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Bintener T, Pacheco MP, Philippidou D, Margue C, Kishk A, Del Mistro G, Di Leo L, Moscardó Garcia M, Halder R, Sinkkonen L, De Zio D, Kreis S, Kulms D, Sauter T. Metabolic modelling-based in silico drug target prediction identifies six novel repurposable drugs for melanoma. Cell Death Dis 2023; 14:468. [PMID: 37495601 PMCID: PMC10372000 DOI: 10.1038/s41419-023-05955-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 06/12/2023] [Accepted: 07/05/2023] [Indexed: 07/28/2023]
Abstract
Despite high initial response rates to targeted kinase inhibitors, the majority of patients suffering from metastatic melanoma present with high relapse rates, demanding for alternative therapeutic options. We have previously developed a drug repurposing workflow to identify metabolic drug targets that, if depleted, inhibit the growth of cancer cells without harming healthy tissues. In the current study, we have applied a refined version of the workflow to specifically predict both, common essential genes across various cancer types, and melanoma-specific essential genes that could potentially be used as drug targets for melanoma treatment. The in silico single gene deletion step was adapted to simulate the knock-out of all targets of a drug on an objective function such as growth or energy balance. Based on publicly available, and in-house, large-scale transcriptomic data metabolic models for melanoma were reconstructed enabling the prediction of 28 candidate drugs and estimating their respective efficacy. Twelve highly efficacious drugs with low half-maximal inhibitory concentration values for the treatment of other cancers, which are not yet approved for melanoma treatment, were used for in vitro validation using melanoma cell lines. Combination of the top 4 out of 6 promising candidate drugs with BRAF or MEK inhibitors, partially showed synergistic growth inhibition compared to individual BRAF/MEK inhibition. Hence, the repurposing of drugs may enable an increase in therapeutic options e.g., for non-responders or upon acquired resistance to conventional melanoma treatments.
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Affiliation(s)
- Tamara Bintener
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Demetra Philippidou
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Christiane Margue
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Greta Del Mistro
- Experimental Dermatology, Department of Dermatology, TU-Dresden, Dresden, Germany
- National Center for Tumour Diseases, TU-Dresden, Dresden, Germany
| | - Luca Di Leo
- Melanoma Research Team, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Maria Moscardó Garcia
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Lasse Sinkkonen
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Daniela De Zio
- Melanoma Research Team, Danish Cancer Society Research Center, Copenhagen, Denmark
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Stephanie Kreis
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Dagmar Kulms
- Experimental Dermatology, Department of Dermatology, TU-Dresden, Dresden, Germany
- National Center for Tumour Diseases, TU-Dresden, Dresden, Germany
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg.
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Sauter T, Bintener T, Kishk A, Presta L, Prohaska T, Guignard D, Zeng N, Cipriani C, Arshad S, Pfau T, Martins Conde P, Pires Pacheco M. Project-based learning course on metabolic network modelling in computational systems biology. PLoS Comput Biol 2022; 18:e1009711. [PMID: 35085230 PMCID: PMC8794106 DOI: 10.1371/journal.pcbi.1009711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Project-based learning (PBL) is a dynamic student-centred teaching method that encourages students to solve real-life problems while fostering engagement and critical thinking. Here, we report on a PBL course on metabolic network modelling that has been running for several years within the Master in Integrated Systems Biology (MISB) at the University of Luxembourg. This 2-week full-time block course comprises an introduction into the core concepts and methods of constraint-based modelling (CBM), applied to toy models and large-scale networks alongside the preparation of individual student projects in week 1 and, in week 2, the presentation and execution of these projects. We describe in detail the schedule and content of the course, exemplary student projects, and reflect on outcomes and lessons learned. PBL requires the full engagement of students and teachers and gives a rewarding teaching experience. The presented course can serve as a role model and inspiration for other similar courses.
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Affiliation(s)
- Thomas Sauter
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
- * E-mail:
| | - Tamara Bintener
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ali Kishk
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Luana Presta
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Tessy Prohaska
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Daniel Guignard
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ni Zeng
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Claudia Cipriani
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sundas Arshad
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Thomas Pfau
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Patricia Martins Conde
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
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Bintener T, Pacheco MP, Kishk A, Didier J, Sauter T. Drug Target Prediction Using Context-Specific Metabolic Models Reconstructed from rFASTCORMICS. Methods Mol Biol 2022; 2535:221-240. [PMID: 35867234 DOI: 10.1007/978-1-0716-2513-2_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Metabolic modeling is a powerful computational tool to analyze metabolism. It has not only been used to identify metabolic rewiring strategies in cancer but also to predict drug targets and candidate drugs for repurposing. Here, we will elaborate on the reconstruction of context-specific metabolic models of cancer using rFASTCORMICS and the subsequent prediction of drugs for repurposing using our drug prediction workflow.
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Affiliation(s)
- Tamara Bintener
- Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jeff Didier
- Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
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Moscardó García M, Pacheco M, Bintener T, Presta L, Sauter T. Importance of the biomass formulation for cancer metabolic modeling and drug prediction. iScience 2021; 24:103110. [PMID: 34622163 PMCID: PMC8482493 DOI: 10.1016/j.isci.2021.103110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 07/27/2021] [Accepted: 09/08/2021] [Indexed: 11/22/2022] Open
Abstract
Genome-scale metabolic reconstructions include all known biochemical reactions occurring in a cell. A typical application is the prediction of potential drug targets for cancer treatment. The precision of these predictions relies on the definition of the objective function. Generally, the biomass reaction is used to illustrate the growth capacity of a cancer cell. Today, seven human biomass reactions can be identified in published metabolic models. The impact of these differences on the metabolic model predictions has not been explored in detail. We explored this impact on cancer metabolic model predictions and showed that the metabolite composition and the associated coefficients had a large impact on the growth rate prediction accuracy, whereas gene essentiality predictions were mainly affected by the metabolite composition. Our results demonstrate the importance of defining a consensus biomass reaction compatible with most human models, which would contribute to ensuring the reproducibility and consistency of the results. The definition of the biomass reaction is of utmost importance for model predictions Growth rate predictions are affected by metabolite composition and their coefficients Gene essentiality predictions are mainly affected by the metabolite composition Need to find a standard biomass reaction for reproducibility and consistency purposes
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Affiliation(s)
- María Moscardó García
- Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Maria Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Tamara Bintener
- Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Luana Presta
- Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
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Pacheco MP, Bintener T, Sauter T. Towards the network-based prediction of repurposed drugs using patient-specific metabolic models. EBioMedicine 2019; 43:26-27. [PMID: 30979684 PMCID: PMC6557803 DOI: 10.1016/j.ebiom.2019.04.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 04/05/2019] [Indexed: 01/05/2023] Open
Affiliation(s)
- Maria Pires Pacheco
- Life Sciences Research Unit, University of Luxembourg, Esch-Alzette, Luxembourg; Ludwig-Maximilians-Universität München, Department Biology I, Plant Evolutionary Cell Biology, Planegg-Martinsried, Germany
| | - Tamara Bintener
- Life Sciences Research Unit, University of Luxembourg, Esch-Alzette, Luxembourg
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, Esch-Alzette, Luxembourg.
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Greenhalgh K, Ramiro-Garcia J, Heinken A, Ullmann P, Bintener T, Pacheco MP, Baginska J, Shah P, Frachet A, Halder R, Fritz JV, Sauter T, Thiele I, Haan S, Letellier E, Wilmes P. Integrated In Vitro and In Silico Modeling Delineates the Molecular Effects of a Synbiotic Regimen on Colorectal-Cancer-Derived Cells. Cell Rep 2019; 27:1621-1632.e9. [DOI: 10.1016/j.celrep.2019.04.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 02/11/2019] [Accepted: 03/28/2019] [Indexed: 02/08/2023] Open
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