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Reconstruction and analysis of a large-scale binary Ras-effector signaling network

BACKGROUND: Ras is a key cellular signaling hub that controls numerous cell fates via multiple downstream effector pathways. While pathways downstream of effectors such as Raf, PI3K and RalGDS are extensively described in the literature, how other effectors signal downstream of Ras is often still en...

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Autores principales: Catozzi, Simona, Ternet, Camille, Gourrege, Alize, Wynne, Kieran, Oliviero, Giorgio, Kiel, Christina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8896392/
https://www.ncbi.nlm.nih.gov/pubmed/35246154
http://dx.doi.org/10.1186/s12964-022-00823-5
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author Catozzi, Simona
Ternet, Camille
Gourrege, Alize
Wynne, Kieran
Oliviero, Giorgio
Kiel, Christina
author_facet Catozzi, Simona
Ternet, Camille
Gourrege, Alize
Wynne, Kieran
Oliviero, Giorgio
Kiel, Christina
author_sort Catozzi, Simona
collection PubMed
description BACKGROUND: Ras is a key cellular signaling hub that controls numerous cell fates via multiple downstream effector pathways. While pathways downstream of effectors such as Raf, PI3K and RalGDS are extensively described in the literature, how other effectors signal downstream of Ras is often still enigmatic. METHODS: A comprehensive and unbiased Ras-effector network was reconstructed downstream of 43 effector proteins (converging onto 12 effector classes) using public pathway and protein–protein interaction (PPI) databases. The output is an oriented graph of pairwise interactions defining a 3-layer signaling network downstream of Ras. The 2290 proteins comprising the network were studied for their implication in signaling crosstalk and feedbacks, their subcellular localizations, and their cellular functions. RESULTS: The final Ras-effector network consists of 2290 proteins that are connected via 19,080 binary PPIs, increasingly distributed across the downstream layers, with 441 PPIs in layer 1, 1660 in layer 2, and 16,979 in layer 3. We identified a high level of crosstalk among proteins of the 12 effector classes. A class-specific Ras sub-network was generated in CellDesigner (.xml file) and a functional enrichment analysis thereof shows that 58% of the processes have previously been associated to a respective effector pathway, with the remaining providing insights into novel and unexplored functions of specific effector pathways. CONCLUSIONS: Our large-scale and cell general Ras-effector network is a crucial steppingstone towards defining the network boundaries. It constitutes a ‘reference interactome’ and can be contextualized for specific conditions, e.g. different cell types or biopsy material obtained from cancer patients. Further, it can serve as a basis for elucidating systems properties, such as input–output relationships, crosstalk, and pathway redundancy. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12964-022-00823-5.
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spelling pubmed-88963922022-03-14 Reconstruction and analysis of a large-scale binary Ras-effector signaling network Catozzi, Simona Ternet, Camille Gourrege, Alize Wynne, Kieran Oliviero, Giorgio Kiel, Christina Cell Commun Signal Research BACKGROUND: Ras is a key cellular signaling hub that controls numerous cell fates via multiple downstream effector pathways. While pathways downstream of effectors such as Raf, PI3K and RalGDS are extensively described in the literature, how other effectors signal downstream of Ras is often still enigmatic. METHODS: A comprehensive and unbiased Ras-effector network was reconstructed downstream of 43 effector proteins (converging onto 12 effector classes) using public pathway and protein–protein interaction (PPI) databases. The output is an oriented graph of pairwise interactions defining a 3-layer signaling network downstream of Ras. The 2290 proteins comprising the network were studied for their implication in signaling crosstalk and feedbacks, their subcellular localizations, and their cellular functions. RESULTS: The final Ras-effector network consists of 2290 proteins that are connected via 19,080 binary PPIs, increasingly distributed across the downstream layers, with 441 PPIs in layer 1, 1660 in layer 2, and 16,979 in layer 3. We identified a high level of crosstalk among proteins of the 12 effector classes. A class-specific Ras sub-network was generated in CellDesigner (.xml file) and a functional enrichment analysis thereof shows that 58% of the processes have previously been associated to a respective effector pathway, with the remaining providing insights into novel and unexplored functions of specific effector pathways. CONCLUSIONS: Our large-scale and cell general Ras-effector network is a crucial steppingstone towards defining the network boundaries. It constitutes a ‘reference interactome’ and can be contextualized for specific conditions, e.g. different cell types or biopsy material obtained from cancer patients. Further, it can serve as a basis for elucidating systems properties, such as input–output relationships, crosstalk, and pathway redundancy. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12964-022-00823-5. BioMed Central 2022-03-04 /pmc/articles/PMC8896392/ /pubmed/35246154 http://dx.doi.org/10.1186/s12964-022-00823-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Catozzi, Simona
Ternet, Camille
Gourrege, Alize
Wynne, Kieran
Oliviero, Giorgio
Kiel, Christina
Reconstruction and analysis of a large-scale binary Ras-effector signaling network
title Reconstruction and analysis of a large-scale binary Ras-effector signaling network
title_full Reconstruction and analysis of a large-scale binary Ras-effector signaling network
title_fullStr Reconstruction and analysis of a large-scale binary Ras-effector signaling network
title_full_unstemmed Reconstruction and analysis of a large-scale binary Ras-effector signaling network
title_short Reconstruction and analysis of a large-scale binary Ras-effector signaling network
title_sort reconstruction and analysis of a large-scale binary ras-effector signaling network
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8896392/
https://www.ncbi.nlm.nih.gov/pubmed/35246154
http://dx.doi.org/10.1186/s12964-022-00823-5
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