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Integrating microarray analysis and the soybean genome to understand the soybeans iron deficiency response

BACKGROUND: Soybeans grown in the upper Midwestern United States often suffer from iron deficiency chlorosis, which results in yield loss at the end of the season. To better understand the effect of iron availability on soybean yield, we identified genes in two near isogenic lines with changes in ex...

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Autores principales: O'Rourke, Jamie A, Nelson, Rex T, Grant, David, Schmutz, Jeremy, Grimwood, Jane, Cannon, Steven, Vance, Carroll P, Graham, Michelle A, Shoemaker, Randy C
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2907705/
https://www.ncbi.nlm.nih.gov/pubmed/19678937
http://dx.doi.org/10.1186/1471-2164-10-376
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author O'Rourke, Jamie A
Nelson, Rex T
Grant, David
Schmutz, Jeremy
Grimwood, Jane
Cannon, Steven
Vance, Carroll P
Graham, Michelle A
Shoemaker, Randy C
author_facet O'Rourke, Jamie A
Nelson, Rex T
Grant, David
Schmutz, Jeremy
Grimwood, Jane
Cannon, Steven
Vance, Carroll P
Graham, Michelle A
Shoemaker, Randy C
author_sort O'Rourke, Jamie A
collection PubMed
description BACKGROUND: Soybeans grown in the upper Midwestern United States often suffer from iron deficiency chlorosis, which results in yield loss at the end of the season. To better understand the effect of iron availability on soybean yield, we identified genes in two near isogenic lines with changes in expression patterns when plants were grown in iron sufficient and iron deficient conditions. RESULTS: Transcriptional profiles of soybean (Glycine max, L. Merr) near isogenic lines Clark (PI548553, iron efficient) and IsoClark (PI547430, iron inefficient) grown under Fe-sufficient and Fe-limited conditions were analyzed and compared using the Affymetrix(® )GeneChip(® )Soybean Genome Array. There were 835 candidate genes in the Clark (PI548553) genotype and 200 candidate genes in the IsoClark (PI547430) genotype putatively involved in soybean's iron stress response. Of these candidate genes, fifty-eight genes in the Clark genotype were identified with a genetic location within known iron efficiency QTL and 21 in the IsoClark genotype. The arrays also identified 170 single feature polymorphisms (SFPs) specific to either Clark or IsoClark. A sliding window analysis of the microarray data and the 7X genome assembly coupled with an iterative model of the data showed the candidate genes are clustered in the genome. An analysis of 5' untranslated regions in the promoter of candidate genes identified 11 conserved motifs in 248 differentially expressed genes, all from the Clark genotype, representing 129 clusters identified earlier, confirming the cluster analysis results. CONCLUSION: These analyses have identified the first genes with expression patterns that are affected by iron stress and are located within QTL specific to iron deficiency stress. The genetic location and promoter motif analysis results support the hypothesis that the differentially expressed genes are co-regulated. The combined results of all analyses lead us to postulate iron inefficiency in soybean is a result of a mutation in a transcription factor(s), which controls the expression of genes required in inducing an iron stress response.
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spelling pubmed-29077052010-07-22 Integrating microarray analysis and the soybean genome to understand the soybeans iron deficiency response O'Rourke, Jamie A Nelson, Rex T Grant, David Schmutz, Jeremy Grimwood, Jane Cannon, Steven Vance, Carroll P Graham, Michelle A Shoemaker, Randy C BMC Genomics Research Article BACKGROUND: Soybeans grown in the upper Midwestern United States often suffer from iron deficiency chlorosis, which results in yield loss at the end of the season. To better understand the effect of iron availability on soybean yield, we identified genes in two near isogenic lines with changes in expression patterns when plants were grown in iron sufficient and iron deficient conditions. RESULTS: Transcriptional profiles of soybean (Glycine max, L. Merr) near isogenic lines Clark (PI548553, iron efficient) and IsoClark (PI547430, iron inefficient) grown under Fe-sufficient and Fe-limited conditions were analyzed and compared using the Affymetrix(® )GeneChip(® )Soybean Genome Array. There were 835 candidate genes in the Clark (PI548553) genotype and 200 candidate genes in the IsoClark (PI547430) genotype putatively involved in soybean's iron stress response. Of these candidate genes, fifty-eight genes in the Clark genotype were identified with a genetic location within known iron efficiency QTL and 21 in the IsoClark genotype. The arrays also identified 170 single feature polymorphisms (SFPs) specific to either Clark or IsoClark. A sliding window analysis of the microarray data and the 7X genome assembly coupled with an iterative model of the data showed the candidate genes are clustered in the genome. An analysis of 5' untranslated regions in the promoter of candidate genes identified 11 conserved motifs in 248 differentially expressed genes, all from the Clark genotype, representing 129 clusters identified earlier, confirming the cluster analysis results. CONCLUSION: These analyses have identified the first genes with expression patterns that are affected by iron stress and are located within QTL specific to iron deficiency stress. The genetic location and promoter motif analysis results support the hypothesis that the differentially expressed genes are co-regulated. The combined results of all analyses lead us to postulate iron inefficiency in soybean is a result of a mutation in a transcription factor(s), which controls the expression of genes required in inducing an iron stress response. BioMed Central 2009-08-13 /pmc/articles/PMC2907705/ /pubmed/19678937 http://dx.doi.org/10.1186/1471-2164-10-376 Text en Copyright ©2009 O'Rourke et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
O'Rourke, Jamie A
Nelson, Rex T
Grant, David
Schmutz, Jeremy
Grimwood, Jane
Cannon, Steven
Vance, Carroll P
Graham, Michelle A
Shoemaker, Randy C
Integrating microarray analysis and the soybean genome to understand the soybeans iron deficiency response
title Integrating microarray analysis and the soybean genome to understand the soybeans iron deficiency response
title_full Integrating microarray analysis and the soybean genome to understand the soybeans iron deficiency response
title_fullStr Integrating microarray analysis and the soybean genome to understand the soybeans iron deficiency response
title_full_unstemmed Integrating microarray analysis and the soybean genome to understand the soybeans iron deficiency response
title_short Integrating microarray analysis and the soybean genome to understand the soybeans iron deficiency response
title_sort integrating microarray analysis and the soybean genome to understand the soybeans iron deficiency response
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2907705/
https://www.ncbi.nlm.nih.gov/pubmed/19678937
http://dx.doi.org/10.1186/1471-2164-10-376
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