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Bai W, Huang M, Li C, Li J. The biological principles and advanced applications of DSB repair in CRISPR-mediated yeast genome editing. Synth Syst Biotechnol 2023; 8:584-596. [PMID: 37711546 PMCID: PMC10497738 DOI: 10.1016/j.synbio.2023.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 09/16/2023] Open
Abstract
To improve the performance of yeast cell factories for industrial production, extensive CRISPR-mediated genome editing systems have been applied by artificially creating double-strand breaks (DSBs) to introduce mutations with the assistance of intracellular DSB repair. Diverse strategies of DSB repair are required to meet various demands, including precise editing or random editing with customized gRNAs or a gRNA library. Although most yeasts remodeling techniques have shown rewarding performance in laboratory verification, industrial yeast strain manipulation relies only on very limited strategies. Here, we comprehensively reviewed the molecular mechanisms underlying recent industrial applications to provide new insights into DSB cleavage and repair pathways in both Saccharomyces cerevisiae and other unconventional yeast species. The discussion of DSB repair covers the most frequently used homologous recombination (HR) and nonhomologous end joining (NHEJ) strategies to the less well-studied illegitimate recombination (IR) pathways, such as single-strand annealing (SSA) and microhomology-mediated end joining (MMEJ). Various CRISPR-based genome editing tools and corresponding gene editing efficiencies are described. Finally, we summarize recently developed CRISPR-based strategies that use optimized DSB repair for genome-scale editing, providing a direction for further development of yeast genome editing.
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Affiliation(s)
- Wenxin Bai
- Institute of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, 100081, Beijing, PR China
- The BIT-QUB International Joint Laboratory in Synthetic Biology, Beijing, 100081, PR China
| | - Meilan Huang
- School of Chemistry and Chemical Engineering, David Keir Building, Queen's University Belfast, Stranmillis Road, Northern Ireland, BT9 5AG, Belfast, United Kingdom
- The BIT-QUB International Joint Laboratory in Synthetic Biology, Beijing, 100081, PR China
| | - Chun Li
- Institute of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, 100081, Beijing, PR China
- Key Lab for Industrial Biocatalysis, Ministry of Education, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, PR China
| | - Jun Li
- Institute of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, 100081, Beijing, PR China
- The BIT-QUB International Joint Laboratory in Synthetic Biology, Beijing, 100081, PR China
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2
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Gawthrop PJ, Pan M. Sensitivity analysis of biochemical systems using bond graphs. J R Soc Interface 2023; 20:20230192. [PMID: 37464805 DOI: 10.1098/rsif.2023.0192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/22/2023] [Indexed: 07/20/2023] Open
Abstract
The sensitivity of systems biology models to parameter variation can give insights into which parameters are most important for physiological function, and also direct efforts to estimate parameters. However, in general, kinetic models of biochemical systems do not remain thermodynamically consistent after perturbing parameters. To address this issue, we analyse the sensitivity of biological reaction networks in the context of a bond graph representation. We find that the parameter sensitivities can themselves be represented as bond graph components, mirroring potential mechanisms for controlling biochemistry. In particular, a sensitivity system is derived which re-expresses parameter variation as additional system inputs. The sensitivity system is then linearized with respect to these new inputs to derive a linear system which can be used to give local sensitivity to parameters in terms of linear system properties such as gain and time constant. This linear system can also be used to find so-called sloppy parameters in biological models. We verify our approach using a model of the Pentose Phosphate Pathway, confirming the reactions and metabolites most essential to maintaining the function of the pathway.
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Affiliation(s)
- Peter J Gawthrop
- Department of Biomedical Engineering, Faculty of Engineering & Information Technology, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Michael Pan
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, Victoria 3010, Australia
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Shaping and Dilating the Fitness Landscape for Parameter Estimation in Stochastic Biochemical Models. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The parameter estimation (PE) of biochemical reactions is one of the most challenging tasks in systems biology given the pivotal role of these kinetic constants in driving the behavior of biochemical systems. PE is a non-convex, multi-modal, and non-separable optimization problem with an unknown fitness landscape; moreover, the quantities of the biochemical species appearing in the system can be low, making biological noise a non-negligible phenomenon and mandating the use of stochastic simulation. Finally, the values of the kinetic parameters typically follow a log-uniform distribution; thus, the optimal solutions are situated in the lowest orders of magnitude of the search space. In this work, we further elaborate on a novel approach to address the PE problem based on a combination of adaptive swarm intelligence and dilation functions (DFs). DFs require prior knowledge of the characteristics of the fitness landscape; therefore, we leverage an alternative solution to evolve optimal DFs. On top of this approach, we introduce surrogate Fourier modeling to simplify the PE, by producing a smoother version of the fitness landscape that excludes the high frequency components of the fitness function. Our results show that the PE exploiting evolved DFs has a performance comparable with that of the PE run with a custom DF. Moreover, surrogate Fourier modeling allows for improving the convergence speed. Finally, we discuss some open problems related to the scalability of our methodology.
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Salahshouri P, Emadi-Baygi M, Jalili M, Khan FM, Wolkenhauer O, Salehzadeh-Yazdi A. A Metabolic Model of Intestinal Secretions: The Link between Human Microbiota and Colorectal Cancer Progression. Metabolites 2021; 11:metabo11070456. [PMID: 34357350 PMCID: PMC8303431 DOI: 10.3390/metabo11070456] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 07/12/2021] [Accepted: 07/13/2021] [Indexed: 12/22/2022] Open
Abstract
The human gut microbiota plays a dual key role in maintaining human health or inducing disorders, for example, obesity, type 2 diabetes, and cancers such as colorectal cancer (CRC). High-throughput data analysis, such as metagenomics and metabolomics, have shown the diverse effects of alterations in dynamic bacterial populations on the initiation and progression of colorectal cancer. However, it is well established that microbiome and human cells constantly influence each other, so it is not appropriate to study them independently. Genome-scale metabolic modeling is a well-established mathematical framework that describes the dynamic behavior of these two axes at the system level. In this study, we created community microbiome models of three conditions during colorectal cancer progression, including carcinoma, adenoma and health status, and showed how changes in the microbial population influence intestinal secretions. Conclusively, our findings showed that alterations in the gut microbiome might provoke mutations and transform adenomas into carcinomas. These alterations include the secretion of mutagenic metabolites such as H2S, NO compounds, spermidine and TMA (trimethylamine), as well as the reduction of butyrate. Furthermore, we found that the colorectal cancer microbiome can promote inflammation, cancer progression (e.g., angiogenesis) and cancer prevention (e.g., apoptosis) by increasing and decreasing certain metabolites such as histamine, glutamine and pyruvate. Thus, modulating the gut microbiome could be a promising strategy for the prevention and treatment of CRC.
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Affiliation(s)
- Pejman Salahshouri
- Department of Genetics, Faculty of Basic Sciences, Shahrekord University, Shahrekord 8818634141, Iran; (P.S.); (M.E.-B.)
| | - Modjtaba Emadi-Baygi
- Department of Genetics, Faculty of Basic Sciences, Shahrekord University, Shahrekord 8818634141, Iran; (P.S.); (M.E.-B.)
- Biotechnology Research Institute, Shahrekord University, Shahrekord 8818634141, Iran
| | - Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran 14114, Iran;
| | - Faiz M. Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany; (F.M.K.); (O.W.)
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany; (F.M.K.); (O.W.)
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany; (F.M.K.); (O.W.)
- Correspondence:
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Nobile MS, Coelho V, Pescini D, Damiani C. Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models. BMC Bioinformatics 2021; 22:78. [PMID: 33902438 PMCID: PMC8074438 DOI: 10.1186/s12859-021-04002-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 02/07/2021] [Indexed: 01/20/2023] Open
Abstract
Background Genome-wide reconstructions of metabolism opened the way to thorough investigations of cell metabolism for health care and industrial purposes. However, the predictions offered by Flux Balance Analysis (FBA) can be strongly affected by the choice of flux boundaries, with particular regard to the flux of reactions that sink nutrients into the system. To mitigate possible errors introduced by a poor selection of such boundaries, a rational approach suggests to focus the modeling efforts on the pivotal ones. Methods In this work, we present a methodology for the automatic identification of the key fluxes in genome-wide constraint-based models, by means of variance-based sensitivity analysis. The goal is to identify the parameters for which a small perturbation entails a large variation of the model outcomes, also referred to as sensitive parameters. Due to the high number of FBA simulations that are necessary to assess sensitivity coefficients on genome-wide models, our method exploits a master-slave methodology that distributes the computation on massively multi-core architectures. We performed the following steps: (1) we determined the putative parameterizations of the genome-wide metabolic constraint-based model, using Saltelli’s method; (2) we applied FBA to each parameterized model, distributing the massive amount of calculations over multiple nodes by means of MPI; (3) we then recollected and exploited the results of all FBA runs to assess a global sensitivity analysis. Results We show a proof-of-concept of our approach on latest genome-wide reconstructions of human metabolism Recon2.2 and Recon3D. We report that most sensitive parameters are mainly associated with the intake of essential amino acids in Recon2.2, whereas in Recon 3D they are associated largely with phospholipids. We also illustrate that in most cases there is a significant contribution of higher order effects. Conclusion Our results indicate that interaction effects between different model parameters exist, which should be taken into account especially at the stage of calibration of genome-wide models, supporting the importance of a global strategy of sensitivity analysis. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04002-0.
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Affiliation(s)
- Marco S Nobile
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.,SYSBIO/ISBE.IT Centre for Systems Biology, Milan, Italy.,Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Vasco Coelho
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | - Dario Pescini
- Department of Statistics and Quantiative Methods, University of Milano-Bicocca, Milan, Italy.,SYSBIO/ISBE.IT Centre for Systems Biology, Milan, Italy
| | - Chiara Damiani
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy. .,SYSBIO/ISBE.IT Centre for Systems Biology, Milan, Italy.
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Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches. Processes (Basel) 2021. [DOI: 10.3390/pr9020322] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.
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Machicao J, Craighero F, Maspero D, Angaroni F, Damiani C, Graudenzi A, Antoniotti M, Bruno OM. On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples. Curr Genomics 2021; 22:88-97. [PMID: 34220296 PMCID: PMC8188584 DOI: 10.2174/1389202922666210301084151] [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: 07/07/2020] [Revised: 12/16/2020] [Accepted: 12/18/2020] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The increasing availability of omics data collected from patients affected by severe pathologies, such as cancer, is fostering the development of data science methods for their analysis. INTRODUCTION The combination of data integration and machine learning approaches can provide new powerful instruments to tackle the complexity of cancer development and deliver effective diagnostic and prognostic strategies. METHODS We explore the possibility of exploiting the topological properties of sample-specific metabolic networks as features in a supervised classification task. Such networks are obtained by projecting transcriptomic data from RNA-seq experiments on genome-wide metabolic models to define weighted networks modeling the overall metabolic activity of a given sample. RESULTS We show the classification results on a labeled breast cancer dataset from the TCGA database, including 210 samples (cancer vs. normal). In particular, we investigate how the performance is affected by a threshold-based pruning of the networks by comparing Artificial Neural Networks, Support Vector Machines and Random Forests. Interestingly, the best classification performance is achieved within a small threshold range for all methods, suggesting that it might represent an effective choice to recover useful information while filtering out noise from data. Overall, the best accuracy is achieved with SVMs, which exhibit performances similar to those obtained when gene expression profiles are used as features. CONCLUSION These findings demonstrate that the topological properties of sample-specific metabolic networks are effective in classifying cancer and normal samples, suggesting that useful information can be extracted from a relatively limited number of features.
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Affiliation(s)
- Jeaneth Machicao
- Address correspondence to these authors at the São Carlos Institute of Physics, University of São Paulo, São Carlos, Brazil; Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan, Italy E-mails: , ,
| | | | | | | | | | - Alex Graudenzi
- Address correspondence to these authors at the São Carlos Institute of Physics, University of São Paulo, São Carlos, Brazil; Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan, Italy E-mails: , ,
| | | | - Odemir M. Bruno
- Address correspondence to these authors at the São Carlos Institute of Physics, University of São Paulo, São Carlos, Brazil; Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan, Italy E-mails: , ,
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Baloni P, Dinalankara W, Earls JC, Knijnenburg TA, Geman D, Marchionni L, Price ND. Identifying Personalized Metabolic Signatures in Breast Cancer. Metabolites 2020; 11:20. [PMID: 33396819 PMCID: PMC7823382 DOI: 10.3390/metabo11010020] [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: 11/16/2020] [Revised: 12/23/2020] [Accepted: 12/28/2020] [Indexed: 01/04/2023] Open
Abstract
Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.
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Affiliation(s)
- Priyanka Baloni
- Institute for Systems Biology, Seattle, WA 98109, USA; (P.B.); (J.C.E.); (T.A.K.)
| | - Wikum Dinalankara
- Department of Oncology, Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - John C. Earls
- Institute for Systems Biology, Seattle, WA 98109, USA; (P.B.); (J.C.E.); (T.A.K.)
| | - Theo A. Knijnenburg
- Institute for Systems Biology, Seattle, WA 98109, USA; (P.B.); (J.C.E.); (T.A.K.)
| | - Donald Geman
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21205, USA;
| | - Luigi Marchionni
- Department of Oncology, Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, WA 98109, USA; (P.B.); (J.C.E.); (T.A.K.)
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Aghakhani S, Zerrouk N, Niarakis A. Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches. Cancers (Basel) 2020; 13:cancers13010035. [PMID: 33374292 PMCID: PMC7795338 DOI: 10.3390/cancers13010035] [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: 10/31/2020] [Revised: 12/12/2020] [Accepted: 12/17/2020] [Indexed: 12/29/2022] Open
Abstract
Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.
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Affiliation(s)
- Sahar Aghakhani
- GenHotel, University of Evry, University of Paris-Saclay, Genopole, 91000 Evry, France; (S.A.); (N.Z.)
- Lifeware Group, Inria Saclay, 91120 Palaiseau, France
| | - Naouel Zerrouk
- GenHotel, University of Evry, University of Paris-Saclay, Genopole, 91000 Evry, France; (S.A.); (N.Z.)
| | - Anna Niarakis
- GenHotel, University of Evry, University of Paris-Saclay, Genopole, 91000 Evry, France; (S.A.); (N.Z.)
- Lifeware Group, Inria Saclay, 91120 Palaiseau, France
- Correspondence:
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10
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Reggiani F, Carraro M, Belligoli A, Sanna M, dal Prà C, Favaretto F, Ferrari C, Vettor R, Tosatto SCE. In silico prediction of blood cholesterol levels from genotype data. PLoS One 2020; 15:e0227191. [PMID: 32040480 PMCID: PMC7010235 DOI: 10.1371/journal.pone.0227191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 11/14/2019] [Indexed: 11/18/2022] Open
Abstract
In this work we present a framework for blood cholesterol levels prediction from genotype data. The predictor is based on an algorithm for cholesterol metabolism simulation available in literature, implemented and optimized by our group in the R language. The main weakness of the former simulation algorithm was the need of experimental data to simulate mutations in genes altering the cholesterol metabolism. This caveat strongly limited the application of the model in the clinical practice. In this work we present how this limitation could be bypassed thanks to an optimization of model parameters based on patient cholesterol levels retrieved from literature. Prediction performance has been assessed taking into consideration several scoring indices currently used for performance evaluation of machine learning methods. Our assessment shows how the optimization phase improved model performance, compared to the original version available in literature.
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Affiliation(s)
- Francesco Reggiani
- Department of Biomedical Sciences, University of Padua, Padua, Italy
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Marco Carraro
- Department of Biomedical Sciences, University of Padua, Padua, Italy
| | - Anna Belligoli
- Clinica Medica 3, Department of Medicine—DIMED, School of Medicine, University of Padua, Padua, Italy
| | - Marta Sanna
- Clinica Medica 3, Department of Medicine—DIMED, School of Medicine, University of Padua, Padua, Italy
| | - Chiara dal Prà
- Clinica Medica 3, Department of Medicine—DIMED, School of Medicine, University of Padua, Padua, Italy
| | - Francesca Favaretto
- Clinica Medica 3, Department of Medicine—DIMED, School of Medicine, University of Padua, Padua, Italy
| | - Carlo Ferrari
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Roberto Vettor
- Clinica Medica 3, Department of Medicine—DIMED, School of Medicine, University of Padua, Padua, Italy
| | - Silvio C. E. Tosatto
- Department of Biomedical Sciences, University of Padua, Padua, Italy
- CNR Institute of Neuroscience, Padua, Italy
- * E-mail:
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11
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Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer. Br J Cancer 2019; 122:233-244. [PMID: 31819186 PMCID: PMC7052204 DOI: 10.1038/s41416-019-0659-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 11/05/2019] [Accepted: 11/06/2019] [Indexed: 12/17/2022] Open
Abstract
Background Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation. Methods We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer. Results We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. Model simulations predicted tumour-specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumour cell killing. Conclusions Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer. We propose that modelling proteomics data from human HCC with our approach will enable an individualised metabolic profiling of tumours and predictions of the efficacy of drug therapies targeting specific metabolic pathways.
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Tangherloni A, Spolaor S, Cazzaniga P, Besozzi D, Rundo L, Mauri G, Nobile MS. Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105494] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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13
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Graudenzi A, Maspero D, Di Filippo M, Gnugnoli M, Isella C, Mauri G, Medico E, Antoniotti M, Damiani C. Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power. J Biomed Inform 2018; 87:37-49. [PMID: 30244122 DOI: 10.1016/j.jbi.2018.09.010] [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] [Received: 07/09/2018] [Revised: 09/07/2018] [Accepted: 09/14/2018] [Indexed: 12/20/2022]
Abstract
Effective stratification of cancer patients on the basis of their molecular make-up is a key open challenge. Given the altered and heterogenous nature of cancer metabolism, we here propose to use the overall expression of central carbon metabolism as biomarker to characterize groups of patients with important characteristics, such as response to ad-hoc therapeutic strategies and survival expectancy. To this end, we here introduce the data integration framework named Metabolic Reaction Enrichment Analysis (MaREA), which strives to characterize the metabolic deregulations that distinguish cancer phenotypes, by projecting RNA-seq data onto metabolic networks, without requiring metabolic measurements. MaREA computes a score for each network reaction, based on the expression of the set of genes encoding for the associated enzyme(s). The scores are first used as features for cluster analysis and then to rank and visualize in an organized fashion the metabolic deregulations that distinguish cancer sub-types. We applied our method to recent lung and breast cancer RNA-seq datasets from The Cancer Genome Atlas and we were able to identify subgroups of patients with significant differences in survival expectancy. We show how the prognostic power of MaREA improves when an extracted and further curated core model focusing on central carbon metabolism is used rather than the genome-wide reference network. The visualization of the metabolic differences between the groups with best and worst prognosis allowed to identify and analyze key metabolic properties related to cancer aggressiveness. Some of these properties are shared across different cancer (sub) types, e.g., the up-regulation of nucleic acid and amino acid synthesis, whereas some other appear to be tumor-specific, such as the up- or down-regulation of the phosphoenolpyruvate carboxykinase reaction, which display different patterns in distinct tumor (sub)types. These results might be soon employed to deliver highly automated diagnostic and prognostic strategies for cancer patients.
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Affiliation(s)
- Alex Graudenzi
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy
| | - Davide Maspero
- Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy
| | - Marzia Di Filippo
- Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy; SYSBIO Centre of Systems Biology, University Milano-Bicocca, Milan, Italy
| | - Marco Gnugnoli
- Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy; SYSBIO Centre of Systems Biology, University Milano-Bicocca, Milan, Italy
| | - Claudio Isella
- University of Torino, Department of Oncology, Candiolo, Torino, Italy; Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Torino, Italy
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy; SYSBIO Centre of Systems Biology, University Milano-Bicocca, Milan, Italy
| | - Enzo Medico
- University of Torino, Department of Oncology, Candiolo, Torino, Italy; Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Torino, Italy
| | - Marco Antoniotti
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy; Milan Center for Neuroscience, University of Milan-Bicocca, Monza, Italy
| | - Chiara Damiani
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy; SYSBIO Centre of Systems Biology, University Milano-Bicocca, Milan, Italy.
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14
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Colombo R, Damiani C, Gilbert D, Heiner M, Mauri G, Pescini D. Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes. BMC Bioinformatics 2018; 19:251. [PMID: 30066662 PMCID: PMC6201900 DOI: 10.1186/s12859-018-2181-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background Determining the value of kinetic constants for a metabolic system in the exact
physiological conditions is an extremely hard task. However, this kind of
information is of pivotal relevance to effectively simulate a biological
phenomenon as complex as metabolism. Results To overcome this issue, we propose to investigate emerging properties of
ensembles of sets of kinetic constants leading to the biological readout observed
in different experimental conditions. To this aim, we exploit information
retrievable from constraint-based analyses (i.e. metabolic flux distributions at
steady state) with the goal to generate feasible values for kinetic constants
exploiting the mass action law. The sets retrieved from the previous step will be
used to parametrize a mechanistic model whose simulation will be performed to
reconstruct the dynamics of the system (until reaching the metabolic steady state)
for each experimental condition. Every parametrization that is in accordance with
the expected metabolic phenotype is collected in an ensemble whose features are
analyzed to determine the emergence of properties of a phenotype. In this work we
apply the proposed approach to identify ensembles of kinetic parameters for five
metabolic phenotypes of E. Coli, by analyzing
five different experimental conditions associated with the ECC2comp model recently
published by Hädicke and collaborators. Conclusions Our results suggest that the parameter values of just few reactions are
responsible for the emergence of a metabolic phenotype. Notably, in contrast with
constraint-based approaches such as Flux Balance Analysis, the methodology used in
this paper does not require to assume that metabolism is optimizing towards a
specific goal. Electronic supplementary material The online version of this article (10.1186/s12859-018-2181-7) contains supplementary material, which is available to authorized
users.
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Affiliation(s)
- Riccardo Colombo
- Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy. .,SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy.
| | - Chiara Damiani
- Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy.,SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy
| | - David Gilbert
- College of Engineering, Design and Physical Sciences, Brunel University, Middlesex, London, Uxbridg, UB8 3PH, UK
| | - Monika Heiner
- Computer Science Department, Brandenburg University of Technology Cottbus-Senftenberg, Walther-Pauer-Str. 2, Cottbus, D-03046, Germany
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy.,SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy
| | - Dario Pescini
- SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy.,Department of Statistics and Quantitative Methods, University of Milan - Bicocca, Via Bicocca degli Arcimboldi, 8, Milan, 20126, Italy
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15
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Damiani C, Di Filippo M, Pescini D, Maspero D, Colombo R, Mauri G. popFBA: tackling intratumour heterogeneity with Flux Balance Analysis. Bioinformatics 2018; 33:i311-i318. [PMID: 28881985 PMCID: PMC5870635 DOI: 10.1093/bioinformatics/btx251] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Motivation Intratumour heterogeneity poses many challenges to the treatment of cancer. Unfortunately, the transcriptional and metabolic information retrieved by currently available computational and experimental techniques portrays the average behaviour of intermixed and heterogeneous cell subpopulations within a given tumour. Emerging single-cell genomic analyses are nonetheless unable to characterize the interactions among cancer subpopulations. In this study, we propose popFBA, an extension to classic Flux Balance Analysis, to explore how metabolic heterogeneity and cooperation phenomena affect the overall growth of cancer cell populations. Results We show how clones of a metabolic network of human central carbon metabolism, sharing the same stoichiometry and capacity constraints, may follow several different metabolic paths and cooperate to maximize the growth of the total population. We also introduce a method to explore the space of possible interactions, given some constraints on plasma supply of nutrients. We illustrate how alternative nutrients in plasma supply and/or a dishomogeneous distribution of oxygen provision may affect the landscape of heterogeneous phenotypes. We finally provide a technique to identify the most proliferative cells within the heterogeneous population. Availability and implementation the popFBA MATLAB function and the SBML model are available at https://github.com/BIMIB-DISCo/popFBA.
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Affiliation(s)
- Chiara Damiani
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy
| | - Marzia Di Filippo
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Statistics and Quantitative Methods, University Milano-Bicocca, Milan, Italy
| | - Davide Maspero
- Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy
| | - Riccardo Colombo
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy
| | - Giancarlo Mauri
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy
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16
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Tebani A, Afonso C, Bekri S. Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery. J Inherit Metab Dis 2018; 41:393-406. [PMID: 28842777 PMCID: PMC5959951 DOI: 10.1007/s10545-017-0080-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 07/27/2017] [Accepted: 07/28/2017] [Indexed: 12/11/2022]
Abstract
This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.
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Affiliation(s)
- Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Carlos Afonso
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France.
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France.
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17
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Damiani C, Colombo R, Gaglio D, Mastroianni F, Pescini D, Westerhoff HV, Mauri G, Vanoni M, Alberghina L. A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect. PLoS Comput Biol 2017; 13:e1005758. [PMID: 28957320 PMCID: PMC5634631 DOI: 10.1371/journal.pcbi.1005758] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 10/10/2017] [Accepted: 09/01/2017] [Indexed: 01/24/2023] Open
Abstract
Cancer cells share several metabolic traits, including aerobic production of lactate from glucose (Warburg effect), extensive glutamine utilization and impaired mitochondrial electron flow. It is still unclear how these metabolic rearrangements, which may involve different molecular events in different cells, contribute to a selective advantage for cancer cell proliferation. To ascertain which metabolic pathways are used to convert glucose and glutamine to balanced energy and biomass production, we performed systematic constraint-based simulations of a model of human central metabolism. Sampling of the feasible flux space allowed us to obtain a large number of randomly mutated cells simulated at different glutamine and glucose uptake rates. We observed that, in the limited subset of proliferating cells, most displayed fermentation of glucose to lactate in the presence of oxygen. At high utilization rates of glutamine, oxidative utilization of glucose was decreased, while the production of lactate from glutamine was enhanced. This emergent phenotype was observed only when the available carbon exceeded the amount that could be fully oxidized by the available oxygen. Under the latter conditions, standard Flux Balance Analysis indicated that: this metabolic pattern is optimal to maximize biomass and ATP production; it requires the activity of a branched TCA cycle, in which glutamine-dependent reductive carboxylation cooperates to the production of lipids and proteins; it is sustained by a variety of redox-controlled metabolic reactions. In a K-ras transformed cell line we experimentally assessed glutamine-induced metabolic changes. We validated computational results through an extension of Flux Balance Analysis that allows prediction of metabolite variations. Taken together these findings offer new understanding of the logic of the metabolic reprogramming that underlies cancer cell growth. Hallmarks describing common key events in initiation, maintenance and progression of cancer have been identified. One hallmark deals with rewiring of metabolic reactions required to sustain enhanced cell proliferation. The availability of molecular, mechanistic models of cancer hallmarks will mightily improve optimized personal treatment and new drug discovery. Metabolism is the only hallmark for which it is currently possible to derive large scale mathematical models, which have predictive ability. In this paper, we exploit a constraint-based model of the core metabolism required for biomass conversion of the most relevant nutrients—glucose and glutamine—to clarify the logic of control of cancer metabolism. We newly report that, when available oxygen is not sufficient to fully oxidize available glucose and glutamine carbons–a situation compatible with that observed under normal oxygen conditions in human and in cancer cells growing in vitro—utilization of glutamine by reductive carboxylation and conversion of glucose and glutamine to lactate confer advantage for biomass production. Redox homeostasis can be maintained through the use of different alternative pathways. In conclusion, this paper offers a logic interpretation to the link between metabolic rewiring and enhanced proliferation, which may offer new approaches to targeted drug discovery and utilization.
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Affiliation(s)
- Chiara Damiani
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Informatics, Systems and Communication, University Milano-Bicocca, Milano, Italy
| | - Riccardo Colombo
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Informatics, Systems and Communication, University Milano-Bicocca, Milano, Italy
| | - Daniela Gaglio
- SYSBIO Centre of Systems Biology, Milano, Italy
- Institute of Molecular Bioimaging and Physiology, CNR, Segrate, Milan, Italy
| | - Fabrizia Mastroianni
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Biotechnology and Biosciences, University Milano-Bicocca, Milano, Italy
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Statistics and Quantitative Methods, University Milano-Bicocca, Milano, Italy
| | - Hans Victor Westerhoff
- Dept of Molecular Cell Physiology, Faculty of Earth and Life Sciences, VU University, Amsterdam, The Netherlands
- Manchester Centre for Integrative Systems Biology, School of Chemical Engineering and Analytical Science, University of Manchester, Manchester, United Kingdom
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
| | - Giancarlo Mauri
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Informatics, Systems and Communication, University Milano-Bicocca, Milano, Italy
| | - Marco Vanoni
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Biotechnology and Biosciences, University Milano-Bicocca, Milano, Italy
- * E-mail: (LA); (MV)
| | - Lilia Alberghina
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Biotechnology and Biosciences, University Milano-Bicocca, Milano, Italy
- * E-mail: (LA); (MV)
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18
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Roy M, Finley SD. Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer. Front Physiol 2017; 8:217. [PMID: 28446878 PMCID: PMC5388762 DOI: 10.3389/fphys.2017.00217] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 03/27/2017] [Indexed: 12/13/2022] Open
Abstract
Reprogramming of energy metabolism is a hallmark of cancer that enables the cancer cells to meet the increased energetic requirements due to uncontrolled proliferation. One prominent example is pancreatic ductal adenocarcinoma, an aggressive form of cancer with an overall 5-year survival rate of 5%. The reprogramming mechanism in pancreatic cancer involves deregulated uptake of glucose and glutamine and other opportunistic modes of satisfying energetic demands in a hypoxic and nutrient-poor environment. In the current study, we apply systems biology approaches to enable a better understanding of the dynamics of the distinct metabolic alterations in KRAS-mediated pancreatic cancer, with the goal of impeding early cell proliferation by identifying the optimal metabolic enzymes to target. We have constructed a kinetic model of metabolism represented as a set of ordinary differential equations that describe time evolution of the metabolite concentrations in glycolysis, glutaminolysis, tricarboxylic acid cycle and the pentose phosphate pathway. The model is comprised of 46 metabolites and 53 reactions. The mathematical model is fit to published enzyme knockdown experimental data. We then applied the model to perform in silico enzyme modulations and evaluate the effects on cell proliferation. Our work identifies potential combinations of enzyme knockdown, metabolite inhibition, and extracellular conditions that impede cell proliferation. Excitingly, the model predicts novel targets that can be tested experimentally. Therefore, the model is a tool to predict the effects of inhibiting specific metabolic reactions within pancreatic cancer cells, which is difficult to measure experimentally, as well as test further hypotheses toward targeted therapies.
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Affiliation(s)
- Mahua Roy
- Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA
| | - Stacey D Finley
- Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA.,Chemical Engineering, University of Southern CaliforniaLos Angeles, CA, USA
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19
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Tebani A, Abily-Donval L, Afonso C, Marret S, Bekri S. Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era. Int J Mol Sci 2016; 17:ijms17071167. [PMID: 27447622 PMCID: PMC4964538 DOI: 10.3390/ijms17071167] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Revised: 07/12/2016] [Accepted: 07/15/2016] [Indexed: 12/29/2022] Open
Abstract
Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era.
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Affiliation(s)
- Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, Rouen 76031, France.
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
- Normandie Univ, UNIROUEN, INSA Rouen, CNRS, COBRA, Rouen 76000, France.
| | - Lenaig Abily-Donval
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
- Department of Neonatal Pediatrics and Intensive Care, Rouen University Hospital, Rouen 76031, France.
| | - Carlos Afonso
- Normandie Univ, UNIROUEN, INSA Rouen, CNRS, COBRA, Rouen 76000, France.
| | - Stéphane Marret
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
- Department of Neonatal Pediatrics and Intensive Care, Rouen University Hospital, Rouen 76031, France.
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, Rouen University Hospital, Rouen 76031, France.
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
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20
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Pancreatic Beta Cell G-Protein Coupled Receptors and Second Messenger Interactions: A Systems Biology Computational Analysis. PLoS One 2016; 11:e0152869. [PMID: 27138453 PMCID: PMC4854486 DOI: 10.1371/journal.pone.0152869] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 03/21/2016] [Indexed: 12/17/2022] Open
Abstract
Insulin secretory in pancreatic beta-cells responses to nutrient stimuli and hormonal modulators include multiple messengers and signaling pathways with complex interdependencies. Here we present a computational model that incorporates recent data on glucose metabolism, plasma membrane potential, G-protein-coupled-receptors (GPCR), cytoplasmic and endoplasmic reticulum calcium dynamics, cAMP and phospholipase C pathways that regulate interactions between second messengers in pancreatic beta-cells. The values of key model parameters were inferred from published experimental data. The model gives a reasonable fit to important aspects of experimentally measured metabolic and second messenger concentrations and provides a framework for analyzing the role of metabolic, hormones and neurotransmitters changes on insulin secretion. Our analysis of the dynamic data provides support for the hypothesis that activation of Ca2+-dependent adenylyl cyclases play a critical role in modulating the effects of glucagon-like peptide 1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and catecholamines. The regulatory properties of adenylyl cyclase isoforms determine fluctuations in cytoplasmic cAMP concentration and reveal a synergistic action of glucose, GLP-1 and GIP on insulin secretion. On the other hand, the regulatory properties of phospholipase C isoforms determine the interaction of glucose, acetylcholine and free fatty acids (FFA) (that act through the FFA receptors) on insulin secretion. We found that a combination of GPCR agonists activating different messenger pathways can stimulate insulin secretion more effectively than a combination of GPCR agonists for a single pathway. This analysis also suggests that the activators of GLP-1, GIP and FFA receptors may have a relatively low risk of hypoglycemia in fasting conditions whereas an activator of muscarinic receptors can increase this risk. This computational analysis demonstrates that study of second messenger pathway interactions will improve understanding of critical regulatory sites, how different GPCRs interact and pharmacological targets for modulating insulin secretion in type 2 diabetes.
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21
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Di Filippo M, Colombo R, Damiani C, Pescini D, Gaglio D, Vanoni M, Alberghina L, Mauri G. Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models. Comput Biol Chem 2016; 62:60-9. [PMID: 27085310 DOI: 10.1016/j.compbiolchem.2016.03.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 02/25/2016] [Accepted: 03/04/2016] [Indexed: 01/10/2023]
Abstract
The metabolic rearrangements occurring in cancer cells can be effectively investigated with a Systems Biology approach supported by metabolic network modeling. We here present tissue-specific constraint-based core models for three different types of tumors (liver, breast and lung) that serve this purpose. The core models were extracted and manually curated from the corresponding genome-scale metabolic models in the Human Metabolic Atlas database with a focus on the pathways that are known to play a key role in cancer growth and proliferation. Along similar lines, we also reconstructed a core model from the original general human metabolic network to be used as a reference model. A comparative Flux Balance Analysis between the reference and the cancer models highlighted both a clear distinction between the two conditions and a heterogeneity within the three different cancer types in terms of metabolic flux distribution. These results emphasize the need for modeling approaches able to keep up with this tumoral heterogeneity in order to identify more suitable drug targets and develop effective treatments. According to this perspective, we identified key points able to reverse the tumoral phenotype toward the reference one or vice-versa.
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Affiliation(s)
- Marzia Di Filippo
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Riccardo Colombo
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Chiara Damiani
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy.
| | - Daniela Gaglio
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Istituto di Bioimmagini e Fisiologia Molecolare, Consiglio Nazionale delle Ricerche, Via F.lli Cervi 93, 20090 Segrate (MI), Italy
| | - Marco Vanoni
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy
| | - Lilia Alberghina
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy
| | - Giancarlo Mauri
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
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Murabito E, Colombo R, Wu C, Verma M, Rehman S, Snoep J, Peng SL, Guan N, Liao X, Westerhoff HV. SupraBiology 2014: Promoting UK-China collaboration on Systems Biology and High Performance Computing. QUANTITATIVE BIOLOGY 2015. [DOI: 10.1007/s40484-015-0039-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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