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Bayesian modeling of plant drought resistance pathway
BACKGROUND: Plants are sessile organisms and are unable to relocate to favorable locations under extreme environmental conditions. Hence they have no choice but to acclimate and eventually adapt to the severe conditions to ensure their survival. As traditional methods of bolstering plant defense aga...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417252/ https://www.ncbi.nlm.nih.gov/pubmed/30866813 http://dx.doi.org/10.1186/s12870-019-1684-3 |
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author | Lahiri, Aditya Venkatasubramani, Priyadharshini S. Datta, Aniruddha |
author_facet | Lahiri, Aditya Venkatasubramani, Priyadharshini S. Datta, Aniruddha |
author_sort | Lahiri, Aditya |
collection | PubMed |
description | BACKGROUND: Plants are sessile organisms and are unable to relocate to favorable locations under extreme environmental conditions. Hence they have no choice but to acclimate and eventually adapt to the severe conditions to ensure their survival. As traditional methods of bolstering plant defense against stressful conditions come to their biological limit, we require newer methods that can allow us to strengthen plants’ internal defense mechanism. These factors motivated us to look into the genetic networks of plants. The WRKY transcription factors are well known for their role in plant defense against biotic stresses, but recent studies have shed light on their activities against abiotic stresses such as drought. We modeled this network of WRKY transcription factors using Bayesian networks and applied inference algorithm to find the best regulators of drought response. Biologically intervening (activating/inhibiting) these regulators can bolster the defense response of plants against droughts. RESULT: We used real world data from the NCBI GEO database and synthetic data generated from dependencies in the Bayesian network to learn the network parameters. These parameters were estimated using both a Bayesian and a frequentist approach. The two sets of parameters were used in a utility-based inference algorithm to determine the best regulator of plant drought response in the WRKY transcription factor network. CONCLUSION: Our analysis revealed that activating the transcription factor WRKY18 had the highest likelihood of inducing drought response among all the other elements of the WRKY transcription factor network. Our observation was also supported by biological literature, as WRKY18 is known to regulate drought responsive genes positively. We also found that activating the protein complex WRKY60-60 had the second highest likelihood of inducing drought defense response. Consistent with the existing biological literature, we also found the transcription factor WRKY40 and the protein complex WRKY40-40 to suppress drought response. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12870-019-1684-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6417252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64172522019-03-25 Bayesian modeling of plant drought resistance pathway Lahiri, Aditya Venkatasubramani, Priyadharshini S. Datta, Aniruddha BMC Plant Biol Research Article BACKGROUND: Plants are sessile organisms and are unable to relocate to favorable locations under extreme environmental conditions. Hence they have no choice but to acclimate and eventually adapt to the severe conditions to ensure their survival. As traditional methods of bolstering plant defense against stressful conditions come to their biological limit, we require newer methods that can allow us to strengthen plants’ internal defense mechanism. These factors motivated us to look into the genetic networks of plants. The WRKY transcription factors are well known for their role in plant defense against biotic stresses, but recent studies have shed light on their activities against abiotic stresses such as drought. We modeled this network of WRKY transcription factors using Bayesian networks and applied inference algorithm to find the best regulators of drought response. Biologically intervening (activating/inhibiting) these regulators can bolster the defense response of plants against droughts. RESULT: We used real world data from the NCBI GEO database and synthetic data generated from dependencies in the Bayesian network to learn the network parameters. These parameters were estimated using both a Bayesian and a frequentist approach. The two sets of parameters were used in a utility-based inference algorithm to determine the best regulator of plant drought response in the WRKY transcription factor network. CONCLUSION: Our analysis revealed that activating the transcription factor WRKY18 had the highest likelihood of inducing drought response among all the other elements of the WRKY transcription factor network. Our observation was also supported by biological literature, as WRKY18 is known to regulate drought responsive genes positively. We also found that activating the protein complex WRKY60-60 had the second highest likelihood of inducing drought defense response. Consistent with the existing biological literature, we also found the transcription factor WRKY40 and the protein complex WRKY40-40 to suppress drought response. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12870-019-1684-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-03-12 /pmc/articles/PMC6417252/ /pubmed/30866813 http://dx.doi.org/10.1186/s12870-019-1684-3 Text en © The Author(s) 2019 Open Access This 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 Lahiri, Aditya Venkatasubramani, Priyadharshini S. Datta, Aniruddha Bayesian modeling of plant drought resistance pathway |
title | Bayesian modeling of plant drought resistance pathway |
title_full | Bayesian modeling of plant drought resistance pathway |
title_fullStr | Bayesian modeling of plant drought resistance pathway |
title_full_unstemmed | Bayesian modeling of plant drought resistance pathway |
title_short | Bayesian modeling of plant drought resistance pathway |
title_sort | bayesian modeling of plant drought resistance pathway |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417252/ https://www.ncbi.nlm.nih.gov/pubmed/30866813 http://dx.doi.org/10.1186/s12870-019-1684-3 |
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