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Brusati A, Agostinetto G, Bruno A, Casiraghi M, Pescini D, Sandionigi A, Balech B. Exploration and Retrieval of Virus-Related Molecular Data Using ExTaxsI: The Monkeypox Use Case. Methods Mol Biol 2024; 2732:145-154. [PMID: 38060123 DOI: 10.1007/978-1-0716-3515-5_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Retrieval and visualization of biological data are essential for understanding complex systems. With the increasing volume of data generated from high-throughput sequencing technologies, effective and optimized data visualization tools have become indispensable. This is particularly relevant in the COVID-19 postpandemic period, where understanding the diversity and interactions of microbial communities (i.e., viral and bacterial) constitutes an important asset to develop and plan suitable interventions.In this chapter, we show the usage and the potentials of ExTaxsI (Exploring Taxonomy Information) tool to retrieve viral biodiversity data stored in National Center for Biotechnology Information (NCBI) databases and create the related visualization. In addition, by integrating different functions and modules, the tool generates relevant types of visualization plots to facilitate the exploration of microbial biodiversity communities useful to deep dive into ecological and taxonomic relationships among different species and identify potential significant targets.Using the Monkeypox virus as a case study, this work points out significant perspectives on biological data visualization, which can be used to gain insights into the ecology, evolution, and pathogenesis of viruses. Accordingly, we show the potentiality of ExTaxsI to organize and describe the available/downloaded data in an easy, simple, and interpretable way allowing the user to interact dynamically with the visualization plots through specific filters, zoom, and explore functions.
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Affiliation(s)
- Alberto Brusati
- University of Pavia, Department of Brain and Behavioral Sciences, Pavia, Italy
| | - Giulia Agostinetto
- University of Milano-Bicocca, Department of Biotechnology and Biosciences, Milan, Italy.
| | - Antonia Bruno
- University of Milano-Bicocca, Department of Biotechnology and Biosciences, Milan, Italy
| | - Maurizio Casiraghi
- University of Milano-Bicocca, Department of Biotechnology and Biosciences, Milan, Italy
| | - Dario Pescini
- University of Milano-Bicocca, Department of Statistics and Quantitative Methods, Milan, Italy
| | - Anna Sandionigi
- University of Milano-Bicocca, Department of Biotechnology and Biosciences, Milan, Italy
- Quantia Consulting srl, Department of Data Science and Education, Remote Company, Milan, Italy
| | - Bachir Balech
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (CNR), Bari, Italy
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Di Filippo M, Avellone A, Belingheri M, Paladino ME, Riva MA, Zambon A, Pescini D. Mobile app to perform anonymized longitudinal studies in the context of COVID-19 adverse drug reaction monitoring, leveraging the citizenship engagement. JMIR Hum Factors 2022; 9:e38701. [PMID: 35930561 DOI: 10.2196/38701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/08/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Over the last few years, increasingly studies focused on the development of mobile apps as complementary tools to existing pharmacovigilance traditional surveillance systems for improving and facilitating adverse drug reactions reporting. OBJECTIVE In this study, we evaluated the potentiality of a new mobile app (vaxEffect@UniMiB) to perform longitudinal studies while preserving the anonymity of the respondents. We applied it to monitor the adverse drug reactions during COVID-19 vaccination campaign in a sample of Italian population. METHODS We administered vaxEffect@UniMiB to a convenience sample of academic subjects vaccinated at Milano-Bicocca University hub for COVID-19 during the Italian national vaccination campaign. vaxEffect@UniMiB was developed for both Android and iOS devices. The mobile app asks users to send their medical history and, upon every vaccine administration, their vaccination data and the adverse reactions that occurred within seven days after the vaccination, allowing the follow of reactions dynamic for each respondent. The app sends data over the web to an application server. The web server, along with receiving all user data, saves them in a SQL database server, and reminds patients to submit vaccine and adverse reactions' data by push notifications sent to the mobile app through Firebase Cloud Messaging. On initial startup of the app, a unique user identifier was generated for each respondent, so that its anonymity is completely ensured, while enabling longitudinal studies. RESULTS A total of 3712 people have been vaccinated during the first vaccination wave. A total of 2733 respondents between the ages of 19 and 80, coming from the University of Milano-Bicocca and the Politecnico of Milan, participated in the survey. Overall, we collected the information about vaccination and adverse reactions to the first vaccine dose for 2226 subjects (60.0% of vaccinated), to the second dose for 1610 subjects (43.3%), and, in a non-sponsored fashion, to the third dose for 169 individuals. CONCLUSIONS vaxEffect@UniMiB revealed to be the first attempt in performing longitudinal studies to monitor the same subject over time in terms of the reported ADRs after each vaccine administration, while guaranteeing at the same time complete anonymity of the subjects. A series of aspects contributed to a positive involvement from people in using this application to report their ADRs to vaccination: ease of use, availability from multiple platforms, anonymity of all the survey participants and protection of the submitted data and the healthcare workers' support. CLINICALTRIAL
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Affiliation(s)
- Marzia Di Filippo
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi, 8, Milan, IT
| | - Alessandro Avellone
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, IT
| | | | | | | | - Antonella Zambon
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, IT
| | - Dario Pescini
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, IT
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Di Filippo M, Pescini D, Galuzzi BG, Bonanomi M, Gaglio D, Mangano E, Consolandi C, Alberghina L, Vanoni M, Damiani C. INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLoS Comput Biol 2022; 18:e1009337. [PMID: 35130273 PMCID: PMC8853556 DOI: 10.1371/journal.pcbi.1009337] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [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: 07/24/2021] [Revised: 02/17/2022] [Accepted: 01/13/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.
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Affiliation(s)
- Marzia Di Filippo
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
| | - Dario Pescini
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
| | - Bruno Giovanni Galuzzi
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Marcella Bonanomi
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Daniela Gaglio
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Segrate, Italy
| | - Eleonora Mangano
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate, Italy
| | - Clarissa Consolandi
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate, Italy
| | - Lilia Alberghina
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Marco Vanoni
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Chiara Damiani
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
- * E-mail:
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Agostinetto G, Brusati A, Sandionigi A, Chahed A, Parladori E, Balech B, Bruno A, Pescini D, Casiraghi M. ExTaxsI: an exploration tool of biodiversity molecular data. Gigascience 2022; 11:giab092. [PMID: 35077538 PMCID: PMC8848311 DOI: 10.1093/gigascience/giab092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 11/16/2021] [Accepted: 11/30/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND The increasing availability of multi-omics data is leading to regularly revised estimates of existing biodiversity data. In particular, the molecular data enable novel species to be characterized and the information linked to those already observed to be increased with new genomics data. For this reason, the management and visualization of existing molecular data, and their related metadata, through the implementation of easy-to-use IT tools have become a key point to design future research. The more users are able to access biodiversity-related information, the greater the ability of the scientific community to expand its knowledge in this area. RESULTS In this article we focus on the development of ExTaxsI (Exploring Taxonomy Information), an IT tool that can retrieve biodiversity data stored in NCBI databases and provide a simple and explorable visualization. We use 3 case studies to show how an efficient organization of the available data can lead to obtaining new information that is fundamental as a starting point for new research. Using this approach highlights the limits in the distribution of data availability, a key factor to consider in the experimental design phase of broad-spectrum studies such as metagenomics. CONCLUSIONS ExTaxsI can easily retrieve molecular data and its metadata with an explorable visualization, with the aim of helping researchers to improve experimental designs and highlight the main gaps in the coverage of available data.
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Affiliation(s)
- Giulia Agostinetto
- University of Milano-Bicocca, Department of Biotechnology and Biosciences, Piazza della Scienza 2, 20126 Milan, Italy
| | - Alberto Brusati
- Istituto Auxologico Italiano - IRCCS, Via Giuseppe Zucchi 18, 20095 Cusano Milanino, Italy
- Università degli Studi di Pavia, Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Via Agostino Bassi 21, 27100 Pavia, Italy
| | - Anna Sandionigi
- Quantia Consulting srl, Via F. Petrarca 20, 22066 Mariano Comense, Italy
| | - Adam Chahed
- University of Milano-Bicocca, Department of Biotechnology and Biosciences, Piazza della Scienza 2, 20126 Milan, Italy
| | - Elena Parladori
- University of Milano-Bicocca, Department of Biotechnology and Biosciences, Piazza della Scienza 2, 20126 Milan, Italy
| | - Bachir Balech
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (CNR), Via Amendola 122/O, 70126 Bari, Italy
| | - Antonia Bruno
- University of Milano-Bicocca, Department of Biotechnology and Biosciences, Piazza della Scienza 2, 20126 Milan, Italy
| | - Dario Pescini
- University of Milano-Bicocca, Department of Statistics and Quantitative Methods, Piazza dell'Ateneo Nuovo 1, 20126 Milan, Italy
| | - Maurizio Casiraghi
- University of Milano-Bicocca, Department of Biotechnology and Biosciences, Piazza della Scienza 2, 20126 Milan, Italy
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5
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Nobile MS, Coelho V, Pescini D, Damiani C. Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models. BMC Bioinformatics 2021; 22:78. [PMID: 33902438 PMCID: PMC8074438 DOI: 10.1186/s12859-021-04002-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 02/07/2021] [Indexed: 01/20/2023] Open
Abstract
Background Genome-wide reconstructions of metabolism opened the way to thorough investigations of cell metabolism for health care and industrial purposes. However, the predictions offered by Flux Balance Analysis (FBA) can be strongly affected by the choice of flux boundaries, with particular regard to the flux of reactions that sink nutrients into the system. To mitigate possible errors introduced by a poor selection of such boundaries, a rational approach suggests to focus the modeling efforts on the pivotal ones. Methods In this work, we present a methodology for the automatic identification of the key fluxes in genome-wide constraint-based models, by means of variance-based sensitivity analysis. The goal is to identify the parameters for which a small perturbation entails a large variation of the model outcomes, also referred to as sensitive parameters. Due to the high number of FBA simulations that are necessary to assess sensitivity coefficients on genome-wide models, our method exploits a master-slave methodology that distributes the computation on massively multi-core architectures. We performed the following steps: (1) we determined the putative parameterizations of the genome-wide metabolic constraint-based model, using Saltelli’s method; (2) we applied FBA to each parameterized model, distributing the massive amount of calculations over multiple nodes by means of MPI; (3) we then recollected and exploited the results of all FBA runs to assess a global sensitivity analysis. Results We show a proof-of-concept of our approach on latest genome-wide reconstructions of human metabolism Recon2.2 and Recon3D. We report that most sensitive parameters are mainly associated with the intake of essential amino acids in Recon2.2, whereas in Recon 3D they are associated largely with phospholipids. We also illustrate that in most cases there is a significant contribution of higher order effects. Conclusion Our results indicate that interaction effects between different model parameters exist, which should be taken into account especially at the stage of calibration of genome-wide models, supporting the importance of a global strategy of sensitivity analysis. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04002-0.
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Affiliation(s)
- Marco S Nobile
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.,SYSBIO/ISBE.IT Centre for Systems Biology, Milan, Italy.,Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Vasco Coelho
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | - Dario Pescini
- Department of Statistics and Quantiative Methods, University of Milano-Bicocca, Milan, Italy.,SYSBIO/ISBE.IT Centre for Systems Biology, Milan, Italy
| | - Chiara Damiani
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy. .,SYSBIO/ISBE.IT Centre for Systems Biology, Milan, Italy.
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Damiani C, Rovida L, Maspero D, Sala I, Rosato L, Di Filippo M, Pescini D, Graudenzi A, Antoniotti M, Mauri G. MaREA4Galaxy: Metabolic reaction enrichment analysis and visualization of RNA-seq data within Galaxy. Comput Struct Biotechnol J 2020; 18:993-999. [PMID: 32373287 PMCID: PMC7191582 DOI: 10.1016/j.csbj.2020.04.008] [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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 03/20/2020] [Accepted: 04/08/2020] [Indexed: 12/21/2022] Open
Abstract
We present MaREA4Galaxy, a user-friendly tool that allows a user to characterize and to graphically compare groups of samples with different transcriptional regulation of metabolism, as estimated from cross-sectional RNA-seq data. The tool is available as plug-in for the widely-used Galaxy platform for comparative genomics and bioinformatics analyses. MaREA4Galaxy combines three modules. The Expression2RAS module, which, for each reaction of a specified set, computes a Reaction Activity Score (RAS) as a function of the expression level of genes encoding for the associated enzyme. The MaREA (Metabolic Reaction Enrichment Analysis) module that allows to highlight significant differences in reaction activities between specified groups of samples. The Clustering module which employs the RAS computed before as a metric for unsupervised clustering of samples into distinct metabolic subgroups; the Clustering tool provides different clustering techniques and implements standard methods to evaluate the goodness of the results.
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Affiliation(s)
- Chiara Damiani
- Dept. of Biotechnology and Biosciences, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Lorenzo Rovida
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Davide Maspero
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Irene Sala
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Luca Rosato
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Marzia Di Filippo
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Department of Statistics and Quantitative Methods, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Dario Pescini
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Department of Statistics and Quantitative Methods, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Alex Graudenzi
- Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan, Italy
| | - Marco Antoniotti
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Giancarlo Mauri
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
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7
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Damiani C, Maspero D, Di Filippo M, Colombo R, Pescini D, Graudenzi A, Westerhoff HV, Alberghina L, Vanoni M, Mauri G. Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLoS Comput Biol 2019; 15:e1006733. [PMID: 30818329 PMCID: PMC6413955 DOI: 10.1371/journal.pcbi.1006733] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [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: 06/20/2018] [Revised: 03/12/2019] [Accepted: 12/22/2018] [Indexed: 02/07/2023] Open
Abstract
Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets. Cytotoxicity of chemotherapeutic agents and resistance to targeted treatments are the main reasons why cancer is still one of the top causes of death. As tumor cells are intrinsically resistant to therapies that target signaling pathways, targeting the metabolic hallmarks of cancer holds promise for more incisive treatments. Regrettably, the heterogeneity of cancer metabolism hinders the identification of effective treatments. To fully uncover the metabolic heterogeneity within tumors, characterization of metabolic programs (metabolic flux distributions) at the single-cell level is required. To fill the gap between current technologies for genomics and future technologies for fluxomics, both at the single-cell and the genome-wide scale, we propose to integrate cancer data from: 1) single-cell transcriptomics and 2) bulk metabolomics, into a multi-scale stoichiometric model, to deliver for the first time metabolic fluxomes at the single-cell level. To this end, we introduce a new paradigm for flux balance analysis and data integration in cancer metabolism to: 1) characterize metabolic heterogeneity, not only at the inter-, but also at the intra-tumor level 2) identify the metabolic interactions between cancer populations, whose role in resistance to metabolic treatments has been recently recognized 3) predict the collective response to drug targeting of metabolism.
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Affiliation(s)
- Chiara Damiani
- Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
- * E-mail:
| | - Davide Maspero
- Dept. of Biotechnology and Biosciences, University of Milan-Bicocca, 20126, Milan, Italy
- Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Marzia Di Filippo
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
- Dept. of Biotechnology and Biosciences, University of Milan-Bicocca, 20126, Milan, Italy
| | - Riccardo Colombo
- Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
- Dept. of Statistics and Quantitative Methods, University of Milan-Bicocca, 20126, Milan, Italy
| | - Alex Graudenzi
- Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy
| | - Hans Victor Westerhoff
- Dept. of Molecular Cell Physiology, Faculty of Earth and Life Sciences, VU University, Amsterdam, The Netherlands
- Manchester Centre for Integrative Systems Biology, School of Chemical Engineering and Analytical Science, University of Manchester, Manchester, United Kingdom
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
| | - Lilia Alberghina
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
- Dept. of Biotechnology and Biosciences, University of Milan-Bicocca, 20126, Milan, Italy
| | - Marco Vanoni
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
- Dept. of Biotechnology and Biosciences, University of Milan-Bicocca, 20126, Milan, Italy
| | - Giancarlo Mauri
- Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
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Abstract
Background Determining the value of kinetic constants for a metabolic system in the exact
physiological conditions is an extremely hard task. However, this kind of
information is of pivotal relevance to effectively simulate a biological
phenomenon as complex as metabolism. Results To overcome this issue, we propose to investigate emerging properties of
ensembles of sets of kinetic constants leading to the biological readout observed
in different experimental conditions. To this aim, we exploit information
retrievable from constraint-based analyses (i.e. metabolic flux distributions at
steady state) with the goal to generate feasible values for kinetic constants
exploiting the mass action law. The sets retrieved from the previous step will be
used to parametrize a mechanistic model whose simulation will be performed to
reconstruct the dynamics of the system (until reaching the metabolic steady state)
for each experimental condition. Every parametrization that is in accordance with
the expected metabolic phenotype is collected in an ensemble whose features are
analyzed to determine the emergence of properties of a phenotype. In this work we
apply the proposed approach to identify ensembles of kinetic parameters for five
metabolic phenotypes of E. Coli, by analyzing
five different experimental conditions associated with the ECC2comp model recently
published by Hädicke and collaborators. Conclusions Our results suggest that the parameter values of just few reactions are
responsible for the emergence of a metabolic phenotype. Notably, in contrast with
constraint-based approaches such as Flux Balance Analysis, the methodology used in
this paper does not require to assume that metabolism is optimizing towards a
specific goal. Electronic supplementary material The online version of this article (10.1186/s12859-018-2181-7) contains supplementary material, which is available to authorized
users.
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Affiliation(s)
- Riccardo Colombo
- Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy. .,SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy.
| | - Chiara Damiani
- Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy.,SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy
| | - David Gilbert
- College of Engineering, Design and Physical Sciences, Brunel University, Middlesex, London, Uxbridg, UB8 3PH, UK
| | - Monika Heiner
- Computer Science Department, Brandenburg University of Technology Cottbus-Senftenberg, Walther-Pauer-Str. 2, Cottbus, D-03046, Germany
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy.,SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy
| | - Dario Pescini
- SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy.,Department of Statistics and Quantitative Methods, University of Milan - Bicocca, Via Bicocca degli Arcimboldi, 8, Milan, 20126, Italy
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9
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Abstract
Motivation Intratumour heterogeneity poses many challenges to the treatment of cancer. Unfortunately, the transcriptional and metabolic information retrieved by currently available computational and experimental techniques portrays the average behaviour of intermixed and heterogeneous cell subpopulations within a given tumour. Emerging single-cell genomic analyses are nonetheless unable to characterize the interactions among cancer subpopulations. In this study, we propose popFBA, an extension to classic Flux Balance Analysis, to explore how metabolic heterogeneity and cooperation phenomena affect the overall growth of cancer cell populations. Results We show how clones of a metabolic network of human central carbon metabolism, sharing the same stoichiometry and capacity constraints, may follow several different metabolic paths and cooperate to maximize the growth of the total population. We also introduce a method to explore the space of possible interactions, given some constraints on plasma supply of nutrients. We illustrate how alternative nutrients in plasma supply and/or a dishomogeneous distribution of oxygen provision may affect the landscape of heterogeneous phenotypes. We finally provide a technique to identify the most proliferative cells within the heterogeneous population. Availability and implementation the popFBA MATLAB function and the SBML model are available at https://github.com/BIMIB-DISCo/popFBA.
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Affiliation(s)
- Chiara Damiani
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy
| | - Marzia Di Filippo
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Statistics and Quantitative Methods, University Milano-Bicocca, Milan, Italy
| | - Davide Maspero
- Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy
| | - Riccardo Colombo
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy
| | - Giancarlo Mauri
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy
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Damiani C, Colombo R, Gaglio D, Mastroianni F, Pescini D, Westerhoff HV, Mauri G, Vanoni M, Alberghina L. A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect. PLoS Comput Biol 2017; 13:e1005758. [PMID: 28957320 PMCID: PMC5634631 DOI: 10.1371/journal.pcbi.1005758] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [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: 04/07/2017] [Revised: 10/10/2017] [Accepted: 09/01/2017] [Indexed: 01/24/2023] Open
Abstract
Cancer cells share several metabolic traits, including aerobic production of lactate from glucose (Warburg effect), extensive glutamine utilization and impaired mitochondrial electron flow. It is still unclear how these metabolic rearrangements, which may involve different molecular events in different cells, contribute to a selective advantage for cancer cell proliferation. To ascertain which metabolic pathways are used to convert glucose and glutamine to balanced energy and biomass production, we performed systematic constraint-based simulations of a model of human central metabolism. Sampling of the feasible flux space allowed us to obtain a large number of randomly mutated cells simulated at different glutamine and glucose uptake rates. We observed that, in the limited subset of proliferating cells, most displayed fermentation of glucose to lactate in the presence of oxygen. At high utilization rates of glutamine, oxidative utilization of glucose was decreased, while the production of lactate from glutamine was enhanced. This emergent phenotype was observed only when the available carbon exceeded the amount that could be fully oxidized by the available oxygen. Under the latter conditions, standard Flux Balance Analysis indicated that: this metabolic pattern is optimal to maximize biomass and ATP production; it requires the activity of a branched TCA cycle, in which glutamine-dependent reductive carboxylation cooperates to the production of lipids and proteins; it is sustained by a variety of redox-controlled metabolic reactions. In a K-ras transformed cell line we experimentally assessed glutamine-induced metabolic changes. We validated computational results through an extension of Flux Balance Analysis that allows prediction of metabolite variations. Taken together these findings offer new understanding of the logic of the metabolic reprogramming that underlies cancer cell growth. Hallmarks describing common key events in initiation, maintenance and progression of cancer have been identified. One hallmark deals with rewiring of metabolic reactions required to sustain enhanced cell proliferation. The availability of molecular, mechanistic models of cancer hallmarks will mightily improve optimized personal treatment and new drug discovery. Metabolism is the only hallmark for which it is currently possible to derive large scale mathematical models, which have predictive ability. In this paper, we exploit a constraint-based model of the core metabolism required for biomass conversion of the most relevant nutrients—glucose and glutamine—to clarify the logic of control of cancer metabolism. We newly report that, when available oxygen is not sufficient to fully oxidize available glucose and glutamine carbons–a situation compatible with that observed under normal oxygen conditions in human and in cancer cells growing in vitro—utilization of glutamine by reductive carboxylation and conversion of glucose and glutamine to lactate confer advantage for biomass production. Redox homeostasis can be maintained through the use of different alternative pathways. In conclusion, this paper offers a logic interpretation to the link between metabolic rewiring and enhanced proliferation, which may offer new approaches to targeted drug discovery and utilization.
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Affiliation(s)
- Chiara Damiani
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Informatics, Systems and Communication, University Milano-Bicocca, Milano, Italy
| | - Riccardo Colombo
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Informatics, Systems and Communication, University Milano-Bicocca, Milano, Italy
| | - Daniela Gaglio
- SYSBIO Centre of Systems Biology, Milano, Italy
- Institute of Molecular Bioimaging and Physiology, CNR, Segrate, Milan, Italy
| | - Fabrizia Mastroianni
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Biotechnology and Biosciences, University Milano-Bicocca, Milano, Italy
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Statistics and Quantitative Methods, University Milano-Bicocca, Milano, Italy
| | - Hans Victor Westerhoff
- Dept of Molecular Cell Physiology, Faculty of Earth and Life Sciences, VU University, Amsterdam, The Netherlands
- Manchester Centre for Integrative Systems Biology, School of Chemical Engineering and Analytical Science, University of Manchester, Manchester, United Kingdom
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
| | - Giancarlo Mauri
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Informatics, Systems and Communication, University Milano-Bicocca, Milano, Italy
| | - Marco Vanoni
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Biotechnology and Biosciences, University Milano-Bicocca, Milano, Italy
- * E-mail: (LA); (MV)
| | - Lilia Alberghina
- SYSBIO Centre of Systems Biology, Milano, Italy
- Dept of Biotechnology and Biosciences, University Milano-Bicocca, Milano, Italy
- * E-mail: (LA); (MV)
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Di Filippo M, Colombo R, Damiani C, Pescini D, Gaglio D, Vanoni M, Alberghina L, Mauri G. Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models. Comput Biol Chem 2016; 62:60-9. [PMID: 27085310 DOI: 10.1016/j.compbiolchem.2016.03.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 02/25/2016] [Accepted: 03/04/2016] [Indexed: 01/10/2023]
Abstract
The metabolic rearrangements occurring in cancer cells can be effectively investigated with a Systems Biology approach supported by metabolic network modeling. We here present tissue-specific constraint-based core models for three different types of tumors (liver, breast and lung) that serve this purpose. The core models were extracted and manually curated from the corresponding genome-scale metabolic models in the Human Metabolic Atlas database with a focus on the pathways that are known to play a key role in cancer growth and proliferation. Along similar lines, we also reconstructed a core model from the original general human metabolic network to be used as a reference model. A comparative Flux Balance Analysis between the reference and the cancer models highlighted both a clear distinction between the two conditions and a heterogeneity within the three different cancer types in terms of metabolic flux distribution. These results emphasize the need for modeling approaches able to keep up with this tumoral heterogeneity in order to identify more suitable drug targets and develop effective treatments. According to this perspective, we identified key points able to reverse the tumoral phenotype toward the reference one or vice-versa.
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Affiliation(s)
- Marzia Di Filippo
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Riccardo Colombo
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Chiara Damiani
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy.
| | - Daniela Gaglio
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Istituto di Bioimmagini e Fisiologia Molecolare, Consiglio Nazionale delle Ricerche, Via F.lli Cervi 93, 20090 Segrate (MI), Italy
| | - Marco Vanoni
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy
| | - Lilia Alberghina
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy
| | - Giancarlo Mauri
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
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Cazzaniga P, Damiani C, Besozzi D, Colombo R, Nobile MS, Gaglio D, Pescini D, Molinari S, Mauri G, Alberghina L, Vanoni M. Computational strategies for a system-level understanding of metabolism. Metabolites 2014; 4:1034-87. [PMID: 25427076 PMCID: PMC4279158 DOI: 10.3390/metabo4041034] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [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: 08/01/2014] [Revised: 11/05/2014] [Accepted: 11/12/2014] [Indexed: 12/20/2022] Open
Abstract
Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.
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Affiliation(s)
- Paolo Cazzaniga
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Chiara Damiani
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Daniela Besozzi
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Riccardo Colombo
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Marco S Nobile
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Daniela Gaglio
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Sara Molinari
- Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Giancarlo Mauri
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Lilia Alberghina
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Marco Vanoni
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
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Nobile MS, Cazzaniga P, Besozzi D, Pescini D, Mauri G. cuTauLeaping: a GPU-powered tau-leaping stochastic simulator for massive parallel analyses of biological systems. PLoS One 2014; 9:e91963. [PMID: 24663957 PMCID: PMC3963881 DOI: 10.1371/journal.pone.0091963] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2013] [Accepted: 02/17/2014] [Indexed: 12/03/2022] Open
Abstract
Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolution of biological systems, modeled according to the stochastic formulation of chemical kinetics. The analysis of dynamical properties of these systems in physiological and perturbed conditions usually requires the execution of a large number of simulations, leading to high computational costs. Since each simulation can be executed independently from the others, a massive parallelization of tau-leaping can bring to relevant reductions of the overall running time. The emerging field of General Purpose Graphic Processing Units (GPGPU) provides power-efficient high-performance computing at a relatively low cost. In this work we introduce cuTauLeaping, a stochastic simulator of biological systems that makes use of GPGPU computing to execute multiple parallel tau-leaping simulations, by fully exploiting the Nvidia's Fermi GPU architecture. We show how a considerable computational speedup is achieved on GPU by partitioning the execution of tau-leaping into multiple separated phases, and we describe how to avoid some implementation pitfalls related to the scarcity of memory resources on the GPU streaming multiprocessors. Our results show that cuTauLeaping largely outperforms the CPU-based tau-leaping implementation when the number of parallel simulations increases, with a break-even directly depending on the size of the biological system and on the complexity of its emergent dynamics. In particular, cuTauLeaping is exploited to investigate the probability distribution of bistable states in the Schlögl model, and to carry out a bidimensional parameter sweep analysis to study the oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae.
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Affiliation(s)
- Marco S. Nobile
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Milano, Italy
- SYSBIO Centre for Systems Biology, Milano, Italy
- * E-mail: (MSN); (PC)
| | - Paolo Cazzaniga
- Dipartimento di Scienze Umane e Sociali, Università degli Studi di Bergamo, Bergamo, Italy
- Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”, Consiglio Nazionale delle Ricerche, Roma, Italy
- SYSBIO Centre for Systems Biology, Milano, Italy
- * E-mail: (MSN); (PC)
| | - Daniela Besozzi
- Dipartimento di Informatica, Università degli Studi di Milano, Milano, Italy
- Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”, Consiglio Nazionale delle Ricerche, Roma, Italy
- SYSBIO Centre for Systems Biology, Milano, Italy
| | - Dario Pescini
- Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca, Milano, Italy
- SYSBIO Centre for Systems Biology, Milano, Italy
| | - Giancarlo Mauri
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Milano, Italy
- SYSBIO Centre for Systems Biology, Milano, Italy
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Amara F, Colombo R, Cazzaniga P, Pescini D, Csikász-Nagy A, Falconi MM, Besozzi D, Plevani P. In vivo and in silico analysis of PCNA ubiquitylation in the activation of the Post Replication Repair pathway in S. cerevisiae. BMC Syst Biol 2013; 7:24. [PMID: 23514624 PMCID: PMC3668150 DOI: 10.1186/1752-0509-7-24] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [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] [Subscribe] [Scholar Register] [Received: 07/24/2012] [Accepted: 02/05/2013] [Indexed: 12/23/2022]
Abstract
BACKGROUND The genome of living organisms is constantly exposed to several damaging agents that induce different types of DNA lesions, leading to cellular malfunctioning and onset of many diseases. To maintain genome stability, cells developed various repair and tolerance systems to counteract the effects of DNA damage. Here we focus on Post Replication Repair (PRR), the pathway involved in the bypass of DNA lesions induced by sunlight exposure and UV radiation. PRR acts through two different mechanisms, activated by mono- and poly-ubiquitylation of the DNA sliding clamp, called Proliferating Cell Nuclear Antigen (PCNA). RESULTS We developed a novel protocol to measure the time-course ratios between mono-, di- and tri-ubiquitylated PCNA isoforms on a single western blot, which were used as the wet readout for PRR events in wild type and mutant S. cerevisiae cells exposed to acute UV radiation doses. Stochastic simulations of PCNA ubiquitylation dynamics, performed by exploiting a novel mechanistic model of PRR, well fitted the experimental data at low UV doses, but evidenced divergent behaviors at high UV doses, thus driving the design of further experiments to verify new hypothesis on the functioning of PRR. The model predicted the existence of a UV dose threshold for the proper functioning of the PRR model, and highlighted an overlapping effect of Nucleotide Excision Repair (the pathway effectively responsible to clean the genome from UV lesions) on the dynamics of PCNA ubiquitylation in different phases of the cell cycle. In addition, we showed that ubiquitin concentration can affect the rate of PCNA ubiquitylation in PRR, offering a possible explanation to the DNA damage sensitivity of yeast strains lacking deubiquitylating enzymes. CONCLUSIONS We exploited an in vivo and in silico combinational approach to analyze for the first time in a Systems Biology context the events of PCNA ubiquitylation occurring in PRR in budding yeast cells. Our findings highlighted an intricate functional crosstalk between PRR and other events controlling genome stability, and evidenced that PRR is more complicated and still far less characterized than previously thought.
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Affiliation(s)
- Flavio Amara
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, Italy
| | - Riccardo Colombo
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Milano, Italy
| | - Paolo Cazzaniga
- Dipartimento di Scienze Umane e Sociali, Università degli Studi di Bergamo, Bergamo, Italy
| | - Dario Pescini
- Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca, Milano, Italy
| | - Attila Csikász-Nagy
- , The Microsoft Research - Università degli Studi di Trento, Centre for Computational and Systems Biology, Rovereto (Trento), Italy
| | - Marco Muzi Falconi
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, Italy
| | - Daniela Besozzi
- Dipartimento di Informatica, Università degli Studi di Milano, Milano, Italy
| | - Paolo Plevani
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, Italy
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15
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Besozzi D, Cazzaniga P, Pescini D, Mauri G, Colombo S, Martegani E. The role of feedback control mechanisms on the establishment of oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae. EURASIP J Bioinform Syst Biol 2012; 2012:10. [PMID: 22818197 PMCID: PMC3479052 DOI: 10.1186/1687-4153-2012-10] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2012] [Accepted: 06/20/2012] [Indexed: 11/12/2022]
Abstract
In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway is involved in the regulation of cell growth and proliferation in response to nutritional sensing and stress conditions. The pathway is tightly regulated by multiple feedback loops, exerted by the protein kinase A (PKA) on a few pivotal components of the pathway. In this article, we investigate the dynamics of the second messenger cAMP by performing stochastic simulations and parameter sweep analysis of a mechanistic model of the Ras/cAMP/PKA pathway, to determine the effects that the modulation of these feedback mechanisms has on the establishment of stable oscillatory regimes. In particular, we start by studying the role of phosphodiesterases, the enzymes that catalyze the degradation of cAMP, which represent the major negative feedback in this pathway. Then, we show the results on cAMP oscillations when perturbing the amount of protein Cdc25 coupled with the alteration of the intracellular ratio of the guanine nucleotides (GTP/GDP), which are known to regulate the switch of the GTPase Ras protein. This multi-level regulation of the amplitude and frequency of oscillations in the Ras/cAMP/PKA pathway might act as a fine tuning mechanism for the downstream targets of PKA, as also recently evidenced by some experimental investigations on the nucleocytoplasmic shuttling of the transcription factor Msn2 in yeast cells.
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Affiliation(s)
- Daniela Besozzi
- Università degli Studi di Milano, Dipartimento di Informatica, Via Comelico 39, 20135 Milano, Italy.
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16
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Nobile MS, Besozzi D, Cazzaniga P, Mauri G, Pescini D. A GPU-Based Multi-swarm PSO Method for Parameter Estimation in Stochastic Biological Systems Exploiting Discrete-Time Target Series. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 2012. [DOI: 10.1007/978-3-642-29066-4_7] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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17
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Pescini D, Cazzaniga P, Besozzi D, Mauri G, Amigoni L, Colombo S, Martegani E. Simulation of the Ras/cAMP/PKA pathway in budding yeast highlights the establishment of stable oscillatory states. Biotechnol Adv 2011; 30:99-107. [PMID: 21741466 DOI: 10.1016/j.biotechadv.2011.06.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2011] [Revised: 05/30/2011] [Accepted: 06/13/2011] [Indexed: 10/18/2022]
Abstract
In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway plays a major role in the regulation of metabolism, stress resistance and cell cycle progression. We extend here a mechanistic model of the Ras/cAMP/PKA pathway that we previously defined by describing the molecular interactions and post-translational modifications of proteins, and perform a computational analysis to investigate the dynamical behaviors of the components of this pathway, regulated by different control mechanisms. We carry out stochastic simulations to consider, in particular, the effect of the negative feedback loops on the activity of both Ira2 (a Ras-GAP) and Cdc25 (a Ras-GEF) proteins. Our results show that stable oscillatory regimes for the dynamics of cAMP can be obtained only through the activation of these feedback mechanisms, and when the amount of Cdc25 is within a specific range. In addition, we highlight that the levels of guanine nucleotides pools are able to regulate the pathway, by influencing the transition between stable steady states and oscillatory regimes.
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Affiliation(s)
- Dario Pescini
- Università degli Studi di Milano-Bicocca, Dipartimento di Statistica, Milano, Italy.
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18
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Besozzi D, Cazzaniga P, Pescini D, Mauri G. BioSimWare: A Simulation Environment for Stochastic Modelling of Complex Biological Systems. J Biotechnol 2010. [DOI: 10.1016/j.jbiotec.2010.09.831] [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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Besozzi D, Cazzaniga P, Devecchi A, Landini P, Pescini D. An in silico investigation of different regulation mechanisms of the bacterial second messenger c-di-GMP. J Biotechnol 2010. [DOI: 10.1016/j.jbiotec.2010.09.905] [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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20
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Cazzaniga P, Pescini D, Besozzi D, Mauri G, Colombo S, Martegani E. Modeling and stochastic simulation of the Ras/cAMP/PKA pathway in the yeast Saccharomyces cerevisiae evidences a key regulatory function for intracellular guanine nucleotides pools. J Biotechnol 2007; 133:377-85. [PMID: 18023904 DOI: 10.1016/j.jbiotec.2007.09.019] [Citation(s) in RCA: 38] [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] [Received: 07/09/2007] [Revised: 09/06/2007] [Accepted: 09/29/2007] [Indexed: 11/26/2022]
Abstract
In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway is involved in the regulation of metabolism and cell cycle progression. The pathway is tightly regulated by several control mechanisms, as the feedback cycle ruled by the activity of phosphodiesterase. Here, we present a discrete mathematical model for the Ras/cAMP/PKA pathway that considers its principal cytoplasmic components and their mutual interactions. The tau-leaping algorithm is then used to perform stochastic simulations of the model. We investigate this system under various conditions, and we test how different values of several stochastic reaction constants affect the pathway behaviour. Finally, we show that the level of guanine nucleotides, GTP and GDP, could be relevant metabolic signals for the regulation of the whole pathway.
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Affiliation(s)
- Paolo Cazzaniga
- Università degli Studi di Milano-Bicocca, Dipartimento di Informatica, Sistemistica e Comunicazione, Viale Sarca 336, 20126 Milano, Italy
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Besozzi D, Cazzaniga P, Pescini D, Mauri G. Modelling metapopulations with stochastic membrane systems. Biosystems 2007; 91:499-514. [PMID: 17904729 DOI: 10.1016/j.biosystems.2006.12.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [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: 04/10/2006] [Revised: 10/19/2006] [Accepted: 12/30/2006] [Indexed: 10/23/2022]
Abstract
Metapopulations, or multi-patch systems, are models describing the interactions and the behavior of populations living in fragmented habitats. Dispersal, persistence and extinction are some of the characteristics of interest in ecological studies of metapopulations. In this paper, we propose a novel method to analyze metapopulations, which is based on a discrete and stochastic modelling framework in the area of Membrane Computing. New structural features of membrane systems, necessary to appropriately describe a multi-patch system, are introduced, such as the reduction of the maximal parallel consumption of objects, the spatial arrangement of membranes and the stochastic creation of objects. The role of the additional features, their meaning for a metapopulation model and the emergence of relevant behaviors are then investigated by means of stochastic simulations. Conclusive remarks and ideas for future research are finally presented.
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Affiliation(s)
- Daniela Besozzi
- Università degli Studi di Milano, Dipartimento di Informatica e Comunicazione, Via Comelico 39, 20135 Milano, Italy.
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Ferri F, Magatti D, Pescini D, Potenza MAC, Giglio M. Heterodyne near-field scattering: a technique for complex fluids. Phys Rev E Stat Nonlin Soft Matter Phys 2004; 70:041405. [PMID: 15600406 DOI: 10.1103/physreve.70.041405] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2003] [Revised: 06/07/2004] [Indexed: 05/24/2023]
Abstract
We implemented the heterodyne near-field scattering (HNFS) technique [Appl. Phys. Lett. 81, 4109 (2002)]], showing that it is a fairly valid alternative to traditional elastic low-angle light scattering and quite suitable for studying complex fluids such as colloidal systems. With respect to the original work, we adopted a different data reduction scheme, which allowed us to improve significantly the performance of the technique, at levels of sensitivity and accuracy much higher than those achievable with classical low-angle light scattering instrumentation. This method also relaxes the requirements on the optical/mechanical stability of the experimental setup and allows for a real time analysis. The HNFS technique has been tested by using calibrated colloidal particles and its capability of performing accurate particle sizing was ascertained on both monodisperse and bimodal particle distributions. Nonstationary samples, such as aggregating colloidal solutions, were profitably studied, and their kinetics quantitatively characterized.
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Affiliation(s)
- F Ferri
- Dipartimento di Fisica e Matematica, Università dell'Insubria and INFM, Via Valleggio 11, Como I-22100, Italy.
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