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Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction

In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to b...

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Autores principales: Larsen, Peter E., Sreedasyam, Avinash, Trivedi, Geetika, Desai, Shalaka, Dai, Yang, Cseke, Leland J., Collart, Frank R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4717292/
https://www.ncbi.nlm.nih.gov/pubmed/26834754
http://dx.doi.org/10.3389/fpls.2015.01061
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author Larsen, Peter E.
Sreedasyam, Avinash
Trivedi, Geetika
Desai, Shalaka
Dai, Yang
Cseke, Leland J.
Collart, Frank R.
author_facet Larsen, Peter E.
Sreedasyam, Avinash
Trivedi, Geetika
Desai, Shalaka
Dai, Yang
Cseke, Leland J.
Collart, Frank R.
author_sort Larsen, Peter E.
collection PubMed
description In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root—mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensor systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with 15 transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and jasmonic acid. This multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.
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spelling pubmed-47172922016-01-29 Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction Larsen, Peter E. Sreedasyam, Avinash Trivedi, Geetika Desai, Shalaka Dai, Yang Cseke, Leland J. Collart, Frank R. Front Plant Sci Genetics In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root—mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensor systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with 15 transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and jasmonic acid. This multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation. Frontiers Media S.A. 2016-01-19 /pmc/articles/PMC4717292/ /pubmed/26834754 http://dx.doi.org/10.3389/fpls.2015.01061 Text en Copyright © 2016 Larsen, Sreedasyam, Trivedi, Desai, Dai, Cseke and Collart. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Larsen, Peter E.
Sreedasyam, Avinash
Trivedi, Geetika
Desai, Shalaka
Dai, Yang
Cseke, Leland J.
Collart, Frank R.
Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction
title Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction
title_full Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction
title_fullStr Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction
title_full_unstemmed Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction
title_short Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction
title_sort multi-omics approach identifies molecular mechanisms of plant-fungus mycorrhizal interaction
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4717292/
https://www.ncbi.nlm.nih.gov/pubmed/26834754
http://dx.doi.org/10.3389/fpls.2015.01061
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