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Gene regulatory network inference in soybean upon infection by Phytophthora sojae
Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean (Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Histori...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328377/ https://www.ncbi.nlm.nih.gov/pubmed/37418376 http://dx.doi.org/10.1371/journal.pone.0287590 |
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author | Hale, Brett Ratnayake, Sandaruwan Flory, Ashley Wijeratne, Ravindu Schmidt, Clarice Robertson, Alison E. Wijeratne, Asela J. |
author_facet | Hale, Brett Ratnayake, Sandaruwan Flory, Ashley Wijeratne, Ravindu Schmidt, Clarice Robertson, Alison E. Wijeratne, Asela J. |
author_sort | Hale, Brett |
collection | PubMed |
description | Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean (Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PRR has entailed host genetic resistance (both vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverse P. sojae pathotypes necessitates developing novel technologies to attenuate PRR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data and deep learning to elucidate molecular features in soybean following infection by P. sojae. In doing so, we generated transcriptomes to identify differentially expressed genes (DEGs) during compatible and incompatible interactions with P. sojae and a mock inoculation. The expression data were then used to select two defense-related transcription factors (TFs) belonging to WRKY and RAV families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites in the soybean genome. These bound sites were used to train Deep Neural Networks with convolutional and recurrent layers to predict new target sites of WRKY and RAV family members in the DEG set. Moreover, we leveraged publicly available Arabidopsis (Arabidopsis thaliana) DAP-seq data for five TF families enriched in our transcriptome analysis to train similar models. These Arabidopsis data-based models were used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target gene interactions that orchestrate an immune response against P. sojae. Information herein provides novel insight into molecular plant-pathogen interaction and may prove useful in developing soybean cultivars with more durable resistance to P. sojae. |
format | Online Article Text |
id | pubmed-10328377 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103283772023-07-08 Gene regulatory network inference in soybean upon infection by Phytophthora sojae Hale, Brett Ratnayake, Sandaruwan Flory, Ashley Wijeratne, Ravindu Schmidt, Clarice Robertson, Alison E. Wijeratne, Asela J. PLoS One Research Article Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean (Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PRR has entailed host genetic resistance (both vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverse P. sojae pathotypes necessitates developing novel technologies to attenuate PRR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data and deep learning to elucidate molecular features in soybean following infection by P. sojae. In doing so, we generated transcriptomes to identify differentially expressed genes (DEGs) during compatible and incompatible interactions with P. sojae and a mock inoculation. The expression data were then used to select two defense-related transcription factors (TFs) belonging to WRKY and RAV families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites in the soybean genome. These bound sites were used to train Deep Neural Networks with convolutional and recurrent layers to predict new target sites of WRKY and RAV family members in the DEG set. Moreover, we leveraged publicly available Arabidopsis (Arabidopsis thaliana) DAP-seq data for five TF families enriched in our transcriptome analysis to train similar models. These Arabidopsis data-based models were used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target gene interactions that orchestrate an immune response against P. sojae. Information herein provides novel insight into molecular plant-pathogen interaction and may prove useful in developing soybean cultivars with more durable resistance to P. sojae. Public Library of Science 2023-07-07 /pmc/articles/PMC10328377/ /pubmed/37418376 http://dx.doi.org/10.1371/journal.pone.0287590 Text en © 2023 Hale et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 | Research Article Hale, Brett Ratnayake, Sandaruwan Flory, Ashley Wijeratne, Ravindu Schmidt, Clarice Robertson, Alison E. Wijeratne, Asela J. Gene regulatory network inference in soybean upon infection by Phytophthora sojae |
title | Gene regulatory network inference in soybean upon infection by Phytophthora sojae |
title_full | Gene regulatory network inference in soybean upon infection by Phytophthora sojae |
title_fullStr | Gene regulatory network inference in soybean upon infection by Phytophthora sojae |
title_full_unstemmed | Gene regulatory network inference in soybean upon infection by Phytophthora sojae |
title_short | Gene regulatory network inference in soybean upon infection by Phytophthora sojae |
title_sort | gene regulatory network inference in soybean upon infection by phytophthora sojae |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328377/ https://www.ncbi.nlm.nih.gov/pubmed/37418376 http://dx.doi.org/10.1371/journal.pone.0287590 |
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