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Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping

Organisms engage in extensive cross-species molecular dialog, yet the underlying molecular actors are known for only a few interactions. Many techniques have been designed to uncover genes involved in signaling between organisms. Typically, these focus on only one of the partners. We developed an ex...

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Autores principales: Guo, Yuelong, Fudali, Sylwia, Gimeno, Jacinta, DiGennaro, Peter, Chang, Stella, Williamson, Valerie M., Bird, David McK., Nielsen, Dahlia M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5560814/
https://www.ncbi.nlm.nih.gov/pubmed/28642272
http://dx.doi.org/10.1534/genetics.117.202531
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author Guo, Yuelong
Fudali, Sylwia
Gimeno, Jacinta
DiGennaro, Peter
Chang, Stella
Williamson, Valerie M.
Bird, David McK.
Nielsen, Dahlia M.
author_facet Guo, Yuelong
Fudali, Sylwia
Gimeno, Jacinta
DiGennaro, Peter
Chang, Stella
Williamson, Valerie M.
Bird, David McK.
Nielsen, Dahlia M.
author_sort Guo, Yuelong
collection PubMed
description Organisms engage in extensive cross-species molecular dialog, yet the underlying molecular actors are known for only a few interactions. Many techniques have been designed to uncover genes involved in signaling between organisms. Typically, these focus on only one of the partners. We developed an expression quantitative trait locus (eQTL) mapping-based approach to identify cause-and-effect relationships between genes from two partners engaged in an interspecific interaction. We demonstrated the approach by assaying expression of 98 isogenic plants (Medicago truncatula), each inoculated with a genetically distinct line of the diploid parasitic nematode Meloidogyne hapla. With this design, systematic differences in gene expression across host plants could be mapped to genetic polymorphisms of their infecting parasites. The effects of parasite genotypes on plant gene expression were often substantial, with up to 90-fold (P = 3.2 × 10(−52)) changes in expression levels caused by individual parasite loci. Mapped loci included a number of pleiotropic sites, including one 87-kb parasite locus that modulated expression of >60 host genes. The 213 host genes identified were substantially enriched for transcription factors. We distilled higher-order connections between polymorphisms and genes from both species via network inference. To replicate our results and test whether effects were conserved across a broader host range, we performed a confirmatory experiment using M. hapla-infected tomato. This revealed that homologous genes were similarly affected. Finally, to validate the broader utility of cross-species eQTL mapping, we applied the strategy to data from a Salmonella infection study, successfully identifying polymorphisms in the human genome affecting bacterial expression.
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spelling pubmed-55608142017-08-21 Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping Guo, Yuelong Fudali, Sylwia Gimeno, Jacinta DiGennaro, Peter Chang, Stella Williamson, Valerie M. Bird, David McK. Nielsen, Dahlia M. Genetics Investigations Organisms engage in extensive cross-species molecular dialog, yet the underlying molecular actors are known for only a few interactions. Many techniques have been designed to uncover genes involved in signaling between organisms. Typically, these focus on only one of the partners. We developed an expression quantitative trait locus (eQTL) mapping-based approach to identify cause-and-effect relationships between genes from two partners engaged in an interspecific interaction. We demonstrated the approach by assaying expression of 98 isogenic plants (Medicago truncatula), each inoculated with a genetically distinct line of the diploid parasitic nematode Meloidogyne hapla. With this design, systematic differences in gene expression across host plants could be mapped to genetic polymorphisms of their infecting parasites. The effects of parasite genotypes on plant gene expression were often substantial, with up to 90-fold (P = 3.2 × 10(−52)) changes in expression levels caused by individual parasite loci. Mapped loci included a number of pleiotropic sites, including one 87-kb parasite locus that modulated expression of >60 host genes. The 213 host genes identified were substantially enriched for transcription factors. We distilled higher-order connections between polymorphisms and genes from both species via network inference. To replicate our results and test whether effects were conserved across a broader host range, we performed a confirmatory experiment using M. hapla-infected tomato. This revealed that homologous genes were similarly affected. Finally, to validate the broader utility of cross-species eQTL mapping, we applied the strategy to data from a Salmonella infection study, successfully identifying polymorphisms in the human genome affecting bacterial expression. Genetics Society of America 2017-08 2017-06-22 /pmc/articles/PMC5560814/ /pubmed/28642272 http://dx.doi.org/10.1534/genetics.117.202531 Text en Copyright © 2017 by the Genetics Society of America Available freely online through the author-supported open access option.
spellingShingle Investigations
Guo, Yuelong
Fudali, Sylwia
Gimeno, Jacinta
DiGennaro, Peter
Chang, Stella
Williamson, Valerie M.
Bird, David McK.
Nielsen, Dahlia M.
Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping
title Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping
title_full Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping
title_fullStr Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping
title_full_unstemmed Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping
title_short Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping
title_sort networks underpinning symbiosis revealed through cross-species eqtl mapping
topic Investigations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5560814/
https://www.ncbi.nlm.nih.gov/pubmed/28642272
http://dx.doi.org/10.1534/genetics.117.202531
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