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Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode

The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection....

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Autores principales: Nissan, Nour, Hooker, Julia, Arezza, Eric, Dick, Kevin, Golshani, Ashkan, Mimee, Benjamin, Cober, Elroy, Green, James, Samanfar, Bahram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319130/
https://www.ncbi.nlm.nih.gov/pubmed/37409347
http://dx.doi.org/10.3389/fbinf.2023.1199675
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author Nissan, Nour
Hooker, Julia
Arezza, Eric
Dick, Kevin
Golshani, Ashkan
Mimee, Benjamin
Cober, Elroy
Green, James
Samanfar, Bahram
author_facet Nissan, Nour
Hooker, Julia
Arezza, Eric
Dick, Kevin
Golshani, Ashkan
Mimee, Benjamin
Cober, Elroy
Green, James
Samanfar, Bahram
author_sort Nissan, Nour
collection PubMed
description The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein–protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein–protein interaction predictors, the Protein–protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a “guilt by association” in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean.
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spelling pubmed-103191302023-07-05 Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode Nissan, Nour Hooker, Julia Arezza, Eric Dick, Kevin Golshani, Ashkan Mimee, Benjamin Cober, Elroy Green, James Samanfar, Bahram Front Bioinform Bioinformatics The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein–protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein–protein interaction predictors, the Protein–protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a “guilt by association” in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean. Frontiers Media S.A. 2023-06-20 /pmc/articles/PMC10319130/ /pubmed/37409347 http://dx.doi.org/10.3389/fbinf.2023.1199675 Text en Copyright © 2023 Nissan, Hooker, Arezza, Dick, Golshani, Mimee, Cober, Green and Samanfar. https://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) and the copyright owner(s) 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 Bioinformatics
Nissan, Nour
Hooker, Julia
Arezza, Eric
Dick, Kevin
Golshani, Ashkan
Mimee, Benjamin
Cober, Elroy
Green, James
Samanfar, Bahram
Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode
title Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode
title_full Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode
title_fullStr Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode
title_full_unstemmed Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode
title_short Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode
title_sort large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319130/
https://www.ncbi.nlm.nih.gov/pubmed/37409347
http://dx.doi.org/10.3389/fbinf.2023.1199675
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