Yang-Ming University, Taipei, Taiwan
- Department of Biochemistry, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ann-Ping Tsou
- Department of Biotechnology and Laboratory Science in Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Ming-Shi Shiao
- Department of Biomedical Sciences, Chang Gung University, Tao-Yuan, Taiwan
- * E-mail: (MSS); (FSW)
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
- * E-mail: (MSS); (FSW)
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Achreja A, Zhao H, Yang L, Yun TH, Marini J, Nagrath D. Exo-MFA - A 13C metabolic flux analysis framework to dissect tumor microenvironment-secreted exosome contributions towards cancer cell metabolism. Metab Eng 2017; 43:156-172. [PMID: 28087332 DOI: 10.1016/j.ymben.2017.01.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 12/05/2016] [Accepted: 01/05/2017] [Indexed: 02/04/2023]
Abstract
Dissecting the pleiotropic roles of tumor micro-environment (TME) on cancer progression has been brought to the foreground of research on cancer pathology. Extracellular vesicles such as exosomes, transport proteins, lipids, and nucleic acids, to mediate intercellular communication between TME components and have emerged as candidates for anti-cancer therapy. We previously reported that cancer-associated fibroblast (CAF) derived exosomes (CDEs) contain metabolites in their cargo that are utilized by cancer cells for central carbon metabolism and promote cancer growth. However, the metabolic fluxes involved in donor cells towards packaging of metabolites in extracellular vesicles and exosome-mediated metabolite flux upregulation in recipient cells are still not known. Here, we have developed a novel empirical and computational technique, exosome-mediated metabolic flux analysis (Exo-MFA) to quantify flow of cargo from source cells to recipient cells via vesicular transport. Our algorithm, which is based on 13C metabolic flux analysis, successfully predicts packaging fluxes to metabolite cargo in CAFs, dynamic changes in rate of exosome internalization by cancer cells, and flux of cargo release over time. We find that cancer cells internalize exosomes rapidly leading to depletion of extracellular exosomes within 24h. However, metabolite cargo significantly alters intracellular metabolism over the course of 24h by regulating glycolysis pathway fluxes via lactate supply. Furthermore, it can supply up to 35% of the TCA cycle fluxes by providing TCA intermediates and glutamine. Our algorithm will help gain insight into (i) metabolic interactions in multicellular systems (ii) biogenesis of extracellular vesicles and their differential packaging of cargo under changing environments, and (iii) regulation of cancer cell metabolism by its microenvironment.
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Affiliation(s)
- Abhinav Achreja
- Laboratory for Systems Biology of Human Diseases, Rice University, Houston, TX 77005, USA; Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hongyun Zhao
- Laboratory for Systems Biology of Human Diseases, Rice University, Houston, TX 77005, USA; Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lifeng Yang
- Laboratory for Systems Biology of Human Diseases, Rice University, Houston, TX 77005, USA; Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA
| | - Tae Hyun Yun
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA
| | | | - Deepak Nagrath
- Laboratory for Systems Biology of Human Diseases, Rice University, Houston, TX 77005, USA; Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA; Department of Bioengineering, Rice University, Houston, TX 77005, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA.
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14
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Zhao Q, Stettner AI, Reznik E, Paschalidis IC, Segrè D. Mapping the landscape of metabolic goals of a cell. Genome Biol 2016; 17:109. [PMID: 27215445 PMCID: PMC4878026 DOI: 10.1186/s13059-016-0968-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Accepted: 04/27/2016] [Indexed: 12/26/2022] Open
Abstract
Genome-scale flux balance models of metabolism provide testable predictions of all metabolic rates in an organism, by assuming that the cell is optimizing a metabolic goal known as the objective function. We introduce an efficient inverse flux balance analysis (invFBA) approach, based on linear programming duality, to characterize the space of possible objective functions compatible with measured fluxes. After testing our algorithm on simulated E. coli data and time-dependent S. oneidensis fluxes inferred from gene expression, we apply our inverse approach to flux measurements in long-term evolved E. coli strains, revealing objective functions that provide insight into metabolic adaptation trajectories.
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Affiliation(s)
- Qi Zhao
- Department of Electrical and Computer Engineering, and Division of Systems Engineering, Boston University, Boston, MA, 02215, USA
| | - Arion I Stettner
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Ed Reznik
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.,Current address: Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Ioannis Ch Paschalidis
- Department of Electrical and Computer Engineering, and Division of Systems Engineering, Boston University, Boston, MA, 02215, USA.
| | - Daniel Segrè
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA. .,Department of Biology and Bioinformatics Program, Boston University, Boston, MA, 02215, USA.
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15
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Reynoso-Meza G, Sanchis J, Blasco X, García-Nieto S. Physical programming for preference driven evolutionary multi-objective optimization. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2014.07.009] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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16
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Zakrzewski P, Medema MH, Gevorgyan A, Kierzek AM, Breitling R, Takano E. MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models. PLoS One 2012; 7:e51511. [PMID: 23272111 PMCID: PMC3522732 DOI: 10.1371/journal.pone.0051511] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2012] [Accepted: 11/01/2012] [Indexed: 12/02/2022] Open
Abstract
Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads.
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Affiliation(s)
- Piotr Zakrzewski
- Department of Microbial Physiology, University of Groningen, Groningen, The Netherlands
- Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands
| | - Marnix H. Medema
- Department of Microbial Physiology, University of Groningen, Groningen, The Netherlands
- Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands
| | - Albert Gevorgyan
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
| | - Andrzej M. Kierzek
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
| | - Rainer Breitling
- Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands
- Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
- * E-mail: (RB); (ET)
| | - Eriko Takano
- Department of Microbial Physiology, University of Groningen, Groningen, The Netherlands
- * E-mail: (RB); (ET)
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17
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Shen T, Rui B, Zhou H, Zhang X, Yi Y, Wen H, Zheng H, Wu J, Shi Y. Metabolic flux ratio analysis and multi-objective optimization revealed a globally conserved and coordinated metabolic response of E. coli to paraquat-induced oxidative stress. MOLECULAR BIOSYSTEMS 2012; 9:121-32. [PMID: 23128557 DOI: 10.1039/c2mb25285f] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The ability of a microorganism to adapt to changes in the environment, such as in nutrient or oxygen availability, is essential for its competitive fitness and survival. The cellular objective and the strategy of the metabolic response to an extreme environment are therefore of tremendous interest and, thus, have been increasingly explored. However, the cellular objective of the complex regulatory structure of the metabolic changes has not yet been fully elucidated and more details regarding the quantitative behaviour of the metabolic flux redistribution are required to understand the systems-wide biological significance of this response. In this study, the intracellular metabolic flux ratios involved in the central carbon metabolism were determined by fractional (13)C-labeling and metabolic flux ratio analysis (MetaFoR) of the wild-type E. coli strain JM101 at an oxidative environment in a chemostat. We observed a significant increase in the flux through phosphoenolpyruvate carboxykinase (PEPCK), phosphoenolpyruvate carboxylase (PEPC), malic enzyme (MEZ) and serine hydroxymethyltransferase (SHMT). We applied an ε-constraint based multi-objective optimization to investigate the trade-off relationships between the biomass yield and the generation of reductive power using the in silico iJR904 genome-scale model of E. coli K-12. The theoretical metabolic redistribution supports that the trans-hydrogenase pathway should not play a direct role in the defence mounted by E. coli against oxidative stress. The agreement between the measured ratio and the theoretical redistribution established the significance of NADPH synthesis as the goal of the metabolic reprogramming that occurs in response to oxidative stress. Our work presents a framework that combines metabolic flux ratio analysis and multi-objective optimization to investigate the metabolic trade-offs that occur under varied environmental conditions. Our results led to the proposal that the metabolic response of E. coli to paraquat-induced oxidative stress is globally conserved and coordinated.
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Affiliation(s)
- Tie Shen
- School of Life Science and Key Laboratory of Plant Physiology and Development Regulation, Guizhou Province, Guizhou Normal University, 550001, Guiyang, China
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18
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Setting optimal operating conditions for a catalytic reactor for butane oxidation using parametric sensitivity analysis and failure probability indices. J Loss Prev Process Ind 2012. [DOI: 10.1016/j.jlp.2012.06.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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19
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Bellance N, Pabst L, Allen G, Rossignol R, Nagrath D. Oncosecretomics coupled to bioenergetics identifies α-amino adipic acid, isoleucine and GABA as potential biomarkers of cancer: Differential expression of c-Myc, Oct1 and KLF4 coordinates metabolic changes. BIOCHIMICA ET BIOPHYSICA ACTA-BIOENERGETICS 2012; 1817:2060-71. [DOI: 10.1016/j.bbabio.2012.07.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2012] [Revised: 06/23/2012] [Accepted: 07/19/2012] [Indexed: 02/04/2023]
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20
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Caneba CA, Bellance N, Yang L, Pabst L, Nagrath D. Pyruvate uptake is increased in highly invasive ovarian cancer cells under anoikis conditions for anaplerosis, mitochondrial function, and migration. Am J Physiol Endocrinol Metab 2012; 303:E1036-52. [PMID: 22895781 DOI: 10.1152/ajpendo.00151.2012] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Anoikis resistance, or the ability for cells to live detached from the extracellular matrix, is a property of epithelial cancers. The "Warburg effect," or the preference of cancer cells for glycolysis for their energy production even in the presence of oxygen, has been shown to be evident in various tumors. Since a cancer cell's metastatic ability depends on microenvironmental conditions (nutrients, stromal cells, and vascularization) and is highly variable for different organs, their cellular metabolic fluxes and nutrient demand may show considerable differences. Moreover, a cancer cell's metastatic ability, which is dependent on the stage of cancer, may further create metabolic alterations depending on its microenvironment. Although recent studies have aimed to elucidate cancer cell metabolism under detached conditions, the nutrient demand and metabolic activity of cancer cells under nonadherent conditions remain poorly understood. Additionally, less is known about metabolic alterations in ovarian cancer cells with varying invasive capability under anoikis conditions. We hypothesized that the metabolism of highly invasive ovarian cancer cells in detachment would differ from less invasive ovarian cancer cells and that ovarian cancer cells will have altered metabolism in detached vs. attached conditions. To assess these metabolic differences, we integrated a secretomics-based metabolic footprinting (MFP) approach with mitochondrial bioenergetics. Interestingly, MFP revealed higher pyruvate uptake and oxygen consumption in more invasive ovarian cancer cells than their less invasive counterparts. Furthermore, ATP production was higher in more invasive vs. less invasive ovarian cancer cells in detachment. We found that pyruvate has an effect on highly invasive ovarian cancer cells' migration ability. Our results are the first to demonstrate that higher mitochondrial activity is related to higher ovarian cancer invasiveness under detached conditions. Importantly, our results bring insights regarding the metabolism of cancer cells under nonadherent conditions and could lead to the development of therapies for modulating cancer cell invasiveness.
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Affiliation(s)
- Christine A Caneba
- Laboratory for Systems Biology of Human Diseases, Rice University, Houston, Texas 77251-1892, USA
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21
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Flux Analysis of the Trypanosoma brucei Glycolysis Based on a Multiobjective-Criteria Bioinformatic Approach. Adv Bioinformatics 2012; 2012:159423. [PMID: 23097667 PMCID: PMC3477527 DOI: 10.1155/2012/159423] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2012] [Accepted: 07/11/2012] [Indexed: 01/26/2023] Open
Abstract
Trypanosoma brucei is a protozoan parasite of major of interest in discovering new genes for drug targets. This parasite alternates its life cycle between the mammal host(s) (bloodstream form) and the insect vector (procyclic form), with two divergent glucose metabolism amenable to in vitro culture. While the metabolic network of the bloodstream forms has been well characterized, the flux distribution between the different branches of the glucose metabolic network in the procyclic form has not been addressed so far. We present a computational analysis (called Metaboflux) that exploits the metabolic topology of the procyclic form, and allows the incorporation of multipurpose experimental data to increase the biological relevance of the model. The alternatives resulting from the structural complexity of networks are formulated as an optimization problem solved by a metaheuristic where experimental data are modeled in a multiobjective function.
Our results show that the current metabolic model is in agreement with experimental data and confirms the observed high metabolic flexibility of glucose metabolism. In addition, Metaboflux offers a rational explanation for the high flexibility in the ratio between final products from glucose metabolism, thsat is, flux redistribution through the malic enzyme steps.
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22
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Pey J, Rubio A, Theodoropoulos C, Cascante M, Planes FJ. Integrating tracer-based metabolomics data and metabolic fluxes in a linear fashion via Elementary Carbon Modes. Metab Eng 2012; 14:344-53. [DOI: 10.1016/j.ymben.2012.03.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2011] [Revised: 03/01/2012] [Accepted: 03/26/2012] [Indexed: 01/10/2023]
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23
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Dan A, Maria G. Pareto Optimal Operating Solutions for a Semibatch Reactor Based on Failure Probability Indices. Chem Eng Technol 2012. [DOI: 10.1002/ceat.201100706] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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24
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Orman MA, Berthiaume F, Androulakis IP, Ierapetritou MG. Advanced stoichiometric analysis of metabolic networks of mammalian systems. Crit Rev Biomed Eng 2012; 39:511-34. [PMID: 22196224 DOI: 10.1615/critrevbiomedeng.v39.i6.30] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Metabolic engineering tools have been widely applied to living organisms to gain a comprehensive understanding about cellular networks and to improve cellular properties. Metabolic flux analysis (MFA), flux balance analysis (FBA), and metabolic pathway analysis (MPA) are among the most popular tools in stoichiometric network analysis. Although application of these tools into well-known microbial systems is extensive in the literature, various barriers prevent them from being utilized in mammalian cells. Limited experimental data, complex regulatory mechanisms, and the requirement of more complex nutrient media are some major obstacles in mammalian cell systems. However, mammalian cells have been used to produce therapeutic proteins, to characterize disease states or related abnormal metabolic conditions, and to analyze the toxicological effects of some medicinally important drugs. Therefore, there is a growing need for extending metabolic engineering principles to mammalian cells in order to understand their underlying metabolic functions. In this review article, advanced metabolic engineering tools developed for stoichiometric analysis including MFA, FBA, and MPA are described. Applications of these tools in mammalian cells are discussed in detail, and the challenges and opportunities are highlighted.
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Affiliation(s)
- Mehmet A Orman
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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25
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Orman MA, Mattick J, Androulakis IP, Berthiaume F, Ierapetritou MG. Stoichiometry based steady-state hepatic flux analysis: computational and experimental aspects. Metabolites 2012; 2:268-91. [PMID: 24957379 PMCID: PMC3901202 DOI: 10.3390/metabo2010268] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2012] [Revised: 03/05/2012] [Accepted: 03/06/2012] [Indexed: 11/16/2022] Open
Abstract
: The liver has many complex physiological functions, including lipid, protein and carbohydrate metabolism, as well as bile and urea production. It detoxifies toxic substances and medicinal products. It also plays a key role in the onset and maintenance of abnormal metabolic patterns associated with various disease states, such as burns, infections and major traumas. Liver cells have been commonly used in in vitro experiments to elucidate the toxic effects of drugs and metabolic changes caused by aberrant metabolic conditions, and to improve the functions of existing systems, such as bioartificial liver. More recently, isolated liver perfusion systems have been increasingly used to characterize intrinsic metabolic changes in the liver caused by various perturbations, including systemic injury, hepatotoxin exposure and warm ischemia. Metabolic engineering tools have been widely applied to these systems to identify metabolic flux distributions using metabolic flux analysis or flux balance analysis and to characterize the topology of the networks using metabolic pathway analysis. In this context, hepatic metabolic models, together with experimental methodologies where hepatocytes or perfused livers are mainly investigated, are described in detail in this review. The challenges and opportunities are also discussed extensively.
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Affiliation(s)
- Mehmet A Orman
- Department of Chemical and Biochemical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - John Mattick
- Department of Chemical and Biochemical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ioannis P Androulakis
- Department of Chemical and Biochemical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Francois Berthiaume
- Department of Biomedical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Marianthi G Ierapetritou
- Department of Chemical and Biochemical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
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Avila-Elchiver M, Nagrath D, Yarmush ML. Optimality and thermodynamics determine the evolution of transcriptional regulatory networks. MOLECULAR BIOSYSTEMS 2011; 8:511-530. [PMID: 22076617 DOI: 10.1039/c1mb05177f] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Transcriptional motifs are small regulatory interaction patterns that regulate biological functions in highly-interacting cellular networks. Recently, attempts have been made to explain the significance of transcriptional motifs through dynamic function. However, fundamental questions remain unanswered. Why are certain transcriptional motifs with similar dynamic function abundant while others occur rarely? What are the criteria for topological generalization of these motifs into complex networks? Here, we present a novel paradigm that combines non-equilibrium thermodynamics with multiobjective-optimality for network analysis. We found that energetic cost, defined herein as specific dissipation energy, is minimal at the optimal environmental conditions and it correlates inversely with the abundance of the network motifs obtained experimentally for E. coli and S. cerevisiae. This yields evidence that dissipative energetics is the underlying criteria used during evolution for motif selection and that biological systems during transcription tend towards evolutionary selection of subgraphs which produces minimum specific heat dissipation under optimal conditions, thereby explaining the abundance/rare occurrence of some motifs. We show that although certain motifs had similar dynamical functionality, they had significantly different energetic cost, thus explaining the abundance/rare occurrence of these motifs. The presented insights may establish global thermodynamic analysis as a backbone in designing and understanding complex networks systems, such as metabolic and protein interaction networks.
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Affiliation(s)
- Marco Avila-Elchiver
- Massachusetts General Hospital and the Harvard Medical School, Shriners Hospitals for Children, 51 Blossom Street, Boston, MA 02114
| | - Deepak Nagrath
- Chemical and Biomolecular Engineering Department, Rice University, Houston, TX 77005.
| | - Martin L Yarmush
- Massachusetts General Hospital and the Harvard Medical School, Shriners Hospitals for Children, 51 Blossom Street, Boston, MA 02114
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27
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Nagrath D, Caneba C, Karedath T, Bellance N. Metabolomics for mitochondrial and cancer studies. BIOCHIMICA ET BIOPHYSICA ACTA-BIOENERGETICS 2011; 1807:650-63. [DOI: 10.1016/j.bbabio.2011.03.006] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2010] [Revised: 02/18/2011] [Accepted: 03/14/2011] [Indexed: 01/29/2023]
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28
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Sharma NS, Nagrath D, Yarmush ML. Metabolic profiling based quantitative evaluation of hepatocellular metabolism in presence of adipocyte derived extracellular matrix. PLoS One 2011; 6:e20137. [PMID: 21603575 PMCID: PMC3095641 DOI: 10.1371/journal.pone.0020137] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2011] [Accepted: 04/26/2011] [Indexed: 12/05/2022] Open
Abstract
The elucidation of the effect of extracellular matrices on hepatocellular metabolism is critical to understand the mechanism of functional upregulation. We have developed a system using natural extracellular matrices [Adipogel] for enhanced albumin synthesis of rat hepatocyte cultures for a period of 10 days as compared to collagen sandwich cultures. Primary rat hepatocytes isolated from livers of female Lewis rats recover within 4 days of culture from isolation induced injury while function is stabilized at 7 days post-isolation. Thus, the culture period can be classified into three distinct stages viz. recovery stage [day 0–4], pre-stable stage [day 5–7] and the stable stage [day 8–10]. A Metabolic Flux Analysis of primary rat hepatocytes cultured in Adipogel was performed to identify the key metabolic pathways modulated as compared to collagen sandwich cultures. In the recovery stage [day 4], the collagen-soluble Adipogel cultures shows an increase in TriCarboxylic Acid [TCA] cycle fluxes; in the pre-stable stage [day 7], there is an increase in PPP and TCA cycle fluxes while in the stable stage [day 10], there is a significant increase in TCA cycle, urea cycle fluxes and amino acid uptake rates concomitant with increased albumin synthesis rate as compared to collagen sandwich cultures throughout the culture period. Metabolic analysis of the collagen-soluble Adipogel condition reveals significantly higher transamination reaction fluxes, amino acid uptake and albumin synthesis rates for the stable vs. recovery stages of culture. The identification of metabolic pathways modulated for hepatocyte cultures in presence of Adipogel will be a useful step to develop an optimization algorithm to further improve hepatocyte function for Bioartificial Liver Devices. The development of this framework for upregulating hepatocyte function in Bioartificial Liver Devices will facilitate the utilization of an integrated experimental and computational approach for broader applications of Adipogel in tissue e engineering and regenerative medicine.
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Affiliation(s)
- Nripen S. Sharma
- Center for Engineering in Medicine/Surgical Services, Massachusetts General Hospital, Harvard Medical School, and The Shriners Hospitals for Children, Boston, Massachusetts, United States of America
| | - Deepak Nagrath
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas, United States of America
| | - Martin L. Yarmush
- Center for Engineering in Medicine/Surgical Services, Massachusetts General Hospital, Harvard Medical School, and The Shriners Hospitals for Children, Boston, Massachusetts, United States of America
- * E-mail:
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