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An integrative method to predict signalling perturbations for cellular transitions

Induction of specific cellular transitions is of clinical importance, as it allows to revert disease cellular phenotype, or induce cellular reprogramming and differentiation for regenerative medicine. Signalling is a convenient way to accomplish such transitions without transfer of genetic material....

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Detalles Bibliográficos
Autores principales: Zaffaroni, Gaia, Okawa, Satoshi, Morales-Ruiz, Manuel, del Sol, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6614844/
https://www.ncbi.nlm.nih.gov/pubmed/30949696
http://dx.doi.org/10.1093/nar/gkz232
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author Zaffaroni, Gaia
Okawa, Satoshi
Morales-Ruiz, Manuel
del Sol, Antonio
author_facet Zaffaroni, Gaia
Okawa, Satoshi
Morales-Ruiz, Manuel
del Sol, Antonio
author_sort Zaffaroni, Gaia
collection PubMed
description Induction of specific cellular transitions is of clinical importance, as it allows to revert disease cellular phenotype, or induce cellular reprogramming and differentiation for regenerative medicine. Signalling is a convenient way to accomplish such transitions without transfer of genetic material. Here we present the first general computational method that systematically predicts signalling molecules, whose perturbations induce desired cellular transitions. This probabilistic method integrates gene regulatory networks (GRNs) with manually-curated signalling pathways obtained from MetaCore from Clarivate Analytics, to model how signalling cues are received and processed in the GRN. The method was applied to 219 cellular transition examples, including cell type transitions, and overall correctly predicted experimentally validated signalling molecules, consistently outperforming other well-established approaches, such as differential gene expression and pathway enrichment analyses. Further, we validated our method predictions in the case of rat cirrhotic liver, and identified the activation of angiopoietins receptor Tie2 as a potential target for reverting the disease phenotype. Experimental results indicated that this perturbation induced desired changes in the gene expression of key TFs involved in fibrosis and angiogenesis. Importantly, this method only requires gene expression data of the initial and desired cell states, and therefore is suited for the discovery of signalling interventions for disease treatments and cellular therapies.
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spelling pubmed-66148442019-07-12 An integrative method to predict signalling perturbations for cellular transitions Zaffaroni, Gaia Okawa, Satoshi Morales-Ruiz, Manuel del Sol, Antonio Nucleic Acids Res Methods Online Induction of specific cellular transitions is of clinical importance, as it allows to revert disease cellular phenotype, or induce cellular reprogramming and differentiation for regenerative medicine. Signalling is a convenient way to accomplish such transitions without transfer of genetic material. Here we present the first general computational method that systematically predicts signalling molecules, whose perturbations induce desired cellular transitions. This probabilistic method integrates gene regulatory networks (GRNs) with manually-curated signalling pathways obtained from MetaCore from Clarivate Analytics, to model how signalling cues are received and processed in the GRN. The method was applied to 219 cellular transition examples, including cell type transitions, and overall correctly predicted experimentally validated signalling molecules, consistently outperforming other well-established approaches, such as differential gene expression and pathway enrichment analyses. Further, we validated our method predictions in the case of rat cirrhotic liver, and identified the activation of angiopoietins receptor Tie2 as a potential target for reverting the disease phenotype. Experimental results indicated that this perturbation induced desired changes in the gene expression of key TFs involved in fibrosis and angiogenesis. Importantly, this method only requires gene expression data of the initial and desired cell states, and therefore is suited for the discovery of signalling interventions for disease treatments and cellular therapies. Oxford University Press 2019-07-09 2019-04-05 /pmc/articles/PMC6614844/ /pubmed/30949696 http://dx.doi.org/10.1093/nar/gkz232 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Zaffaroni, Gaia
Okawa, Satoshi
Morales-Ruiz, Manuel
del Sol, Antonio
An integrative method to predict signalling perturbations for cellular transitions
title An integrative method to predict signalling perturbations for cellular transitions
title_full An integrative method to predict signalling perturbations for cellular transitions
title_fullStr An integrative method to predict signalling perturbations for cellular transitions
title_full_unstemmed An integrative method to predict signalling perturbations for cellular transitions
title_short An integrative method to predict signalling perturbations for cellular transitions
title_sort integrative method to predict signalling perturbations for cellular transitions
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6614844/
https://www.ncbi.nlm.nih.gov/pubmed/30949696
http://dx.doi.org/10.1093/nar/gkz232
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