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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....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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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. |
format | Online Article Text |
id | pubmed-6614844 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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