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Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy

BACKGROUND: Muscle atrophy, an involuntary loss of muscle mass, is involved in various diseases and sometimes leads to mortality. However, therapeutics for muscle atrophy thus far have had limited effects. Here, we present a new approach for therapeutic target prediction using Petri net simulation o...

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Autores principales: Jung, Jinmyung, Kwon, Mijin, Bae, Sunghwa, Yim, Soorin, Lee, Doheon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5838966/
https://www.ncbi.nlm.nih.gov/pubmed/29506508
http://dx.doi.org/10.1186/s12918-018-0555-0
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author Jung, Jinmyung
Kwon, Mijin
Bae, Sunghwa
Yim, Soorin
Lee, Doheon
author_facet Jung, Jinmyung
Kwon, Mijin
Bae, Sunghwa
Yim, Soorin
Lee, Doheon
author_sort Jung, Jinmyung
collection PubMed
description BACKGROUND: Muscle atrophy, an involuntary loss of muscle mass, is involved in various diseases and sometimes leads to mortality. However, therapeutics for muscle atrophy thus far have had limited effects. Here, we present a new approach for therapeutic target prediction using Petri net simulation of the status of phosphorylation, with a reasonable assumption that the recovery of abnormally phosphorylated proteins can be a treatment for muscle atrophy. RESULTS: The Petri net model was employed to simulate phosphorylation status in three states, i.e. reference, atrophic and each gene-inhibited state based on the myocyte-specific phosphorylation network. Here, we newly devised a phosphorylation specific Petri net that involves two types of transitions (phosphorylation or de-phosphorylation) and two types of places (activation with or without phosphorylation). Before predicting therapeutic targets, the simulation results in reference and atrophic states were validated by Western blotting experiments detecting five marker proteins, i.e. RELA, SMAD2, SMAD3, FOXO1 and FOXO3. Finally, we determined 37 potential therapeutic targets whose inhibition recovers the phosphorylation status from an atrophic state as indicated by the five validated marker proteins. In the evaluation, we confirmed that the 37 potential targets were enriched for muscle atrophy-related terms such as actin and muscle contraction processes, and they were also significantly overlapping with the genes associated with muscle atrophy reported in the Comparative Toxicogenomics Database (p-value < 0.05). Furthermore, we noticed that they included several proteins that could not be characterized by the shortest path analysis. The three potential targets, i.e. BMPR1B, ROCK, and LEPR, were manually validated with the literature. CONCLUSIONS: In this study, we suggest a new approach to predict potential therapeutic targets of muscle atrophy with an analysis of phosphorylation status simulated by Petri net. We generated a list of the potential therapeutic targets whose inhibition recovers abnormally phosphorylated proteins in an atrophic state. They were evaluated by various approaches, such as Western blotting, GO terms, literature, known muscle atrophy-related genes and shortest path analysis. We expect the new proposed strategy to provide an understanding of phosphorylation status in muscle atrophy and to provide assistance towards identifying new therapies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0555-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-58389662018-03-09 Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy Jung, Jinmyung Kwon, Mijin Bae, Sunghwa Yim, Soorin Lee, Doheon BMC Syst Biol Research Article BACKGROUND: Muscle atrophy, an involuntary loss of muscle mass, is involved in various diseases and sometimes leads to mortality. However, therapeutics for muscle atrophy thus far have had limited effects. Here, we present a new approach for therapeutic target prediction using Petri net simulation of the status of phosphorylation, with a reasonable assumption that the recovery of abnormally phosphorylated proteins can be a treatment for muscle atrophy. RESULTS: The Petri net model was employed to simulate phosphorylation status in three states, i.e. reference, atrophic and each gene-inhibited state based on the myocyte-specific phosphorylation network. Here, we newly devised a phosphorylation specific Petri net that involves two types of transitions (phosphorylation or de-phosphorylation) and two types of places (activation with or without phosphorylation). Before predicting therapeutic targets, the simulation results in reference and atrophic states were validated by Western blotting experiments detecting five marker proteins, i.e. RELA, SMAD2, SMAD3, FOXO1 and FOXO3. Finally, we determined 37 potential therapeutic targets whose inhibition recovers the phosphorylation status from an atrophic state as indicated by the five validated marker proteins. In the evaluation, we confirmed that the 37 potential targets were enriched for muscle atrophy-related terms such as actin and muscle contraction processes, and they were also significantly overlapping with the genes associated with muscle atrophy reported in the Comparative Toxicogenomics Database (p-value < 0.05). Furthermore, we noticed that they included several proteins that could not be characterized by the shortest path analysis. The three potential targets, i.e. BMPR1B, ROCK, and LEPR, were manually validated with the literature. CONCLUSIONS: In this study, we suggest a new approach to predict potential therapeutic targets of muscle atrophy with an analysis of phosphorylation status simulated by Petri net. We generated a list of the potential therapeutic targets whose inhibition recovers abnormally phosphorylated proteins in an atrophic state. They were evaluated by various approaches, such as Western blotting, GO terms, literature, known muscle atrophy-related genes and shortest path analysis. We expect the new proposed strategy to provide an understanding of phosphorylation status in muscle atrophy and to provide assistance towards identifying new therapies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0555-0) contains supplementary material, which is available to authorized users. BioMed Central 2018-03-05 /pmc/articles/PMC5838966/ /pubmed/29506508 http://dx.doi.org/10.1186/s12918-018-0555-0 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Jung, Jinmyung
Kwon, Mijin
Bae, Sunghwa
Yim, Soorin
Lee, Doheon
Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy
title Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy
title_full Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy
title_fullStr Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy
title_full_unstemmed Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy
title_short Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy
title_sort petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5838966/
https://www.ncbi.nlm.nih.gov/pubmed/29506508
http://dx.doi.org/10.1186/s12918-018-0555-0
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