RNA-Seq analysis of rye-grass transcriptomic response to an herbicide inhibiting acetolactate-synthase identifies transcripts linked to non-target-site-based resistance

RNA-Seq analysis of rye-grass transcriptomic response to an herbicide inhibiting... Non-target-site resistance (NTSR) to herbicides that disrupts agricultural weed control is a worldwide concern for food security. NTSR is considered a polygenic adaptive trait driven by differential gene regulation in resistant plants. Little is known about its genetic determinism, which precludes NTSR diagnosis and evolutionary studies. We used Illumina RNA-sequencing to investigate transcriptomic differences between plants from the global major weed rye-grass sensitive or resistant to the acetolactate-synthase (ALS) inhibiting herbicide pyroxsulam. Plants were collected before and along a time-course after herbicide application. De novo transcriptome assembly yielded a resource (LOLbase) including 92,381 contigs representing potentially active transcripts that were assigned putative annotations. Early effects of ALS inhibition consistent with the literature were observed in resistant and sensitive plants, proving LOLbase data were relevant to study herbicide response. Comparison of resistant and sensitive plants identified 30 candidate NTSR contigs. Further validation using 212 plants resistant or sensitive to pyroxsulam and/or to the ALS inhibitors iodosulfuron + mesosulfuron confirmed four contigs (two cytochromes P450, one glycosyl-transferase and one glutathione-S-transferase) were NTSR markers which combined expression levels could reliably identify resistant plants. This work confirmed that NTSR is driven by differential gene expression and involves different mechanisms. It provided tools and foundation for subsequent NTSR investigations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Plant Molecular Biology Springer Journals

RNA-Seq analysis of rye-grass transcriptomic response to an herbicide inhibiting acetolactate-synthase identifies transcripts linked to non-target-site-based resistance

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Publisher
Springer Netherlands
Copyright
Copyright © 2015 by Springer Science+Business Media Dordrecht
Subject
Life Sciences; Plant Sciences; Biochemistry, general; Plant Pathology
ISSN
0167-4412
eISSN
1573-5028
D.O.I.
10.1007/s11103-015-0292-3
Publisher site
See Article on Publisher Site

Abstract

Non-target-site resistance (NTSR) to herbicides that disrupts agricultural weed control is a worldwide concern for food security. NTSR is considered a polygenic adaptive trait driven by differential gene regulation in resistant plants. Little is known about its genetic determinism, which precludes NTSR diagnosis and evolutionary studies. We used Illumina RNA-sequencing to investigate transcriptomic differences between plants from the global major weed rye-grass sensitive or resistant to the acetolactate-synthase (ALS) inhibiting herbicide pyroxsulam. Plants were collected before and along a time-course after herbicide application. De novo transcriptome assembly yielded a resource (LOLbase) including 92,381 contigs representing potentially active transcripts that were assigned putative annotations. Early effects of ALS inhibition consistent with the literature were observed in resistant and sensitive plants, proving LOLbase data were relevant to study herbicide response. Comparison of resistant and sensitive plants identified 30 candidate NTSR contigs. Further validation using 212 plants resistant or sensitive to pyroxsulam and/or to the ALS inhibitors iodosulfuron + mesosulfuron confirmed four contigs (two cytochromes P450, one glycosyl-transferase and one glutathione-S-transferase) were NTSR markers which combined expression levels could reliably identify resistant plants. This work confirmed that NTSR is driven by differential gene expression and involves different mechanisms. It provided tools and foundation for subsequent NTSR investigations.

Journal

Plant Molecular BiologySpringer Journals

Published: Jan 31, 2015

References

  • Improved scoring of functional groups from gene expression data by decorrelating GO graph structure
    Alexa, A; Rahnenfuhrer, J; Lengauer, T
  • The evolution of weeds
    Baker, HG

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