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The Binary Classification of Protein Kinases

In an earlier publication a binary model for chronic diseases classification has been proposed. According to the model, chronic diseases were classified as “high Treg” or “low Treg” diseases, depending on whether the immune response is anti- or pro-inflammatory and assuming that regulatory T cells a...

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Autor principal: Elkoshi, Zeev
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988341/
https://www.ncbi.nlm.nih.gov/pubmed/33776467
http://dx.doi.org/10.2147/JIR.S303750
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author Elkoshi, Zeev
author_facet Elkoshi, Zeev
author_sort Elkoshi, Zeev
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description In an earlier publication a binary model for chronic diseases classification has been proposed. According to the model, chronic diseases were classified as “high Treg” or “low Treg” diseases, depending on whether the immune response is anti- or pro-inflammatory and assuming that regulatory T cells are major determinants of the response. It turned out that most cancers are “high Treg” diseases, while autoimmune diseases are “low Treg”. This paper proposes a molecular cause for this binary response. The mechanism proposed depends on the effect of protein kinases on the immune system. Thus, protein kinases are classified as anti- or pro-inflammatory kinases depending on whether they drive “high Treg” or “low Treg” diseases. Observations reported in the earlier publication can be described in terms of anti-inflammatory kinase (AIK) or pro-inflammatory kinase (PIK) activity. Analysis of literature data reveals that the two classes of kinases display distinctive properties relating to their interactions with pathogens and environmental factors. Pathogens that promote Treg activity (“high Treg” pathogens) activate AIKs, while pathogens that suppress Treg activity (“low Treg” pathogens) activate PIKs. Diseases driven by AIKs are associated with “high Treg” pathogens while those diseases driven by PIKs are associated with “low Treg” pathogens. By promoting the activity of AIKs, alcohol consumption increases the risk of “high Treg” cancers but decreases the risk of some “low Treg” autoimmune diseases. JAK1 gain-of-function mutations are observed at high frequencies in autoimmune diseases while JAK1 loss-of-function mutations are observed at high frequencies in cancers with high tumor-infiltrating Tregs. It should also be noted that the corresponding two classes of protein kinase inhibitors are mutually exclusive in terms of their approved therapeutic indications. There is no protein kinase inhibitor that is approved for the treatment of both autoimmune diseases and “high Treg” cancers. Although there are exceptions to the conclusions presented above, these conclusions are supported by the great bulk of published data. It therefore seems that the binary division of protein kinases is a useful tool for elucidating (at the molecular level) many distinctive properties of cancers and autoimmune diseases.
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spelling pubmed-79883412021-03-25 The Binary Classification of Protein Kinases Elkoshi, Zeev J Inflamm Res Hypothesis In an earlier publication a binary model for chronic diseases classification has been proposed. According to the model, chronic diseases were classified as “high Treg” or “low Treg” diseases, depending on whether the immune response is anti- or pro-inflammatory and assuming that regulatory T cells are major determinants of the response. It turned out that most cancers are “high Treg” diseases, while autoimmune diseases are “low Treg”. This paper proposes a molecular cause for this binary response. The mechanism proposed depends on the effect of protein kinases on the immune system. Thus, protein kinases are classified as anti- or pro-inflammatory kinases depending on whether they drive “high Treg” or “low Treg” diseases. Observations reported in the earlier publication can be described in terms of anti-inflammatory kinase (AIK) or pro-inflammatory kinase (PIK) activity. Analysis of literature data reveals that the two classes of kinases display distinctive properties relating to their interactions with pathogens and environmental factors. Pathogens that promote Treg activity (“high Treg” pathogens) activate AIKs, while pathogens that suppress Treg activity (“low Treg” pathogens) activate PIKs. Diseases driven by AIKs are associated with “high Treg” pathogens while those diseases driven by PIKs are associated with “low Treg” pathogens. By promoting the activity of AIKs, alcohol consumption increases the risk of “high Treg” cancers but decreases the risk of some “low Treg” autoimmune diseases. JAK1 gain-of-function mutations are observed at high frequencies in autoimmune diseases while JAK1 loss-of-function mutations are observed at high frequencies in cancers with high tumor-infiltrating Tregs. It should also be noted that the corresponding two classes of protein kinase inhibitors are mutually exclusive in terms of their approved therapeutic indications. There is no protein kinase inhibitor that is approved for the treatment of both autoimmune diseases and “high Treg” cancers. Although there are exceptions to the conclusions presented above, these conclusions are supported by the great bulk of published data. It therefore seems that the binary division of protein kinases is a useful tool for elucidating (at the molecular level) many distinctive properties of cancers and autoimmune diseases. Dove 2021-03-18 /pmc/articles/PMC7988341/ /pubmed/33776467 http://dx.doi.org/10.2147/JIR.S303750 Text en © 2021 Elkoshi. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Hypothesis
Elkoshi, Zeev
The Binary Classification of Protein Kinases
title The Binary Classification of Protein Kinases
title_full The Binary Classification of Protein Kinases
title_fullStr The Binary Classification of Protein Kinases
title_full_unstemmed The Binary Classification of Protein Kinases
title_short The Binary Classification of Protein Kinases
title_sort binary classification of protein kinases
topic Hypothesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988341/
https://www.ncbi.nlm.nih.gov/pubmed/33776467
http://dx.doi.org/10.2147/JIR.S303750
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