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Structural host-microbiota interaction networks
Hundreds of different species colonize multicellular organisms making them “metaorganisms”. A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insight...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638203/ https://www.ncbi.nlm.nih.gov/pubmed/29023448 http://dx.doi.org/10.1371/journal.pcbi.1005579 |
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author | Guven-Maiorov, Emine Tsai, Chung-Jung Nussinov, Ruth |
author_facet | Guven-Maiorov, Emine Tsai, Chung-Jung Nussinov, Ruth |
author_sort | Guven-Maiorov, Emine |
collection | PubMed |
description | Hundreds of different species colonize multicellular organisms making them “metaorganisms”. A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole–may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences. |
format | Online Article Text |
id | pubmed-5638203 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-56382032017-11-03 Structural host-microbiota interaction networks Guven-Maiorov, Emine Tsai, Chung-Jung Nussinov, Ruth PLoS Comput Biol Review Hundreds of different species colonize multicellular organisms making them “metaorganisms”. A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole–may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences. Public Library of Science 2017-10-12 /pmc/articles/PMC5638203/ /pubmed/29023448 http://dx.doi.org/10.1371/journal.pcbi.1005579 Text en © 2017 Guven-Maiorov et al 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 use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Review Guven-Maiorov, Emine Tsai, Chung-Jung Nussinov, Ruth Structural host-microbiota interaction networks |
title | Structural host-microbiota interaction networks |
title_full | Structural host-microbiota interaction networks |
title_fullStr | Structural host-microbiota interaction networks |
title_full_unstemmed | Structural host-microbiota interaction networks |
title_short | Structural host-microbiota interaction networks |
title_sort | structural host-microbiota interaction networks |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638203/ https://www.ncbi.nlm.nih.gov/pubmed/29023448 http://dx.doi.org/10.1371/journal.pcbi.1005579 |
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