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Kishk A, Pires Pacheco M, Heurtaux T, Sauter T. Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas. Brief Bioinform 2024; 25:bbae199. [PMID: 38701414 PMCID: PMC11066901 DOI: 10.1093/bib/bbae199] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/15/2024] [Accepted: 04/15/2024] [Indexed: 05/05/2024] Open
Abstract
Gliomas are the most common type of malignant brain tumors, with glioblastoma multiforme (GBM) having a median survival of 15 months due to drug resistance and relapse. The treatment of gliomas relies on surgery, radiotherapy and chemotherapy. Only 12 anti-brain tumor chemotherapies (AntiBCs), mostly alkylating agents, have been approved so far. Glioma subtype-specific metabolic models were reconstructed to simulate metabolite exchanges, in silico knockouts and the prediction of drug and drug combinations for all three subtypes. The simulations were confronted with literature, high-throughput screenings (HTSs), xenograft and clinical trial data to validate the workflow and further prioritize the drug candidates. The three subtype models accurately displayed different degrees of dependencies toward glutamine and glutamate. Furthermore, 33 single drugs, mainly antimetabolites and TXNRD1-inhibitors, as well as 17 drug combinations were predicted as potential candidates for gliomas. Half of these drug candidates have been previously tested in HTSs. Half of the tested drug candidates reduce proliferation in cell lines and two-thirds in xenografts. Most combinations were predicted to be efficient for all three glioma types. However, eflornithine/rifamycin and cannabidiol/adapalene were predicted specifically for GBM and low-grade glioma, respectively. Most drug candidates had comparable efficiency in preclinical tests, cerebrospinal fluid bioavailability and mode-of-action to AntiBCs. However, fotemustine and valganciclovir alone and eflornithine and celecoxib in combination with AntiBCs improved the survival compared to AntiBCs in two-arms, phase I/II and higher glioma clinical trials. Our work highlights the potential of metabolic modeling in advancing glioma drug discovery, which accurately predicted metabolic vulnerabilities, repurposable drugs and combinations for the glioma subtypes.
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Affiliation(s)
- Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Tony Heurtaux
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Luxembourg Centre of Neuropathology, L-3555 Dudelange, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
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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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Frias A, Di Leo L, Antoranz A, Nazerai L, Carretta M, Bodemeyer V, Pagliuca C, Dahl C, Claps G, Mandelli GE, Andhari MD, Pacheco MP, Sauter T, Robert C, Guldberg P, Madsen DH, Cecconi F, Bosisio FM, De Zio D. Ambra1 modulates the tumor immune microenvironment and response to PD-1 blockade in melanoma. J Immunother Cancer 2023; 11:jitc-2022-006389. [PMID: 36868570 PMCID: PMC9990656 DOI: 10.1136/jitc-2022-006389] [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] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/21/2023] [Indexed: 03/05/2023] Open
Abstract
BACKGROUND Loss of Ambra1 (autophagy and beclin 1 regulator 1), a multifunctional scaffold protein, promotes the formation of nevi and contributes to several phases of melanoma development. The suppressive functions of Ambra1 in melanoma are mediated by negative regulation of cell proliferation and invasion; however, evidence suggests that loss of Ambra1 may also affect the melanoma microenvironment. Here, we investigate the possible impact of Ambra1 on antitumor immunity and response to immunotherapy. METHODS This study was performed using an Ambra1-depleted BrafV600E /Pten-/ - genetically engineered mouse (GEM) model of melanoma, as well as GEM-derived allografts of BrafV600E /Pten-/ - and BrafV600E /Pten-/ -/Cdkn2a-/ - tumors with Ambra1 knockdown. The effects of Ambra1 loss on the tumor immune microenvironment (TIME) were analyzed using NanoString technology, multiplex immunohistochemistry, and flow cytometry. Transcriptome and CIBERSORT digital cytometry analyses of murine melanoma samples and human melanoma patients (The Cancer Genome Atlas) were applied to determine the immune cell populations in null or low-expressing AMBRA1 melanoma. The contribution of Ambra1 on T-cell migration was evaluated using a cytokine array and flow cytometry. Tumor growth kinetics and overall survival analysis in BrafV600E /Pten-/ -/Cdkn2a-/ - mice with Ambra1 knockdown were evaluated prior to and after administration of a programmed cell death protein-1 (PD-1) inhibitor. RESULTS Loss of Ambra1 was associated with altered expression of a wide range of cytokines and chemokines as well as decreased infiltration of tumors by regulatory T cells, a subpopulation of T cells with potent immune-suppressive properties. These changes in TIME composition were associated with the autophagic function of Ambra1. In the BrafV600E /Pten-/ -/Cdkn2a-/ - model inherently resistant to immune checkpoint blockade, knockdown of Ambra1 led to accelerated tumor growth and reduced overall survival, but at the same time conferred sensitivity to anti-PD-1 treatment. CONCLUSIONS This study shows that loss of Ambra1 affects the TIME and the antitumor immune response in melanoma, highlighting new functions of Ambra1 in the regulation of melanoma biology.
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Affiliation(s)
- Alex Frias
- Melanoma Research Team, Center for Autophagy, Recycling and Disease (CARD), Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Luca Di Leo
- Melanoma Research Team, Center for Autophagy, Recycling and Disease (CARD), Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Asier Antoranz
- Lab of Translational Cell and Tissue Research, KU Leuven, Leuven, Belgium
| | - Loulieta Nazerai
- Melanoma Research Team, Center for Autophagy, Recycling and Disease (CARD), Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Marco Carretta
- National Center for Cancer Immunotherapy, Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Valérie Bodemeyer
- Melanoma Research Team, Center for Autophagy, Recycling and Disease (CARD), Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Chiara Pagliuca
- Melanoma Research Team, Center for Autophagy, Recycling and Disease (CARD), Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Christina Dahl
- Molecular Diagnostics Group, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Giuseppina Claps
- INSERM U981 and Department of Oncologic Medicine, Gustave Roussy Institute and Paris Saclay University, Villejuif, France
| | | | | | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Caroline Robert
- INSERM U981 and Department of Oncologic Medicine, Gustave Roussy Institute and Paris Saclay University, Villejuif, France
| | - Per Guldberg
- Molecular Diagnostics Group, Danish Cancer Society Research Center, Copenhagen, Denmark.,Department of Cancer and Inflammation Research, Institute for Molecular Medicine, University of Southern Denmark, Odense, Denmark
| | - Daniel Hargbøl Madsen
- National Center for Cancer Immunotherapy, Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Francesco Cecconi
- Cell Stress and Survival, Center for Autophagy, Recycling and Disease (CARD), Danish Cancer Society Research Center, Copenhagen, Denmark.,Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | - Daniela De Zio
- Melanoma Research Team, Center for Autophagy, Recycling and Disease (CARD), Danish Cancer Society Research Center, Copenhagen, Denmark .,Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Kishk A, Pacheco MP, Heurtaux T, Sinkkonen L, Pang J, Fritah S, Niclou SP, Sauter T. Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases. Cells 2022; 11:cells11162486. [PMID: 36010563 PMCID: PMC9406599 DOI: 10.3390/cells11162486] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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] [Received: 05/23/2022] [Revised: 07/28/2022] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.
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Affiliation(s)
- Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Tony Heurtaux
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Luxembourg Center of Neuropathology, L-3555 Dudelange, Luxembourg
| | - Lasse Sinkkonen
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Jun Pang
- Department of Computer Science, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg
| | - Sabrina Fritah
- NORLUX Neuro-Oncology Laboratory, Luxembourg Institute of Health, Department of Cancer Research, L-1526 Luxembourg, Luxembourg
| | - Simone P. Niclou
- NORLUX Neuro-Oncology Laboratory, Luxembourg Institute of Health, Department of Cancer Research, L-1526 Luxembourg, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Correspondence:
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Cipriani C, Pacheco MP, Kishk A, Wachich M, Abankwa D, Schaffner-Reckinger E, Sauter T. Bruceine D Identified as a Drug Candidate against Breast Cancer by a Novel Drug Selection Pipeline and Cell Viability Assay. Pharmaceuticals (Basel) 2022; 15:ph15020179. [PMID: 35215292 PMCID: PMC8875459 DOI: 10.3390/ph15020179] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/20/2022] [Accepted: 01/26/2022] [Indexed: 02/06/2023] Open
Abstract
The multi-target effects of natural products allow us to fight complex diseases like cancer on multiple fronts. Unlike docking techniques, network-based approaches such as genome-scale metabolic modelling can capture multi-target effects. However, the incompleteness of natural product target information reduces the prediction accuracy of in silico gene knockout strategies. Here, we present a drug selection workflow based on context-specific genome-scale metabolic models, built from the expression data of cancer cells treated with natural products, to predict cell viability. The workflow comprises four steps: first, in silico single-drug and drug combination predictions; second, the assessment of the effects of natural products on cancer metabolism via the computation of a dissimilarity score between the treated and control models; third, the identification of natural products with similar effects to the approved drugs; and fourth, the identification of drugs with the predicted effects in pathways of interest, such as the androgen and estrogen pathway. Out of the initial 101 natural products, nine candidates were tested in a 2D cell viability assay. Bruceine D, emodin, and scutellarein showed a dose-dependent inhibition of MCF-7 and Hs 578T cell proliferation with IC50 values between 0.7 to 65 μM, depending on the drug and cell line. Bruceine D, extracted from Brucea javanica seeds, showed the highest potency.
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Affiliation(s)
- Claudia Cipriani
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (C.C.); (M.P.P.); (A.K.)
| | - Maria Pires Pacheco
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (C.C.); (M.P.P.); (A.K.)
| | - Ali Kishk
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (C.C.); (M.P.P.); (A.K.)
| | - Maryem Wachich
- Cancer Cell Biology and Drug Discovery Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (M.W.); (D.A.); (E.S.-R.)
| | - Daniel Abankwa
- Cancer Cell Biology and Drug Discovery Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (M.W.); (D.A.); (E.S.-R.)
| | - Elisabeth Schaffner-Reckinger
- Cancer Cell Biology and Drug Discovery Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (M.W.); (D.A.); (E.S.-R.)
| | - Thomas Sauter
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg; (C.C.); (M.P.P.); (A.K.)
- Correspondence:
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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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Kishk A, Pacheco MP, Sauter T. DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling. iScience 2021; 24:103331. [PMID: 34723158 PMCID: PMC8536485 DOI: 10.1016/j.isci.2021.103331] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 04/19/2021] [Revised: 07/29/2021] [Accepted: 10/19/2021] [Indexed: 12/15/2022] Open
Abstract
The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).
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Affiliation(s)
- Ali Kishk
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Maria Pires Pacheco
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Thomas Sauter
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
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Abstract
Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood and urine metabolite dynamics, the integration of multiple metabolically active tissues is necessary. We developed a dynamic multi-tissue model, which recapitulates key properties of human metabolism at the molecular and physiological level based on the integration of transcriptomics data. It enables the simulation of the dynamics of intra-cellular and extra-cellular metabolites at the genome scale. The predictive capacity of the model is shown through the accurate simulation of different healthy conditions (i.e., during fasting, while consuming meals or during exercise), and the prediction of biomarkers for a set of Inborn Errors of Metabolism with a precision of 83%. This novel approach is useful to prioritize new biomarkers for many metabolic diseases, as well as for the integration of various types of personal omics data, towards the personalized analysis of blood and urine metabolites.
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Affiliation(s)
- Patricia Martins Conde
- Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Megeno S.A., Esch-sur-Alzette, Luxembourg
| | - Thomas Pfau
- 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
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, 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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Abstract
The FASTCORE family is a family of algorithms that are mainly used to build context-specific models but can also be applied to other tasks such as gapfilling and consistency testing. The FASTCORE family has very low computational demands with running times that are several orders of magnitude lower than its main competitors. Furthermore, the models built by the FASTCORE family have a better resolution power (defined as the ability to capture metabolic variations between different tissues, cell types, or contexts) than models from other algorithms.
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Affiliation(s)
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
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Pfau T, Pacheco MP, Sauter T. Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond. Brief Bioinform 2015; 17:1060-1069. [PMID: 26615025 PMCID: PMC5142010 DOI: 10.1093/bib/bbv100] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 10/20/2015] [Indexed: 12/24/2022] Open
Abstract
Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted.
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Affiliation(s)
- Thomas Pfau
- *These authors contributed equally to this work
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Pacheco MP, John E, Kaoma T, Heinäniemi M, Nicot N, Vallar L, Bueb JL, Sinkkonen L, Sauter T. Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network. BMC Genomics 2015; 16:809. [PMID: 26480823 PMCID: PMC4617894 DOI: 10.1186/s12864-015-1984-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Accepted: 10/06/2015] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied. METHODS Here we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks. RESULTS To understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism. CONCLUSIONS By integrating metabolic modelling and epigenomic analysis we have identified high regulatory load as a common feature of metabolic genes at pathway entry points such as transporters within the macrophage metabolic network. Analysis of these control points through further integration of metabolic and gene regulatory networks in various contexts could be beneficial in multiple fields from identification of disease intervention strategies to cellular reprogramming.
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Affiliation(s)
- Maria Pires Pacheco
- Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg.
| | - Elisabeth John
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg.
| | - Tony Kaoma
- Genomics Research Unit, Luxembourg Institute of Health, L-1526, Luxembourg, Luxembourg.
| | - Merja Heinäniemi
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, 70211, Kuopio, Finland.
| | - Nathalie Nicot
- Genomics Research Unit, Luxembourg Institute of Health, L-1526, Luxembourg, Luxembourg.
| | - Laurent Vallar
- Genomics Research Unit, Luxembourg Institute of Health, L-1526, Luxembourg, Luxembourg.
| | - Jean-Luc Bueb
- Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg.
| | - Lasse Sinkkonen
- Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg.
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg.
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg.
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Abstract
Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.
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Affiliation(s)
- Nikos Vlassis
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg City, Luxembourg
| | - Maria Pires Pacheco
- Life Sciences Research Unit, University of Luxembourg, Luxembourg City, Luxembourg
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, Luxembourg City, Luxembourg
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