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Blasco T, Balzerani F, Valcárcel LV, Larrañaga P, Bielza C, Francino MP, Rufián-Henares JÁ, Planes FJ, Pérez-Burillo S. BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities. Bioinformatics 2024; 40:btae266. [PMID: 38688585 PMCID: PMC11082422 DOI: 10.1093/bioinformatics/btae266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 03/11/2024] [Accepted: 04/29/2024] [Indexed: 05/02/2024] Open
Abstract
MOTIVATION Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe-microbe stimulant or inhibitory effects or for nutrient-microbe inhibitory effects, typically observed in different compounds present in the daily diet. RESULTS Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe-microbe and nutrient-microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient-bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria-bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism. AVAILABILITY AND IMPLEMENTATION MATLAB and R code are available in https://github.com/PlanesLab/BN-BacArena.
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Affiliation(s)
- Telmo Blasco
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
| | - Francesco Balzerani
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
| | - Luis V Valcárcel
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, Campus Universitario, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, University of Navarra, Pamplona 31080, Spain
| | - Pedro Larrañaga
- Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid 28660, Spain
| | - Concha Bielza
- Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid 28660, Spain
| | - María Pilar Francino
- Area de Genómica y Salud, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia 46020, Spain
- CIBER en Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid 28029, Spain
| | - José Ángel Rufián-Henares
- Departamento de Nutrición y Bromatología, Centro de Investigación Biomédica, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Granada, Granada 18016, Spain
- Instituto de Investigación Biosanitaria ibs. GRANADA, Universidad de Granada, Granada 18012, Spain
| | - Francisco J Planes
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, Campus Universitario, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, University of Navarra, Pamplona 31080, Spain
| | - Sergio Pérez-Burillo
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
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Barrena N, Valcárcel LV, Olaverri-Mendizabal D, Apaolaza I, Planes FJ. Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells. NPJ Syst Biol Appl 2023; 9:32. [PMID: 37454223 DOI: 10.1038/s41540-023-00296-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 07/04/2023] [Indexed: 07/18/2023] Open
Abstract
Synthetic lethality (SL) is a promising concept in cancer research. A wide array of computational tools has been developed to predict and exploit synthetic lethality for the identification of tumour-specific vulnerabilities. Previously, we introduced the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to SL developed for genome-scale metabolic networks. The major challenge in our gMCS framework is to go beyond metabolic networks and extend existing algorithms to more complex protein-protein interactions. In this article, we take a step further and incorporate linear regulatory pathways into our gMCS approach. Extensive algorithmic modifications to compute gMCSs in integrated metabolic and regulatory models are presented in detail. Our extended approach is applied to calculate gMCSs in integrated models of human cells. In particular, we integrate the most recent genome-scale metabolic network, Human1, with 3 different regulatory network databases: Omnipath, Dorothea and TRRUST. Based on the computed gMCSs and transcriptomic data, we discovered new essential genes and their associated synthetic lethal for different cancer cell lines. The performance of the different integrated models is assessed with available large-scale in-vitro gene silencing data. Finally, we discuss the most relevant gene essentiality predictions based on published literature in cancer research.
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Affiliation(s)
- Naroa Barrena
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Luis V Valcárcel
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain
| | - Danel Olaverri-Mendizabal
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Iñigo Apaolaza
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain
| | - Francisco J Planes
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain.
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain.
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San Juan I, Pereira-Ortuzar T, Cendoya X, Laín A, To-Figueras J, Mateos B, Planes FJ, Bernardo-Seisdedos G, Mato JM, Millet O. ALAD Inhibition by Porphobilinogen Rationalizes the Accumulation of δ-Aminolevulinate in Acute Porphyrias. Biochemistry 2022; 61:2409-2416. [PMID: 36241173 DOI: 10.1021/acs.biochem.2c00434] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Patients with major forms of acute hepatic porphyria present acute neurological attacks with overproduction of porphobilinogen (PBG) and δ-aminolevulinic acid (ALA). Even if ALA is considered the most likely agent inducing the acute symptoms, the mechanism of its accumulation has not been experimentally demonstrated. In the most frequent form, acute intermittent porphyria (AIP), inherited gene mutations induce a deficiency in PBG deaminase; thus, accumulation of the substrate PBG is biochemically obligated but not that of ALA. A similar scenario is observed in other forms of acute hepatic porphyria (i.e., porphyria variegate, VP) in which PBG deaminase is inhibited by metabolic intermediates. Here, we have investigated the molecular basis of δ-aminolevulinate accumulation using in vitro fluxomics monitored by NMR spectroscopy and other biophysical techniques. Our results show that porphobilinogen, the natural product of δ-aminolevulinate deaminase, effectively inhibits its anabolic enzyme at abnormally low concentrations. Structurally, this high affinity can be explained by the interactions that porphobilinogen generates with the active site, most of them shared with the substrate. Enzymatically, our flux analysis of an altered heme pathway demonstrates that a minimum accumulation of porphobilinogen will immediately trigger the accumulation of δ-aminolevulinate, a long-lasting observation in patients suffering from acute porphyrias.
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Affiliation(s)
- Itxaso San Juan
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - Tania Pereira-Ortuzar
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - Xabier Cendoya
- , Universidad de Navarra, Tecnun Escuela de Ingeniería y Centro de Ingeniería Biomédica, San Sebastián 20009, Spain
| | - Ana Laín
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - Jordi To-Figueras
- Biochemistry and Molecular Genetics Unit, Hospital Clinic, IDIBAPS, University of Barcelona, Villarroel 170, 08036 Barcelona, Spain
| | - Borja Mateos
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - Francisco J Planes
- , Universidad de Navarra, Tecnun Escuela de Ingeniería y Centro de Ingeniería Biomédica, San Sebastián 20009, Spain.,Universidad de Navarra, DATAI Instituto de Ciencia de los Datos e Inteligencia Artificial, Pamplona 31009, Spain
| | - Ganeko Bernardo-Seisdedos
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Science and Technology Park, 48160 Derio, Spain.,ATLAS Molecular Pharma, Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - José M Mato
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Science and Technology Park, 48160 Derio, Spain.,Biomedical Research Network on Hepatic and Digestive Diseases (CIBEREHD), Instituto de Salud Carlos III, Madrid 28029, Spain
| | - Oscar Millet
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Science and Technology Park, 48160 Derio, Spain.,ATLAS Molecular Pharma, Bizkaia Science and Technology Park, 48160 Derio, Spain.,Biomedical Research Network on Hepatic and Digestive Diseases (CIBEREHD), Instituto de Salud Carlos III, Madrid 28029, Spain
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4
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Viñado AC, Calvo IA, Cenzano I, Olaverri D, Cocera M, San Martin-Uriz P, Romero JP, Vilas-Zornoza A, Vera L, Gomez-Cebrian N, Puchades-Carrasco L, Lisi-Vega LE, Apaolaza I, Valera P, Guruceaga E, Granero-Molto F, Ripalda-Cemborain P, Luck TJ, Bullinger L, Planes FJ, Rifon JJ, Méndez-Ferrer S, Yusuf RZ, Pardo-Saganta A, Prosper F, Saez B. The bone marrow niche regulates redox and energy balance in MLL::AF9 leukemia stem cells. Leukemia 2022; 36:1969-1979. [PMID: 35618797 PMCID: PMC7614282 DOI: 10.1038/s41375-022-01601-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 01/14/2023]
Abstract
Eradicating leukemia requires a deep understanding of the interaction between leukemic cells and their protective microenvironment. The CXCL12/CXCR4 axis has been postulated as a critical pathway dictating leukemia stem cell (LSC) chemoresistance in AML due to its role in controlling cellular egress from the marrow. Nevertheless, the cellular source of CXCL12 in the acute myeloid leukemia (AML) microenvironment and the mechanism by which CXCL12 exerts its protective role in vivo remain unresolved. Here, we show that CXCL12 produced by Prx1+ mesenchymal cells but not by mature osteolineage cells provide the necessary cues for the maintenance of LSCs in the marrow of an MLL::AF9-induced AML model. Prx1+ cells promote survival of LSCs by modulating energy metabolism and the REDOX balance in LSCs. Deletion of Cxcl12 leads to the accumulation of reactive oxygen species and DNA damage in LSCs, impairing their ability to perpetuate leukemia in transplantation experiments, a defect that can be attenuated by antioxidant therapy. Importantly, our data suggest that this phenomenon appears to be conserved in human patients. Hence, we have identified Prx1+ mesenchymal cells as an integral part of the complex niche-AML metabolic intertwining, pointing towards CXCL12/CXCR4 as a target to eradicate parenchymal LSCs in AML.
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Affiliation(s)
- Ana C Viñado
- Hematology-Oncology Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain
| | - Isabel A Calvo
- Hematology-Oncology Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain
| | - Itziar Cenzano
- Hematology-Oncology Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
| | - Danel Olaverri
- Tecnun Universidad de Navarra, School of Engineering, 20018, San Sebastian, Spain
| | - Miguel Cocera
- Hematology-Oncology Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
| | - Patxi San Martin-Uriz
- Hematology-Oncology Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
| | - Juan P Romero
- Hematology-Oncology Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
| | - Amaia Vilas-Zornoza
- Hematology-Oncology Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
| | - Laura Vera
- Regenerative Medicine Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
| | - Nuria Gomez-Cebrian
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026, Valencia, Spain
| | | | - Livia E Lisi-Vega
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, and NHS Blood and Transplant, Cambridge, CB2 0AW, UK
| | - Iñigo Apaolaza
- Tecnun Universidad de Navarra, School of Engineering, 20018, San Sebastian, Spain
- Universidad de Navarra, Centro de Ingeniería Biomédica and DATAI Instituto de Ciencia de los Datos e Inteligencia Artificial, 31008, Pamplona, Spain
| | - Pablo Valera
- Hematology-Oncology Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
| | - Elisabeth Guruceaga
- Hematology-Oncology Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
| | - Froilan Granero-Molto
- Regenerative Medicine Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
- Cell Therapy Area, Clínica Universidad de Navarra, 31008, Pamplona, Spain
- Department of Orthopaedic Surgery and Traumatology, Clínica Universidad de Navarra, 31008, Pamplona, Spain
| | - Purificacion Ripalda-Cemborain
- Regenerative Medicine Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
- Department of Orthopaedic Surgery and Traumatology, Clínica Universidad de Navarra, 31008, Pamplona, Spain
| | - Tamara J Luck
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Hematology, Oncology, and Cancer Immunology, Berlin, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lars Bullinger
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Hematology, Oncology, and Cancer Immunology, Berlin, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Francisco J Planes
- Tecnun Universidad de Navarra, School of Engineering, 20018, San Sebastian, Spain
- Universidad de Navarra, Centro de Ingeniería Biomédica and DATAI Instituto de Ciencia de los Datos e Inteligencia Artificial, 31008, Pamplona, Spain
| | - José J Rifon
- Department of Hematology, Cell Therapy and Center of Cancer of the University of Navarra, Clínica Universidad de Navarra (CCUN), 31008, Pamplona, Spain
| | - Simón Méndez-Ferrer
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, and NHS Blood and Transplant, Cambridge, CB2 0AW, UK
| | - Rushdia Z Yusuf
- Dana Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA
| | - Ana Pardo-Saganta
- Institute for Lung Health (ILH), Justus Liebig University Giessen, Giessen, 35392, Germany
- Universities of Giessen and Marburg Lung Center (UGMLC), Giessen, 35392, Germany
- German Center for Lung Research (DZL), Giessen, 35392, Germany
| | - Felipe Prosper
- Hematology-Oncology Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain
- Department of Hematology, Cell Therapy and Center of Cancer of the University of Navarra, Clínica Universidad de Navarra (CCUN), 31008, Pamplona, Spain
| | - Borja Saez
- Hematology-Oncology Program, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008, Pamplona, Spain.
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain.
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Valcárcel LV, San José-Enériz E, Cendoya X, Rubio Á, Agirre X, Prósper F, Planes FJ. BOSO: A novel feature selection algorithm for linear regression with high-dimensional data. PLoS Comput Biol 2022; 18:e1010180. [PMID: 35639775 PMCID: PMC9187084 DOI: 10.1371/journal.pcbi.1010180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 06/10/2022] [Accepted: 05/07/2022] [Indexed: 11/18/2022] Open
Abstract
With the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. We introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior accuracy for feature selection in high-dimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism. We present BOSO (Bilevel Optimization Selector Operator), a novel method to conduct feature selection in linear regression models. In machine learning, feature selection consists of identifying the subset of input variables (features) that are correctly associated with the response variable that is aimed to be predicted. An adequate feature selection is particularly relevant for high-dimensional datasets, commonly encountered in biomedical research questions that rely on -omics data, e.g. predictive models of drug sensitivity, resistance or toxicity, construction of gene regulatory networks, biomarker selection or association studies. The need of feature selection is emphasized in many of these complex problems, since the number of features is greater than the number of samples, which makes it harder to obtain accurate and general predictive models. In this context, we show that the models derived by BOSO make a better combination of accuracy and simplicity than competing approaches in the literature. The relevance of BOSO is illustrated in the prediction of drug sensitivity of cancer cell lines, using RNA-seq data and drug screenings from GDSC (Genomics of Drug Sensitivity in Cancer) database. BOSO obtains linear regression models with a similar level of accuracy but involving a substantially lower number of features, which simplifies the interpretation and validation of predictive models.
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Affiliation(s)
- Luis V. Valcárcel
- Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
| | - Edurne San José-Enériz
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, Pamplona, Spain
| | - Xabier Cendoya
- Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain
| | - Ángel Rubio
- Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain
- Universidad de Navarra, Centro de Ingeniería Biomédica, Pamplona, Spain
- Universidad de Navarra, DATAI Instituto de Ciencia de los Datos e Inteligencia Artificial, Pamplona, Spain
| | - Xabier Agirre
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, Pamplona, Spain
| | - Felipe Prósper
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, Pamplona, Spain
- IdiSNA Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
- Clínica Universidad de Navarra, Pamplona, Spain
| | - Francisco J. Planes
- Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain
- Universidad de Navarra, Centro de Ingeniería Biomédica, Pamplona, Spain
- Universidad de Navarra, DATAI Instituto de Ciencia de los Datos e Inteligencia Artificial, Pamplona, Spain
- * E-mail:
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6
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Apaolaza I, San José-Enériz E, Valcarcel LV, Agirre X, Prosper F, Planes FJ. A network-based approach to integrate nutrient microenvironment in the prediction of synthetic lethality in cancer metabolism. PLoS Comput Biol 2022; 18:e1009395. [PMID: 35286311 PMCID: PMC8947600 DOI: 10.1371/journal.pcbi.1009395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 03/24/2022] [Accepted: 02/18/2022] [Indexed: 11/18/2022] Open
Abstract
Synthetic Lethality (SL) is currently defined as a type of genetic interaction in which the loss of function of either of two genes individually has limited effect in cell viability but inactivation of both genes simultaneously leads to cell death. Given the profound genomic aberrations acquired by tumor cells, which can be systematically identified with -omics data, SL is a promising concept in cancer research. In particular, SL has received much attention in the area of cancer metabolism, due to the fact that relevant functional alterations concentrate on key metabolic pathways that promote cellular proliferation. With the extensive prior knowledge about human metabolic networks, a number of computational methods have been developed to predict SL in cancer metabolism, including the genetic Minimal Cut Sets (gMCSs) approach. A major challenge in the application of SL approaches to cancer metabolism is to systematically integrate tumor microenvironment, given that genetic interactions and nutritional availability are interconnected to support proliferation. Here, we propose a more general definition of SL for cancer metabolism that combines genetic and environmental interactions, namely loss of gene functions and absence of nutrients in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase (DHFR) inhibition in different environments. Finally, our approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies. Metabolic reprogramming is one of the hallmarks of tumor cells. Synthetic lethality (SL) is a promising approach to exploit these metabolic alterations and elucidate cancer-specific genetic dependences. However, current SL approaches do not systematically consider tumor microenvironment, which is particularly important in cancer metabolism in order to generalize identified genetic dependences. In this article, we directly address this issue and propose a more general approach to SL that integrates both genetic and environmental context of tumor cells. Our definition can help to contextualize genetic dependencies in different environmental scenarios, but it could also reveal nutrient dependencies according to the genetic context. We also provide a computational pipeline to identify this new family of synthetic lethals in genome-scale metabolic networks. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase (DHFR) inhibition in different environments. Finally, our approach is applied to identify extracellular nutrient dependences of cancer cell lines, elucidating cholesterol and myo-Inositol depletion as potential vulnerabilities in different malignancies.
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Affiliation(s)
- Iñigo Apaolaza
- Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain
- Universidad de Navarra, Centro de Ingeniería Biomédica, Pamplona, Spain
- Universidad de Navarra, DATAI Instituto de Ciencia de los Datos e Inteligencia Artificial, Pamplona, Spain
| | - Edurne San José-Enériz
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
- IdiSNA Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, Pamplona, Spain
| | - Luis V. Valcarcel
- Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
- IdiSNA Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | - Xabier Agirre
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
- IdiSNA Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, Pamplona, Spain
| | - Felipe Prosper
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
- IdiSNA Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, Pamplona, Spain
- Clínica Universidad de Navarra, Pamplona, Spain
| | - Francisco J. Planes
- Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain
- Universidad de Navarra, Centro de Ingeniería Biomédica, Pamplona, Spain
- Universidad de Navarra, DATAI Instituto de Ciencia de los Datos e Inteligencia Artificial, Pamplona, Spain
- * E-mail:
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7
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Amundarain A, Valcárcel LV, Ordoñez R, Garate L, Miranda E, Cendoya X, Carrasco‐Leon A, Calasanz MJ, Paiva B, Meydan C, Mason CE, Melnick A, Rodriguez‐Otero P, Martín‐Subero JI, San Miguel J, Planes FJ, Prósper F, Agirre X. Landscape and clinical significance of long noncoding RNAs involved in multiple myeloma expressed fusion transcripts. Am J Hematol 2022; 97:E113-E117. [PMID: 34961980 DOI: 10.1002/ajh.26450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 12/06/2021] [Accepted: 12/20/2021] [Indexed: 12/31/2022]
Affiliation(s)
- Ane Amundarain
- Hemato‐Oncology Program, Center for Applied Medical Research (CIMA), IDISNA University of Navarra Pamplona Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
| | - Luis V. Valcárcel
- Hemato‐Oncology Program, Center for Applied Medical Research (CIMA), IDISNA University of Navarra Pamplona Spain
- Tecnun School of Engineering, Biomedical Engineering Center University of Navarra San Sebastian Spain
| | - Raquel Ordoñez
- Hemato‐Oncology Program, Center for Applied Medical Research (CIMA), IDISNA University of Navarra Pamplona Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
| | - Leire Garate
- Hemato‐Oncology Program, Center for Applied Medical Research (CIMA), IDISNA University of Navarra Pamplona Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
- Hematology Department, Clínica Universidad de Navarra University of Navarra Pamplona Spain
| | - Estíbaliz Miranda
- Hemato‐Oncology Program, Center for Applied Medical Research (CIMA), IDISNA University of Navarra Pamplona Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
| | - Xabier Cendoya
- Tecnun School of Engineering, Biomedical Engineering Center University of Navarra San Sebastian Spain
| | - Arantxa Carrasco‐Leon
- Hemato‐Oncology Program, Center for Applied Medical Research (CIMA), IDISNA University of Navarra Pamplona Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
| | - María José Calasanz
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
- CIMA LAB Diagnostics University of Navarra Pamplona Spain
| | - Bruno Paiva
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
- Hematology Department, Clínica Universidad de Navarra University of Navarra Pamplona Spain
- CIMA LAB Diagnostics University of Navarra Pamplona Spain
- Flow Cytometry Core, CIMA University of Navarra Pamplona Spain
| | - Cem Meydan
- Division of Hematology/Oncology, Department of Medicine Weill Cornell Medical College New York New York USA
- Department of Physiology and Biophysics Weill Cornell Medicine New York New York USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine Weill Cornell Medicine New York New York USA
| | - Christopher E. Mason
- Division of Hematology/Oncology, Department of Medicine Weill Cornell Medical College New York New York USA
- Department of Physiology and Biophysics Weill Cornell Medicine New York New York USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine Weill Cornell Medicine New York New York USA
| | - Ari Melnick
- Division of Hematology/Oncology, Department of Medicine Weill Cornell Medical College New York New York USA
| | - Paula Rodriguez‐Otero
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
- Hematology Department, Clínica Universidad de Navarra University of Navarra Pamplona Spain
| | - José I. Martín‐Subero
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
- Hospital Clínic de Barcelona and Departament de Fonaments Clínics, Facultat de Medicina Universitat de Barcelona Barcelona Spain
- Institut d'Investigacions Biomèdiques August Pi I Sunyer IDIBAPS Barcelona Spain
- Institució Catalana de Recerca i Estudis Avançats ICREA Barcelona Spain
| | - Jesús San Miguel
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
- Hematology Department, Clínica Universidad de Navarra University of Navarra Pamplona Spain
| | - Francisco J. Planes
- Tecnun School of Engineering, Biomedical Engineering Center University of Navarra San Sebastian Spain
| | - Felipe Prósper
- Hemato‐Oncology Program, Center for Applied Medical Research (CIMA), IDISNA University of Navarra Pamplona Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
- Hematology Department, Clínica Universidad de Navarra University of Navarra Pamplona Spain
| | - Xabier Agirre
- Hemato‐Oncology Program, Center for Applied Medical Research (CIMA), IDISNA University of Navarra Pamplona Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Pamplona Spain
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8
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Blasco T, Pérez-Burillo S, Balzerani F, Hinojosa-Nogueira D, Lerma-Aguilera A, Pastoriza S, Cendoya X, Rubio Á, Gosalbes MJ, Jiménez-Hernández N, Pilar Francino M, Apaolaza I, Rufián-Henares JÁ, Planes FJ. An extended reconstruction of human gut microbiota metabolism of dietary compounds. Nat Commun 2021; 12:4728. [PMID: 34354065 PMCID: PMC8342455 DOI: 10.1038/s41467-021-25056-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 07/21/2021] [Indexed: 02/07/2023] Open
Abstract
Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex problem. However, when applied to nutritional questions, a major issue in existing reconstructions is the limited information about compounds in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of diet metabolism in the human gut microbiota. AGREDA adds the degradation pathways of 209 compounds present in the human diet, mainly phenolic compounds, a family of metabolites highly relevant for human health and nutrition. We show that AGREDA outperforms existing reconstructions in predicting diet-specific output metabolites from the gut microbiota. Using 16S rRNA gene sequencing data of faecal samples from Spanish children representing different clinical conditions, we illustrate the potential of AGREDA to establish relevant metabolic interactions between diet and gut microbiota.
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Affiliation(s)
- Telmo Blasco
- Tecnun, University of Navarra, San Sebastián, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain
| | - Sergio Pérez-Burillo
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Francesco Balzerani
- Tecnun, University of Navarra, San Sebastián, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain
| | - Daniel Hinojosa-Nogueira
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Alberto Lerma-Aguilera
- Área de Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia, Spain
- CIBER en Epidemiología y Salud Pública, Madrid, Spain
| | - Silvia Pastoriza
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Xabier Cendoya
- Tecnun, University of Navarra, San Sebastián, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain
| | - Ángel Rubio
- Tecnun, University of Navarra, San Sebastián, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain
| | - María José Gosalbes
- Área de Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia, Spain
- CIBER en Epidemiología y Salud Pública, Madrid, Spain
| | - Nuria Jiménez-Hernández
- Área de Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia, Spain
- CIBER en Epidemiología y Salud Pública, Madrid, Spain
| | - M Pilar Francino
- Área de Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia, Spain.
- CIBER en Epidemiología y Salud Pública, Madrid, Spain.
| | - Iñigo Apaolaza
- Tecnun, University of Navarra, San Sebastián, Spain.
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain.
| | - José Ángel Rufián-Henares
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain.
- Instituto de Investigación Biosanitaria ibs.GRANADA, Universidad de Granada, Granada, Spain.
| | - Francisco J Planes
- Tecnun, University of Navarra, San Sebastián, Spain.
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain.
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9
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Pérez-Burillo S, Hinojosa-Nogueira D, Navajas-Porras B, Blasco T, Balzerani F, Lerma-Aguilera A, León D, Pastoriza S, Apaolaza I, Planes FJ, Francino MP, Rufián-Henares JÁ. Effect of Freezing on Gut Microbiota Composition and Functionality for In Vitro Fermentation Experiments. Nutrients 2021; 13:nu13072207. [PMID: 34199047 PMCID: PMC8308218 DOI: 10.3390/nu13072207] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 06/17/2021] [Accepted: 06/23/2021] [Indexed: 11/16/2022] Open
Abstract
The gut microbiota has a profound effect on human health and is modulated by food and bioactive compounds. To study such interaction, in vitro batch fermentations are performed with fecal material, and some experimental designs may require that such fermentations be performed with previously frozen stools. Although it is known that freezing fecal material does not alter the composition of the microbial community in 16S rRNA gene amplicon and metagenomic sequencing studies, it is not known whether the microbial community in frozen samples could still be used for in vitro fermentations. To explore this, we undertook a pilot study in which in vitro fermentations were performed with fecal material from celiac, cow’s milk allergic, obese, or lean children that was frozen (or not) with 20% glycerol. Before fermentation, the fecal material was incubated in a nutritious medium for 6 days, with the aim of giving the microbial community time to recover from the effects of freezing. An aliquot was taken daily from the stabilization vessel and used for the in vitro batch fermentation of lentils. The microbial community structure was significantly different between fresh and frozen samples, but the variation introduced by freezing a sample was always smaller than the variation among individuals, both before and after fermentation. Moreover, the potential functionality (as determined in silico by a genome-scaled metabolic reconstruction) did not differ significantly, possibly due to functional redundancy. The most affected genus was Bacteroides, a fiber degrader. In conclusion, if frozen fecal material is to be used for in vitro fermentation purposes, our preliminary analyses indicate that the functionality of microbial communities can be preserved after stabilization.
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Affiliation(s)
- Sergio Pérez-Burillo
- Centro de Investigación Biomédica, Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Granada, 18071 Granada, Spain; (S.P.-B.); (D.H.-N.); (B.N.-P.); (S.P.)
- Department of Biochemistry and Molecular Biology, Boonshoft School of Medicine, Wright State University, Dayton, OH 45435, USA
| | - Daniel Hinojosa-Nogueira
- Centro de Investigación Biomédica, Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Granada, 18071 Granada, Spain; (S.P.-B.); (D.H.-N.); (B.N.-P.); (S.P.)
| | - Beatriz Navajas-Porras
- Centro de Investigación Biomédica, Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Granada, 18071 Granada, Spain; (S.P.-B.); (D.H.-N.); (B.N.-P.); (S.P.)
| | - Telmo Blasco
- Tecnun, University of Navarra, Manuel de Lardizábal 13, 20018 San Sebastián, Spain; (T.B.); (F.B.); (I.A.); (F.J.P.)
| | - Francesco Balzerani
- Tecnun, University of Navarra, Manuel de Lardizábal 13, 20018 San Sebastián, Spain; (T.B.); (F.B.); (I.A.); (F.J.P.)
| | - Alberto Lerma-Aguilera
- Area de Genòmica i Salut, Fundació per al Foment de la Investigació Sanitària i Biomèdica de la Comunitat Valenciana (FISABIO-Salut Pública), 46020 València, Spain; (A.L.-A.); (D.L.); (M.P.F.)
| | - Daniel León
- Area de Genòmica i Salut, Fundació per al Foment de la Investigació Sanitària i Biomèdica de la Comunitat Valenciana (FISABIO-Salut Pública), 46020 València, Spain; (A.L.-A.); (D.L.); (M.P.F.)
| | - Silvia Pastoriza
- Centro de Investigación Biomédica, Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Granada, 18071 Granada, Spain; (S.P.-B.); (D.H.-N.); (B.N.-P.); (S.P.)
| | - Iñigo Apaolaza
- Tecnun, University of Navarra, Manuel de Lardizábal 13, 20018 San Sebastián, Spain; (T.B.); (F.B.); (I.A.); (F.J.P.)
| | - Francisco J. Planes
- Tecnun, University of Navarra, Manuel de Lardizábal 13, 20018 San Sebastián, Spain; (T.B.); (F.B.); (I.A.); (F.J.P.)
| | - Maria Pilar Francino
- Area de Genòmica i Salut, Fundació per al Foment de la Investigació Sanitària i Biomèdica de la Comunitat Valenciana (FISABIO-Salut Pública), 46020 València, Spain; (A.L.-A.); (D.L.); (M.P.F.)
- CIBER en Epidemiología y Salud Pública, 28001 Madrid, Spain
| | - José Ángel Rufián-Henares
- Centro de Investigación Biomédica, Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Granada, 18071 Granada, Spain; (S.P.-B.); (D.H.-N.); (B.N.-P.); (S.P.)
- Instituto de Investigación Biosanitaria ibs.GRANADA, Universidad de Granada, 18071 Granada, Spain
- Correspondence: ; Tel.: +34-958-24-28-41; Fax: +34-958-24-95-77
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10
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Valcárcel LV, Amundarain A, Kulis M, Charalampopoulou S, Melnick A, San Miguel J, Martín-Subero JI, Planes FJ, Agirre X, Prosper F. Gene expression derived from alternative promoters improves prognostic stratification in multiple myeloma. Leukemia 2021; 35:3012-3016. [PMID: 33972667 PMCID: PMC8478642 DOI: 10.1038/s41375-021-01263-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 11/05/2020] [Revised: 04/08/2021] [Accepted: 04/26/2021] [Indexed: 02/06/2023]
Abstract
Clinical and genetic risk factors are currently used in multiple myeloma (MM) to stratify patients and to design specific therapies. However, these systems do not capture the heterogeneity of the disease supporting the development of new prognostic factors. In this study, we identified active promoters and alternative active promoters in 6 different B cell subpopulations, including bone-marrow plasma cells, and 32 MM patient samples, using RNA-seq data. We find that expression initiated at both regular and alternative promoters was specific of each B cell subpopulation or MM plasma cells, showing a remarkable level of consistency with chromatin-based promoter definition. Interestingly, using 595 MM patient samples from the CoMMpass dataset, we observed that the expression derived from some alternative promoters was associated with lower progression-free and overall survival in MM patients independently of genetic alterations. Altogether, our results define cancer-specific alternative active promoters as new transcriptomic features that can provide a new avenue for prognostic stratification possibilities in patients with MM.
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Affiliation(s)
- Luis V. Valcárcel
- grid.5924.a0000000419370271Area de Oncología, Centro de Investigación Médica Aplicada (CIMA), Universidad de Navarra, IDISNA, Pamplona, Spain ,grid.5924.a0000000419370271Tecnun School of Engineering, Universidad de Navarra, San Sebastian, Spain
| | - Ane Amundarain
- grid.5924.a0000000419370271Area de Oncología, Centro de Investigación Médica Aplicada (CIMA), Universidad de Navarra, IDISNA, Pamplona, Spain ,grid.510933.d0000 0004 8339 0058Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Pamplona, Spain
| | - Marta Kulis
- grid.428756.a0000 0004 0412 0974Fundació Clínic per a la Recerca Biomèdica, Barcelona, Spain ,grid.10403.36Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Stella Charalampopoulou
- grid.428756.a0000 0004 0412 0974Fundació Clínic per a la Recerca Biomèdica, Barcelona, Spain ,grid.10403.36Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Ari Melnick
- grid.5386.8000000041936877XDivision of Hematology/Oncology, Department of Medicine, Weill Cornell Medical College, New York, NY USA
| | - Jesús San Miguel
- grid.5924.a0000000419370271Area de Oncología, Centro de Investigación Médica Aplicada (CIMA), Universidad de Navarra, IDISNA, Pamplona, Spain ,grid.510933.d0000 0004 8339 0058Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Pamplona, Spain ,grid.411730.00000 0001 2191 685XServicio de Hematología, Clínica Universidad de Navarra, Pamplona, Spain
| | - José I. Martín-Subero
- grid.510933.d0000 0004 8339 0058Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Pamplona, Spain ,grid.10403.36Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain ,grid.425902.80000 0000 9601 989XInstitució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain ,grid.5841.80000 0004 1937 0247Departamento de Fundamentos Clínicos, Universitat de Barcelona, Barcelona, Spain
| | - Francisco J. Planes
- grid.5924.a0000000419370271Tecnun School of Engineering, Universidad de Navarra, San Sebastian, Spain
| | - Xabier Agirre
- grid.5924.a0000000419370271Area de Oncología, Centro de Investigación Médica Aplicada (CIMA), Universidad de Navarra, IDISNA, Pamplona, Spain ,grid.510933.d0000 0004 8339 0058Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Pamplona, Spain
| | - Felipe Prosper
- grid.5924.a0000000419370271Area de Oncología, Centro de Investigación Médica Aplicada (CIMA), Universidad de Navarra, IDISNA, Pamplona, Spain ,grid.510933.d0000 0004 8339 0058Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Pamplona, Spain ,grid.411730.00000 0001 2191 685XServicio de Hematología, Clínica Universidad de Navarra, Pamplona, Spain
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11
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Valcárcel LV, Torrano V, Tobalina L, Carracedo A, Planes FJ. rMTA: robust metabolic transformation analysis. Bioinformatics 2020; 35:4350-4355. [PMID: 30923806 DOI: 10.1093/bioinformatics/btz231] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 03/15/2019] [Accepted: 03/27/2019] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION The development of computational tools exploiting -omics data and high-quality genome-scale metabolic networks for the identification of novel drug targets is a relevant topic in Systems Medicine. Metabolic Transformation Algorithm (MTA) is one of these tools, which aims to identify targets that transform a disease metabolic state back into a healthy state, with potential application in any disease where a clear metabolic alteration is observed. RESULTS Here, we present a robust extension to MTA (rMTA), which additionally incorporates a worst-case scenario analysis and minimization of metabolic adjustment to evaluate the beneficial effect of gene knockouts. We show that rMTA complements MTA in the different datasets analyzed (gene knockout perturbations in different organisms, Alzheimer's disease and prostate cancer), bringing a more accurate tool for predicting therapeutic targets. AVAILABILITY AND IMPLEMENTATION rMTA is freely available on The Cobra Toolbox: https://opencobra.github.io/cobratoolbox/latest/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Luis V Valcárcel
- Tecnun, University of Navarra, San Sebastián 20018, Spain
- Area de Hemato-Oncología, IDISNA, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen D-52074, Germany
| | - Verónica Torrano
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen D-52074, Germany
- CIC bioGUNE, Bizkaia Technology Park, Derio, Spain
| | - Luis Tobalina
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen D-52074, Germany
| | - Arkaitz Carracedo
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen D-52074, Germany
- CIC bioGUNE, Bizkaia Technology Park, Derio, Spain
- Ikerbasque, Basque foundation for science, Bilbao, Spain
- Biochemistry and Molecular Biology Department, University of the Basque Country (UPV/EHU), Bilbao E-48080, Spain
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12
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Cendoya X, Quevedo C, Ipiñazar M, Planes FJ. Computational approach for collection and prediction of molecular initiating events in developmental toxicity. Reprod Toxicol 2020; 94:55-64. [PMID: 32344110 DOI: 10.1016/j.reprotox.2020.03.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 03/04/2020] [Accepted: 03/20/2020] [Indexed: 02/06/2023]
Abstract
Developmental toxicity is defined as the occurrence of adverse effects on the developing organism as a result from exposure to a toxic agent. These alterations can have long-term acute effects. Current in vitro models present important limitations and the evaluation of toxicity is not entirely objective. In silico methods have also shown limited success, in part due to complex and varied mechanisms of action that mediate developmental toxicity, which are sometimes poorly understood. In this article, we compiled a dataset of compounds with developmental toxicity categories and annotated mechanisms of action for both toxic and non-toxic compounds (DVTOX). With it, we selected a panel of protein targets that might be part of putative Molecular Initiating Events (MIEs) of Adverse Outcome Pathways of developmental toxicity. The validity of this list of candidate MIEs was studied through the evaluation of new drug-target relationships that include such proteins, but were not part of the original database. Finally, an orthology analysis of this protein panel was conducted to select an appropriate animal model to assess developmental toxicity. We tested our approach using the zebrafish embryo toxicity test, finding positive results.
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Affiliation(s)
- Xabier Cendoya
- TECNUN, University of Navarra, San Sebastian, 20018, Spain
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13
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Ponce-de-León M, Apaolaza I, Valencia A, Planes FJ. On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models. Bioinformatics 2020; 36:1986-1988. [PMID: 31722383 PMCID: PMC8662768 DOI: 10.1093/bioinformatics/btz832] [Citation(s) in RCA: 4] [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: 03/08/2019] [Revised: 10/22/2019] [Accepted: 11/12/2019] [Indexed: 11/12/2022] Open
Abstract
Supplementary information:Supplementary data are
available at Bioinformatics online.
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Affiliation(s)
- Miguel Ponce-de-León
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Iñigo Apaolaza
- Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
| | - Alfonso Valencia
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain.,ICREA, Barcelona 08010, Spain
| | - Francisco J Planes
- Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
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14
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Apaolaza I, Valcarcel LV, Planes FJ. gMCS: fast computation of genetic minimal cut sets in large networks. Bioinformatics 2019; 35:535-537. [PMID: 30052768 DOI: 10.1093/bioinformatics/bty656] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 07/19/2018] [Indexed: 11/14/2022] Open
Abstract
Motivation The identification of minimal gene knockout strategies to engineer metabolic systems constitutes one of the most relevant applications of the COnstraint-Based Reconstruction and Analysis (COBRA) framework. In the last years, the minimal cut sets (MCSs) approach has emerged as a promising tool to carry out this task. However, MCSs define reaction knockout strategies, which are not necessarily transformed into feasible strategies at the gene level. Results We present a more general, easy-to-use and efficient computational implementation of a previously published algorithm to calculate MCSs to the gene level (gMCSs). Our tool was compared with existing methods in order to calculate essential genes and synthetic lethals in metabolic networks of different complexity, showing a significant reduction in model size and computation time. Availability and implementation gMCS is publicly and freely available under GNU license in the COBRA toolbox (https://github.com/opencobra/cobratoolbox/tree/master/src/analysis/gMCS). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Iñigo Apaolaza
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián, Spain
| | - Luis Vitores Valcarcel
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián, Spain.,Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Programa de Oncohematología, Pamplona, Spain
| | - Francisco J Planes
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián, Spain
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15
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Fuertes A, Pérez-Burillo S, Apaolaza I, Vallès Y, Francino MP, Rufián-Henares JÁ, Planes FJ. Adaptation of the Human Gut Microbiota Metabolic Network During the First Year After Birth. Front Microbiol 2019; 10:848. [PMID: 31105659 PMCID: PMC6491881 DOI: 10.3389/fmicb.2019.00848] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [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: 12/11/2018] [Accepted: 04/02/2019] [Indexed: 12/11/2022] Open
Abstract
Predicting the metabolic behavior of the human gut microbiota in different contexts is one of the most promising areas of constraint-based modeling. Recently, we presented a supra-organismal approach to build context-specific metabolic networks of bacterial communities using functional and taxonomic assignments of meta-omics data. In this work, this algorithm is applied to elucidate the metabolic changes induced over the first year after birth in the gut microbiota of a cohort of Spanish infants. We used metagenomics data of fecal samples and nutritional data of 13 infants at five time points. The resulting networks for each time point were analyzed, finding significant alterations once solid food is introduced in the diet. Our work shows that solid food leads to a different pattern of output metabolites that can be potentially released from the gut microbiota to the host. Experimental validation is presented for ferulate, a neuroprotective metabolite involved in the gut-brain axis.
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Affiliation(s)
- Alvaro Fuertes
- Bioinformatics Group, TECNUN School of Engineering, Department of Biomedical Engineering and Sciences, Universidad de Navarra, San Sebastián, Spain.,Bioinformatics Group, CEIT, Water and Health Division, Universidad de Navarra, San Sebastián, Spain
| | - Sergio Pérez-Burillo
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Iñigo Apaolaza
- Bioinformatics Group, TECNUN School of Engineering, Department of Biomedical Engineering and Sciences, Universidad de Navarra, San Sebastián, Spain.,Bioinformatics Group, CEIT, Water and Health Division, Universidad de Navarra, San Sebastián, Spain
| | - Yvonne Vallès
- Unitat Mixta d'Investigació en Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública/Instituto de Biología Integrativa de Sistemas, Universitat de València, Valencia, Spain.,Department of Biological and Chemical Sciences, The University of the West Indies, Cave Hill, Barbados
| | - M Pilar Francino
- Unitat Mixta d'Investigació en Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública/Instituto de Biología Integrativa de Sistemas, Universitat de València, Valencia, Spain.,CIBER en Epidemiología y Salud Pública, Madrid, Spain
| | - José Ángel Rufián-Henares
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain.,Instituto de Investigación Biosanitaria ibs.GRANADA, Universidad de Granada, Granada, Spain
| | - Francisco J Planes
- Bioinformatics Group, TECNUN School of Engineering, Department of Biomedical Engineering and Sciences, Universidad de Navarra, San Sebastián, Spain.,Bioinformatics Group, CEIT, Water and Health Division, Universidad de Navarra, San Sebastián, Spain
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16
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Carazo F, Campuzano L, Cendoya X, Planes FJ, Rubio A. TranscriptAchilles: a genome-wide platform to predict isoform biomarkers of gene essentiality in cancer. Gigascience 2019; 8:giz021. [PMID: 30942869 PMCID: PMC6446222 DOI: 10.1093/gigascience/giz021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [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: 09/14/2018] [Revised: 12/18/2018] [Accepted: 02/07/2019] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Aberrant alternative splicing plays a key role in cancer development. In recent years, alternative splicing has been used as a prognosis biomarker, a therapy response biomarker, and even as a therapeutic target. Next-generation RNA sequencing has an unprecedented potential to measure the transcriptome. However, due to the complexity of dealing with isoforms, the scientific community has not sufficiently exploited this valuable resource in precision medicine. FINDINGS We present TranscriptAchilles, the first large-scale tool to predict transcript biomarkers associated with gene essentiality in cancer. This application integrates 412 loss-of-function RNA interference screens of >17,000 genes, together with their corresponding whole-transcriptome expression profiling. Using this tool, we have studied which are the cancer subtypes for which alternative splicing plays a significant role to state gene essentiality. In addition, we include a case study of renal cell carcinoma that shows the biological soundness of the results. The databases, the source code, and a guide to build the platform within a Docker container are available at GitLab. The application is also available online. CONCLUSIONS TranscriptAchilles provides a user-friendly web interface to identify transcript or gene biomarkers of gene essentiality, which could be used as a starting point for a drug development project. This approach opens a wide range of translational applications in cancer.
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Affiliation(s)
- Fernando Carazo
- Tecnun (University of Navarra), Paseo Manuel Lardizábal 15, 20018 San Sebastián, Spain. Department of Biomedical Engineering and Sciences
| | - Lucía Campuzano
- University of Luxembourg, 2, avenue de l'Université, 4365 Esch-sur-Alzette, Luxembourg
| | - Xabier Cendoya
- Tecnun (University of Navarra), Paseo Manuel Lardizábal 15, 20018 San Sebastián, Spain. Department of Biomedical Engineering and Sciences
| | - Francisco J Planes
- Tecnun (University of Navarra), Paseo Manuel Lardizábal 15, 20018 San Sebastián, Spain. Department of Biomedical Engineering and Sciences
| | - Angel Rubio
- Tecnun (University of Navarra), Paseo Manuel Lardizábal 15, 20018 San Sebastián, Spain. Department of Biomedical Engineering and Sciences
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17
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Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, Haraldsdóttir HS, Wachowiak J, Keating SM, Vlasov V, Magnusdóttir S, Ng CY, Preciat G, Žagare A, Chan SHJ, Aurich MK, Clancy CM, Modamio J, Sauls JT, Noronha A, Bordbar A, Cousins B, El Assal DC, Valcarcel LV, Apaolaza I, Ghaderi S, Ahookhosh M, Ben Guebila M, Kostromins A, Sompairac N, Le HM, Ma D, Sun Y, Wang L, Yurkovich JT, Oliveira MAP, Vuong PT, El Assal LP, Kuperstein I, Zinovyev A, Hinton HS, Bryant WA, Aragón Artacho FJ, Planes FJ, Stalidzans E, Maass A, Vempala S, Hucka M, Saunders MA, Maranas CD, Lewis NE, Sauter T, Palsson BØ, Thiele I, Fleming RMT. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat Protoc 2019; 14:639-702. [PMID: 30787451 PMCID: PMC6635304 DOI: 10.1038/s41596-018-0098-2] [Citation(s) in RCA: 565] [Impact Index Per Article: 113.0] [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] [Indexed: 12/23/2022]
Abstract
Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
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Affiliation(s)
- Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sylvain Arreckx
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Thomas Pfau
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Sebastián N Mendoza
- Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile
- Mathomics, Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | - Anne Richelle
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Hulda S Haraldsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jacek Wachowiak
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sarah M Keating
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
| | - Vanja Vlasov
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Stefania Magnusdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Chiam Yu Ng
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - German Preciat
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Alise Žagare
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Siu H J Chan
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Maike K Aurich
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Catherine M Clancy
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jennifer Modamio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - John T Sauls
- Department of Physics, and Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Alberto Noronha
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | | | - Benjamin Cousins
- Algorithms and Randomness Center, School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Diana C El Assal
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Luis V Valcarcel
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Iñigo Apaolaza
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Susan Ghaderi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Masoud Ahookhosh
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Marouen Ben Guebila
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Andrejs Kostromins
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Hoai M Le
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ding Ma
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Yuekai Sun
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - James T Yurkovich
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Miguel A P Oliveira
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Phan T Vuong
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Lemmer P El Assal
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Inna Kuperstein
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - H Scott Hinton
- Utah State University Research Foundation, North Logan, UT, USA
| | - William A Bryant
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, UK
| | | | - Francisco J Planes
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Egils Stalidzans
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Alejandro Maass
- Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile
- Mathomics, Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | - Santosh Vempala
- Algorithms and Randomness Center, School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Michael A Saunders
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Bernhard Ø Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Lyngby, Denmark
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ronan M T Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, The Netherlands.
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18
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Cortazar AR, Torrano V, Martín-Martín N, Caro-Maldonado A, Camacho L, Hermanova I, Guruceaga E, Lorenzo-Martín LF, Caloto R, Gomis RR, Apaolaza I, Quesada V, Trka J, Gomez-Muñoz A, Vincent S, Bustelo XR, Planes FJ, Aransay AM, Carracedo A. CANCERTOOL: A Visualization and Representation Interface to Exploit Cancer Datasets. Cancer Res 2018; 78:6320-6328. [PMID: 30232219 DOI: 10.1158/0008-5472.can-18-1669] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 08/02/2018] [Accepted: 09/06/2018] [Indexed: 11/16/2022]
Abstract
With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design.Significance: In order to facilitate access of research groups without bioinformatics support to public transcriptomics data, we have developed a free online tool with an easy-to-use interface that allows researchers to obtain quality information in a readily publishable format. Cancer Res; 78(21); 6320-8. ©2018 AACR.
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Affiliation(s)
- Ana R Cortazar
- CIC bioGUNE, Bizkaia Technology Park, Bizkaia, Spain.,CIBERONC, Madrid, Spain
| | - Veronica Torrano
- CIC bioGUNE, Bizkaia Technology Park, Bizkaia, Spain.,CIBERONC, Madrid, Spain
| | | | | | - Laura Camacho
- CIC bioGUNE, Bizkaia Technology Park, Bizkaia, Spain.,Biochemistry and Molecular Biology Department, University of the Basque Country (UPV/EHU), Bilbao, Spain
| | | | - Elizabeth Guruceaga
- CIBERONC, Madrid, Spain.,Bioinformatics Unit, Center for Applied Medical Research, University of Navarra, Pamplona, Spain
| | - Luis F Lorenzo-Martín
- CIBERONC, Madrid, Spain.,Centro de Investigación del Cáncer, Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca, Salamanca, Spain.,Instituto de Biología Molecular y Celular del Cáncer, Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca, Salamanca, Spain
| | - Ruben Caloto
- CIBERONC, Madrid, Spain.,Centro de Investigación del Cáncer, Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca, Salamanca, Spain.,Instituto de Biología Molecular y Celular del Cáncer, Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca, Salamanca, Spain
| | - Roger R Gomis
- CIBERONC, Madrid, Spain.,Oncology Programme, Institute for Research in Biomedicine (IRB-Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Iñigo Apaolaza
- University of Navarra, Tecnun School of Engineering, San Sebastián, Spain
| | - Victor Quesada
- CIBERONC, Madrid, Spain.,Departamento de Bioquímica y Biología Molecular, Universidad de Oviedo, Oviedo, Spain
| | - Jan Trka
- CLIP-Childhood Leukaemia Investigation Prague and Second Faculty of Medicine, Charles University, Prague, Czech Republic.,University Hospital Motol, Prague, Czech Republic
| | - Antonio Gomez-Muñoz
- Biochemistry and Molecular Biology Department, University of the Basque Country (UPV/EHU), Bilbao, Spain
| | - Silvestre Vincent
- CIBERONC, Madrid, Spain.,University of Navarra, Department of Histology and Pathology, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,Center for Applied Medical Research, Program of Solid Tumors, University of Navarra, Pamplona, Spain
| | - Xose R Bustelo
- CIBERONC, Madrid, Spain.,Centro de Investigación del Cáncer, Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca, Salamanca, Spain.,Instituto de Biología Molecular y Celular del Cáncer, Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca, Salamanca, Spain
| | - Francisco J Planes
- University of Navarra, Tecnun School of Engineering, San Sebastián, Spain
| | - Ana M Aransay
- CIC bioGUNE, Bizkaia Technology Park, Bizkaia, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
| | - Arkaitz Carracedo
- CIC bioGUNE, Bizkaia Technology Park, Bizkaia, Spain. .,CIBERONC, Madrid, Spain.,Biochemistry and Molecular Biology Department, University of the Basque Country (UPV/EHU), Bilbao, Spain.,Ikerbasque, Basque Foundation for Science, Bilbao, Spain
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19
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Apaolaza I, José-Eneriz ES, Agirre X, Prósper F, Planes FJ. COBRA methods and metabolic drug targets in cancer. Mol Cell Oncol 2017; 5:e1389672. [PMID: 29404389 PMCID: PMC5791858 DOI: 10.1080/23723556.2017.1389672] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [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: 09/29/2017] [Revised: 10/03/2017] [Accepted: 10/05/2017] [Indexed: 02/09/2023]
Abstract
The identification of therapeutic strategies exploiting the metabolic alterations of malignant cells is a relevant area in cancer research. Here, we discuss a novel computational method, based on the COBRA (COnstraint-Based Reconstruction and Analysis) framework for metabolic networks, to perform this task. Current and future steps are presented.
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Affiliation(s)
- Iñigo Apaolaza
- CEIT and Tecnun, University of Navarra, Manuel de Lardizábal 13, San Sebastián, Spain
| | - Edurne San José-Eneriz
- Area de Hemato-Oncología, IDISNA, Ciberonc, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pío XII 55, Pamplona, Spain
| | - Xabier Agirre
- Area de Hemato-Oncología, IDISNA, Ciberonc, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pío XII 55, Pamplona, Spain
| | - Felipe Prósper
- Area de Hemato-Oncología, IDISNA, Ciberonc, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pío XII 55, Pamplona, Spain
| | - Francisco J Planes
- CEIT and Tecnun, University of Navarra, Manuel de Lardizábal 13, San Sebastián, Spain
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20
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Quevedo C, Ipiñazar M, Planes FJ, Cendoya X. In silico platform based on bioinformatic and chemoinformatic data to complement zebrafish embryo teratogenicity test. Toxicol Lett 2017. [DOI: 10.1016/j.toxlet.2017.07.338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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21
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Apaolaza I, San José-Eneriz E, Tobalina L, Miranda E, Garate L, Agirre X, Prósper F, Planes FJ. An in-silico approach to predict and exploit synthetic lethality in cancer metabolism. Nat Commun 2017; 8:459. [PMID: 28878380 PMCID: PMC5587678 DOI: 10.1038/s41467-017-00555-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [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: 12/14/2016] [Accepted: 07/07/2017] [Indexed: 02/02/2023] Open
Abstract
Synthetic lethality is a promising concept in cancer research, potentially opening new possibilities for the development of more effective and selective treatments. Here, we present a computational method to predict and exploit synthetic lethality in cancer metabolism. Our approach relies on the concept of genetic minimal cut sets and gene expression data, demonstrating a superior performance to previous approaches predicting metabolic vulnerabilities in cancer. Our genetic minimal cut set computational framework is applied to evaluate the lethality of ribonucleotide reductase catalytic subunit M1 (RRM1) inhibition in multiple myeloma. We present a computational and experimental study of the effect of RRM1 inhibition in four multiple myeloma cell lines. In addition, using publicly available genome-scale loss-of-function screens, a possible mechanism by which the inhibition of RRM1 is effective in cancer is established. Overall, our approach shows promising results and lays the foundation to build a novel family of algorithms to target metabolism in cancer. Exploiting synthetic lethality is a promising approach for cancer therapy. Here, the authors present an approach to identifying such interactions by finding genetic minimal cut sets (gMCSs) that block cancer proliferation, and apply it to study the lethality of RRM1 inhibition in multiple myeloma.
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Affiliation(s)
- Iñigo Apaolaza
- CEIT and Tecnun, University of Navarra, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Edurne San José-Eneriz
- Area de Hemato-Oncología, IDISNA, Ciberonc, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pío XII 55, 31008, Pamplona, Spain
| | - Luis Tobalina
- CEIT and Tecnun, University of Navarra, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, MTI2 Wendlingweg 2, D-52074, Aachen, Germany
| | - Estíbaliz Miranda
- Area de Hemato-Oncología, IDISNA, Ciberonc, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pío XII 55, 31008, Pamplona, Spain
| | - Leire Garate
- Area de Hemato-Oncología, IDISNA, Ciberonc, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pío XII 55, 31008, Pamplona, Spain
| | - Xabier Agirre
- Area de Hemato-Oncología, IDISNA, Ciberonc, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pío XII 55, 31008, Pamplona, Spain
| | - Felipe Prósper
- Area de Hemato-Oncología, IDISNA, Ciberonc, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pío XII 55, 31008, Pamplona, Spain.
| | - Francisco J Planes
- CEIT and Tecnun, University of Navarra, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.
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Tobalina L, Pey J, Rezola A, Planes FJ. Assessment of FBA Based Gene Essentiality Analysis in Cancer with a Fast Context-Specific Network Reconstruction Method. PLoS One 2016; 11:e0154583. [PMID: 27145226 PMCID: PMC4856428 DOI: 10.1371/journal.pone.0154583] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2015] [Accepted: 04/15/2016] [Indexed: 01/28/2023] Open
Abstract
MOTIVATION Gene Essentiality Analysis based on Flux Balance Analysis (FBA-based GEA) is a promising tool for the identification of novel metabolic therapeutic targets in cancer. The reconstruction of cancer-specific metabolic networks, typically based on gene expression data, constitutes a sensible step in this approach. However, to our knowledge, no extensive assessment on the influence of the reconstruction process on the obtained results has been carried out to date. RESULTS In this article, we aim to study context-specific networks and their FBA-based GEA results for the identification of cancer-specific metabolic essential genes. To that end, we used gene expression datasets from the Cancer Cell Line Encyclopedia (CCLE), evaluating the results obtained in 174 cancer cell lines. In order to more clearly observe the effect of cancer-specific expression data, we did the same analysis using randomly generated expression patterns. Our computational analysis showed some essential genes that are fairly common in the reconstructions derived from both gene expression and randomly generated data. However, though of limited size, we also found a subset of essential genes that are very rare in the randomly generated networks, while recurrent in the sample derived networks, and, thus, would presumably constitute relevant drug targets for further analysis. In addition, we compare the in-silico results to high-throughput gene silencing experiments from Project Achilles with conflicting results, which leads us to raise several questions, particularly the strong influence of the selected biomass reaction on the obtained results. Notwithstanding, using previous literature in cancer research, we evaluated the most relevant of our targets in three different cancer cell lines, two derived from Gliobastoma Multiforme and one from Non-Small Cell Lung Cancer, finding that some of the predictions are in the right track.
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Affiliation(s)
- Luis Tobalina
- CEIT and Tecnun (University of Navarra), Manuel de Lardizábal 15, 20018, San Sebastian, Spain
| | - Jon Pey
- CEIT and Tecnun (University of Navarra), Manuel de Lardizábal 15, 20018, San Sebastian, Spain
| | - Alberto Rezola
- CEIT and Tecnun (University of Navarra), Manuel de Lardizábal 15, 20018, San Sebastian, Spain
| | - Francisco J. Planes
- CEIT and Tecnun (University of Navarra), Manuel de Lardizábal 15, 20018, San Sebastian, Spain
- * E-mail:
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Tobalina L, Pey J, Planes FJ. Direct calculation of minimal cut sets involving a specific reaction knock-out. Bioinformatics 2016; 32:2001-7. [PMID: 27153694 DOI: 10.1093/bioinformatics/btw072] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Accepted: 02/02/2016] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION The concept of Minimal Cut Sets (MCSs) is used in metabolic network modeling to describe minimal groups of reactions or genes whose simultaneous deletion eliminates the capability of the network to perform a specific task. Previous work showed that MCSs where closely related to Elementary Flux Modes (EFMs) in a particular dual problem, opening up the possibility to use the tools developed for computing EFMs to compute MCSs. Until recently, however, there existed no method to compute an EFM with some specific characteristic, meaning that, in the case of MCSs, the only strategy to obtain them was to enumerate them using, for example, the standard K-shortest EFMs algorithm. RESULTS In this work, we adapt the recently developed theory to compute EFMs satisfying several constraints to the calculation of MCSs involving a specific reaction knock-out. Importantly, we emphasize that not all the EFMs in the dual problem correspond to real MCSs, and propose a new formulation capable of correctly identifying the MCS wanted. Furthermore, this formulation brings interesting insights about the relationship between the primal and the dual problem of the MCS computation. AVAILABILITY AND IMPLEMENTATION A Matlab-Cplex implementation of the proposed algorithm is available as a supplementary material CONTACT fplanes@ceit.es SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Luis Tobalina
- CEIT and Tecnun, University of Navarra, San Sebastián 20018, Spain
| | - Jon Pey
- CEIT and Tecnun, University of Navarra, San Sebastián 20018, Spain
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Tobalina L, Bargiela R, Pey J, Herbst FA, Lores I, Rojo D, Barbas C, Peláez AI, Sánchez J, von Bergen M, Seifert J, Ferrer M, Planes FJ. Context-specific metabolic network reconstruction of a naphthalene-degrading bacterial community guided by metaproteomic data. ACTA ACUST UNITED AC 2015; 31:1771-9. [PMID: 25618865 DOI: 10.1093/bioinformatics/btv036] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Accepted: 01/18/2015] [Indexed: 12/21/2022]
Abstract
MOTIVATION With the advent of meta-'omics' data, the use of metabolic networks for the functional analysis of microbial communities became possible. However, while network-based methods are widely developed for single organisms, their application to bacterial communities is currently limited. RESULTS Herein, we provide a novel, context-specific reconstruction procedure based on metaproteomic and taxonomic data. Without previous knowledge of a high-quality, genome-scale metabolic networks for each different member in a bacterial community, we propose a meta-network approach, where the expression levels and taxonomic assignments of proteins are used as the most relevant clues for inferring an active set of reactions. Our approach was applied to draft the context-specific metabolic networks of two different naphthalene-enriched communities derived from an anthropogenically influenced, polyaromatic hydrocarbon contaminated soil, with (CN2) or without (CN1) bio-stimulation. We were able to capture the overall functional differences between the two conditions at the metabolic level and predict an important activity for the fluorobenzoate degradation pathway in CN1 and for geraniol metabolism in CN2. Experimental validation was conducted, and good agreement with our computational predictions was observed. We also hypothesize different pathway organizations at the organismal level, which is relevant to disentangle the role of each member in the communities. The approach presented here can be easily transferred to the analysis of genomic, transcriptomic and metabolomic data.
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Affiliation(s)
- Luis Tobalina
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Rafael Bargiela
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Jon Pey
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Florian-Alexander Herbst
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Iván Lores
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - David Rojo
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Coral Barbas
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Ana I Peláez
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Jesús Sánchez
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Martin von Bergen
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Jana Seifert
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Manuel Ferrer
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Francisco J Planes
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
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Pey J, Villar JA, Tobalina L, Rezola A, García JM, Beasley JE, Planes FJ. TreeEFM: calculating elementary flux modes using linear optimization in a tree-based algorithm. ACTA ACUST UNITED AC 2014; 31:897-904. [PMID: 25380956 DOI: 10.1093/bioinformatics/btu733] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Elementary flux modes (EFMs) analysis constitutes a fundamental tool in systems biology. However, the efficient calculation of EFMs in genome-scale metabolic networks (GSMNs) is still a challenge. We present a novel algorithm that uses a linear programming-based tree search and efficiently enumerates a subset of EFMs in GSMNs. RESULTS Our approach is compared with the EFMEvolver approach, demonstrating a significant improvement in computation time. We also validate the usefulness of our new approach by studying the acetate overflow metabolism in the Escherichia coli bacteria. To do so, we computed 1 million EFMs for each energetic amino acid and then analysed the relevance of each energetic amino acid based on gene/protein expression data and the obtained EFMs. We found good agreement between previous experiments and the conclusions reached using EFMs. Finally, we also analysed the performance of our approach when applied to large GSMNs. AVAILABILITY AND IMPLEMENTATION The stand-alone software TreeEFM is implemented in C++ and interacts with the open-source linear solver COIN-OR Linear program Solver (CLP).
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Affiliation(s)
- Jon Pey
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - Juan A Villar
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - Luis Tobalina
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - Alberto Rezola
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - José Manuel García
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - John E Beasley
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - Francisco J Planes
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
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Rezola A, Pey J, Rubio Á, Planes FJ. In-silico prediction of key metabolic differences between two non-small cell lung cancer subtypes. PLoS One 2014; 9:e103998. [PMID: 25093336 PMCID: PMC4122379 DOI: 10.1371/journal.pone.0103998] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [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: 02/06/2014] [Accepted: 07/09/2014] [Indexed: 01/11/2023] Open
Abstract
Metabolism expresses the phenotype of living cells and understanding it is crucial for different applications in biotechnology and health. With the increasing availability of metabolomic, proteomic and, to a larger extent, transcriptomic data, the elucidation of specific metabolic properties in different scenarios and cell types is a key topic in systems biology. Despite the potential of the elementary flux mode (EFM) concept for this purpose, its use has been limited so far, mainly because their computation has been infeasible for genome-scale metabolic networks. In a recent work, we determined a subset of EFMs in human metabolism and proposed a new protocol to integrate gene expression data, spotting key 'characteristic EFMs' in different scenarios. Our approach was successfully applied to identify metabolic differences among several human healthy tissues. In this article, we evaluated the performance of our approach in clinically interesting situation. In particular, we identified key EFMs and metabolites in adenocarcinoma and squamous-cell carcinoma subtypes of non-small cell lung cancers. Results are consistent with previous knowledge of these major subtypes of lung cancer in the medical literature. Therefore, this work constitutes the starting point to establish a new methodology that could lead to distinguish key metabolic processes among different clinical outcomes.
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Affiliation(s)
- Alberto Rezola
- Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain
| | - Jon Pey
- Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain
| | - Ángel Rubio
- Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain
| | - Francisco J. Planes
- Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain
- * E-mail:
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Pey J, Planes FJ. Direct calculation of elementary flux modes satisfying several biological constraints in genome-scale metabolic networks. ACTA ACUST UNITED AC 2014; 30:2197-203. [PMID: 24728852 DOI: 10.1093/bioinformatics/btu193] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION The concept of Elementary Flux Mode (EFM) has been widely used for the past 20 years. However, its application to genome-scale metabolic networks (GSMNs) is still under development because of methodological limitations. Therefore, novel approaches are demanded to extend the application of EFMs. A novel family of methods based on optimization is emerging that provides us with a subset of EFMs. Because the calculation of the whole set of EFMs goes beyond our capacity, performing a selective search is a proper strategy. RESULTS Here, we present a novel mathematical approach calculating EFMs fulfilling additional linear constraints. We validated our approach based on two metabolic networks in which all the EFMs can be obtained. Finally, we analyzed the performance of our methodology in the GSMN of the yeast Saccharomyces cerevisiae by calculating EFMs producing ethanol with a given minimum carbon yield. Overall, this new approach opens new avenues for the calculation of EFMs in GSMNs. AVAILABILITY AND IMPLEMENTATION Matlab code is provided in the supplementary online materials CONTACT fplanes@ceit.es. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jon Pey
- CEIT and TECNUN, University of Navarra, 20018 San Sebastian, Spain
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Pey J, Valgepea K, Rubio A, Beasley JE, Planes FJ. Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways. BMC Syst Biol 2013; 7:134. [PMID: 24314206 PMCID: PMC3878952 DOI: 10.1186/1752-0509-7-134] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Accepted: 11/27/2013] [Indexed: 12/26/2022]
Abstract
Background The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. Results We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. Conclusions A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.
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Affiliation(s)
| | | | | | - John E Beasley
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain.
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Abstract
MOTIVATION Pathway analysis tools are a powerful strategy to analyze 'omics' data in the field of systems biology. From a metabolic perspective, several pathway definitions can be found in the literature, each one appropriate for a particular study. Recently, a novel pathway concept termed carbon flux paths (CFPs) was introduced and benchmarked against existing approaches, showing a clear advantage for finding linear pathways from a given source to target metabolite. CFPs are simple paths in a metabolite-metabolite graph that satisfy typical constraints in stoichiometric models: mass balancing and thermodynamics (irreversibility). In addition, CFPs guarantee carbon exchange in each of their intermediate steps, but not between the source and the target metabolites and consequently false positive solutions may arise. These pathways often lack biological interest, particularly when studying biosynthetic or degradation routes of a metabolite. To overcome this issue, we amend the formulation in CFP, so as to account for atomic fate information. This approach is termed atomic CFP (aCFP). RESULTS By means of a side-by-side comparison in a medium scale metabolic network in Escherichia Coli, we show that aCFP provides more biologically relevant pathways than CFP, because canonical pathways are more easily recovered, which reflects the benefits of removing false positives. In addition, we demonstrate that aCFP can be successfully applied to genome-scale metabolic networks. As the quality of genome-scale atomic reconstruction is improved, methods such as the one presented here will undoubtedly be of value to interpret 'omics' data.
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Affiliation(s)
- Jon Pey
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH, Uxbridge, UK
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Seifert J, Herbst FA, Halkjaer Nielsen P, Planes FJ, Jehmlich N, Ferrer M, von Bergen M. Bioinformatic progress and applications in metaproteogenomics for bridging the gap between genomic sequences and metabolic functions in microbial communities. Proteomics 2013; 13:2786-804. [PMID: 23625762 DOI: 10.1002/pmic.201200566] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2012] [Revised: 03/07/2013] [Accepted: 03/28/2013] [Indexed: 11/06/2022]
Abstract
Metaproteomics of microbial communities promises to add functional information to the blueprint of genes derived from metagenomics. Right from its beginning, the achievements and developments in metaproteomics were closely interlinked with metagenomics. In addition, the evaluation, visualization, and interpretation of metaproteome data demanded for the developments in bioinformatics. This review will give an overview about recent strategies to use genomic data either from public databases or organismal specific genomes/metagenomes to increase the number of identified proteins obtained by mass spectrometric measurements. We will review different published metaproteogenomic approaches in respect to the used MS pipeline and to the used protein identification workflow. Furthermore, different approaches of data visualization and strategies for phylogenetic interpretation of metaproteome data are discussed as well as approaches for functional mapping of the results to the investigated biological systems. This information will in the end allow a comprehensive analysis of interactions and interdependencies within microbial communities.
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Affiliation(s)
- Jana Seifert
- Department of Proteomics, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany; Institute of Animal Nutrition, University of Hohenheim, Stuttgart, Germany
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Pey J, Tobalina L, de Cisneros JPJ, Planes FJ. A network-based approach for predicting key enzymes explaining metabolite abundance alterations in a disease phenotype. BMC Syst Biol 2013; 7:62. [PMID: 23870038 PMCID: PMC3733687 DOI: 10.1186/1752-0509-7-62] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2012] [Accepted: 07/16/2013] [Indexed: 12/28/2022]
Abstract
Background The study of metabolism has attracted much attention during the last years due to its relevance in various diseases. The advance in metabolomics platforms allows us to detect an increasing number of metabolites in abnormal high/low concentration in a disease phenotype. Finding a mechanistic interpretation for these alterations is important to understand pathophysiological processes, however it is not an easy task. The availability of genome scale metabolic networks and Systems Biology techniques open new avenues to address this question. Results In this article we present a novel mathematical framework to find enzymes whose malfunction explains the accumulation/depletion of a given metabolite in a disease phenotype. Our approach is based on a recently introduced pathway concept termed Carbon Flux Paths (CFPs), which extends classical topological definition by including network stoichiometry. Using CFPs, we determine the Connectivity Curve of an altered metabolite, which allows us to quantify changes in its pathway structure when a certain enzyme is removed. The influence of enzyme removal is then ranked and used to explain the accumulation/depletion of such metabolite. For illustration, we center our study in the accumulation of two metabolites (L-Cystine and Homocysteine) found in high concentration in the brain of patients with mental disorders. Our results were discussed based on literature and found a good agreement with previously reported mechanisms. In addition, we hypothesize a novel role of several enzymes for the accumulation of these metabolites, which opens new strategies to understand the metabolic processes underlying these diseases. Conclusions With personalized medicine on the horizon, metabolomic platforms are providing us with a vast amount of experimental data for a number of complex diseases. Our approach provides a novel apparatus to rationally investigate and understand metabolite alterations under disease phenotypes. This work contributes to the development of Systems Medicine, whose objective is to answer clinical questions based on theoretical methods and high-throughput “omics” data.
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Affiliation(s)
- Jon Pey
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, San Sebastian 20018, Spain
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Rezola A, Pey J, de Figueiredo LF, Podhorski A, Schuster S, Rubio A, Planes FJ. Selection of human tissue-specific elementary flux modes using gene expression data. ACTA ACUST UNITED AC 2013; 29:2009-16. [PMID: 23742984 DOI: 10.1093/bioinformatics/btt328] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION The analysis of high-throughput molecular data in the context of metabolic pathways is essential to uncover their underlying functional structure. Among different metabolic pathway concepts in systems biology, elementary flux modes (EFMs) hold a predominant place, as they naturally capture the complexity and plasticity of cellular metabolism and go beyond predefined metabolic maps. However, their use to interpret high-throughput data has been limited so far, mainly because their computation in genome-scale metabolic networks has been unfeasible. To face this issue, different optimization-based techniques have been recently introduced and their application to human metabolism is promising. RESULTS In this article, we exploit and generalize the K-shortest EFM algorithm to determine a subset of EFMs in a human genome-scale metabolic network. This subset of EFMs involves a wide number of reported human metabolic pathways, as well as potential novel routes, and constitutes a valuable database where high-throughput data can be mapped and contextualized from a metabolic perspective. To illustrate this, we took expression data of 10 healthy human tissues from a previous study and predicted their characteristic EFMs based on enrichment analysis. We used a multivariate hypergeometric test and showed that it leads to more biologically meaningful results than standard hypergeometric. Finally, a biological discussion on the characteristic EFMs obtained in liver is conducted, finding a high level of agreement when compared with the literature.
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Affiliation(s)
- Alberto Rezola
- Biomedical Engineering Department, CEIT and Tecnun, University of Navarra, San Sebastian, Spain
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Pey J, Rubio A, Theodoropoulos C, Cascante M, Planes FJ. Integrating tracer-based metabolomics data and metabolic fluxes in a linear fashion via Elementary Carbon Modes. Metab Eng 2012; 14:344-53. [DOI: 10.1016/j.ymben.2012.03.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2011] [Revised: 03/01/2012] [Accepted: 03/26/2012] [Indexed: 01/10/2023]
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Abstract
miRNAs are small RNA molecules ('22 nt) that interact with their target mRNAs inhibiting translation or/and cleavaging the target mRNA. This interaction is guided by sequence complentarity and results in the reduction of mRNA and/or protein levels. miRNAs are involved in key biological processes and different diseases. Therefore, deciphering miRNA targets is crucial for diagnostics and therapeutics. However, miRNA regulatory mechanisms are complex and there is still no high-throughput and low-cost miRNA target screening technique. In recent years, several computational methods based on sequence complementarity of the miRNA and the mRNAs have been developed. However, the predicted interactions using these computational methods are inconsistent and the expected false positive rates are still large. Recently, it has been proposed to use the expression values of miRNAs and mRNAs (and/or proteins) to refine the results of sequence-based putative targets for a particular experiment. These methods have shown to be effective identifying the most prominent interactions from the databases of putative targets. Here, we review these methods that combine both expression and sequence-based putative targets to predict miRNA targets.
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Pey J, Prada J, Beasley JE, Planes FJ. Path finding methods accounting for stoichiometry in metabolic networks. Genome Biol 2011; 12:R49. [PMID: 21619601 PMCID: PMC3219972 DOI: 10.1186/gb-2011-12-5-r49] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [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: 03/01/2011] [Revised: 05/14/2011] [Accepted: 05/27/2011] [Indexed: 01/30/2023] Open
Abstract
Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks.
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Affiliation(s)
- Jon Pey
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain
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Rezola A, de Figueiredo LF, Brock M, Pey J, Podhorski A, Wittmann C, Schuster S, Bockmayr A, Planes FJ. Exploring metabolic pathways in genome-scale networks via generating flux modes. ACTA ACUST UNITED AC 2010; 27:534-40. [PMID: 21149278 DOI: 10.1093/bioinformatics/btq681] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION The reconstruction of metabolic networks at the genome scale has allowed the analysis of metabolic pathways at an unprecedented level of complexity. Elementary flux modes (EFMs) are an appropriate concept for such analysis. However, their number grows in a combinatorial fashion as the size of the metabolic network increases, which renders the application of EFMs approach to large metabolic networks difficult. Novel methods are expected to deal with such complexity. RESULTS In this article, we present a novel optimization-based method for determining a minimal generating set of EFMs, i.e. a convex basis. We show that a subset of elements of this convex basis can be effectively computed even in large metabolic networks. Our method was applied to examine the structure of pathways producing lysine in Escherichia coli. We obtained a more varied and informative set of pathways in comparison with existing methods. In addition, an alternative pathway to produce lysine was identified using a detour via propionyl-CoA, which shows the predictive power of our novel approach. AVAILABILITY The source code in C++ is available upon request.
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Affiliation(s)
- A Rezola
- Biomedical Engineering, University of Navarra, 20018 San Sebastian, Spain
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de Figueiredo LF, Podhorski A, Rubio A, Kaleta C, Beasley JE, Schuster S, Planes FJ. Computing the shortest elementary flux modes in genome-scale metabolic networks. ACTA ACUST UNITED AC 2009; 25:3158-65. [PMID: 19793869 DOI: 10.1093/bioinformatics/btp564] [Citation(s) in RCA: 164] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
MOTIVATION Elementary flux modes (EFMs) represent a key concept to analyze metabolic networks from a pathway-oriented perspective. In spite of considerable work in this field, the computation of the full set of elementary flux modes in large-scale metabolic networks still constitutes a challenging issue due to its underlying combinatorial complexity. RESULTS In this article, we illustrate that the full set of EFMs can be enumerated in increasing order of number of reactions via integer linear programming. In this light, we present a novel procedure to efficiently determine the K-shortest EFMs in large-scale metabolic networks. Our method was applied to find the K-shortest EFMs that produce lysine in the genome-scale metabolic networks of Escherichia coli and Corynebacterium glutamicum. A detailed analysis of the biological significance of the K-shortest EFMs was conducted, finding that glucose catabolism, ammonium assimilation, lysine anabolism and cofactor balancing were correctly predicted. The work presented here represents an important step forward in the analysis and computation of EFMs for large-scale metabolic networks, where traditional methods fail for networks of even moderate size. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Abstract
A metabolic pathway is a coherent set of enzyme catalysed biochemical reactions by which a living organism transforms an initial (source) compound into a final (target) compound. Some of the different metabolic pathways adopted within organisms have been experimentally determined. In this paper, we show that a number of experimentally determined metabolic pathways can be recovered by a mathematical optimization model.
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Affiliation(s)
- John E Beasley
- Mathematical Sciences, Brunel University Uxbridge, UB8 3PH, UK.
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