LC-MS untargeted metabolomics assesses the delayed response of glufosinate treatment of transgenic glufosinate resistant (GR) buffalo grasses (Stenotaphrum secundatum L.)
- PMID: 33609206
- DOI: 10.1007/s11306-021-01776-5
LC-MS untargeted metabolomics assesses the delayed response of glufosinate treatment of transgenic glufosinate resistant (GR) buffalo grasses (Stenotaphrum secundatum L.)
Abstract
Introduction: Glufosinate resistant (GR) buffalo grasses were genetically modified to resist the broad-spectrum herbicide, glufosinate by inserting a novel pat gene into its genome. This modification results in a production of additional phosphinothricin acetyltransferase (PAT) to detoxify the deleterious effects of glufosinate. The GR grasses and its associated herbicide form a modern, weeding program, to eradicate obnoxious weeds in turf lawn without damaging the grasses at relatively low costs and labor. As with several principal crops which are genetically modified to improve agricultural traits, biosafety of the GR buffalo grasses is inevitably expected to become a public concern. For the first time, we had previously examined the metabolome of glufosinate-resistant buffalo grasses, using a GC-MS untargeted approach to assess the risk of GR as well as identify any pleotropic effects arising from the genetically modification process. In this paper, an untargeted high-resolution LC-MS (LC-HRMS) untargeted metabolomics approach was carried out to complement our previous findings with respect to GR and wild type (WT) buffalo grasses.
Objective: One of the major aims of this present work was to compare GR to WT buffalo grasses by including the detection of the secondary metabolome and determine any unprecedented metabolic changes.
Methods: Eight-week old plants of 4 GR buffalo grasses, (93-1A, 93-2B, 93-3 C and 93-5A) and 3 wild type varieties (WT 8-4A, WT 9-1B and WT 9-1B) were submerged in either 5 % v/v of glufosinate or distilled water 3 days prior to a LC-HRMS based untargeted metabolomics analysis (glufosinate-treated or control, samples, respectively). An Ultra-High-Performance Liquid Chromatography (UHPLC) system coupled to a Velos Pro Orbitrap mass spectrometer system was employed to holistically measure the primary and secondary metabolome of both GR and WT buffalo grasses either treated with or without glufosinate and subsequently apply several bioinformatic tools including the automated pathway analysis algorithm, mummichog.
Results: LC-HRMS untargeted based metabolomics clearly identified that the global metabolite pools of both GR and WT cultivars were highly similar, providing strong, supporting evidence of substantial equivalence between the GR and WT varieties. These findings indicate that if any associated risks to these GR grasses were somehow present, the risk would be within those acceptable ranges present in the WT. Additionally, mummichog-based pathway analysis indicated that phenylalanine metabolism and the TCA cycle were significantly impacted by glufosinate treatment in the WT cultivar. It was possible that alterations in the relative concentrations of several intermediates in these pathways were likely due to glufosinate-induced production of secondary metabolites to enhance plant defense mechanisms against herbicidal stress at the expense of primary metabolism.
Conclusions: GR buffalo grasses were found to be near identical to its WT comparator based on this complementary LC-HRMS based untargeted metabolomics. Therefore, these results further support the safe use of these GR buffalo grasses with substantial evidence. Interestingly, despite protected by PAT, GR buffalo grasses still demonstrated the response to glufosinate treatment by up-regulating some secondary metabolite-related pathways.
Keywords: Buffalo grasses; Glufosinate resistance; High resolution liquid chromatography–mass spectrometry; Untargeted metabolomics.
Similar articles
-
[A novel method for efficient screening and annotation of important pathway-associated metabolites based on the modified metabolome and probe molecules].Se Pu. 2022 Sep;40(9):788-796. doi: 10.3724/SP.J.1123.2022.03025. Se Pu. 2022. PMID: 36156625 Free PMC article. Chinese.
-
Utilization of GC-MS untargeted metabolomics to assess the delayed response of glufosinate treatment of transgenic herbicide resistant (HR) buffalo grasses (Stenotaphrum secundatum L.).Metabolomics. 2020 Jan 27;16(2):22. doi: 10.1007/s11306-020-1644-9. Metabolomics. 2020. PMID: 31989303
-
Resistance to glufosinate is proportional to phosphinothricin acetyltransferase expression and activity in LibertyLink(®) and WideStrike(®) cotton.Planta. 2016 Apr;243(4):925-33. doi: 10.1007/s00425-015-2457-3. Epub 2016 Jan 5. Planta. 2016. PMID: 26733464 Free PMC article.
-
Recent developments in metabolomics-based research in understanding transgenic grass metabolism.Metabolomics. 2019 Mar 15;15(4):47. doi: 10.1007/s11306-019-1507-4. Metabolomics. 2019. PMID: 30877485 Review.
-
Glufosinate-ammonium: a review of the current state of knowledge.Pest Manag Sci. 2020 Dec;76(12):3911-3925. doi: 10.1002/ps.5965. Epub 2020 Jul 28. Pest Manag Sci. 2020. PMID: 32578317 Review.
Cited by
-
Halophyte-based crop managements induce biochemical, metabolomic and proteomic changes in tomato plants under saline conditions.Physiol Plant. 2025 Jan-Feb;177(1):e70060. doi: 10.1111/ppl.70060. Physiol Plant. 2025. PMID: 39822104 Free PMC article.
-
Metabolomics in archaeological science: A review of their advances and present requirements.Sci Adv. 2023 Aug 11;9(32):eadh0485. doi: 10.1126/sciadv.adh0485. Epub 2023 Aug 11. Sci Adv. 2023. PMID: 37566664 Free PMC article. Review.
-
[A novel method for efficient screening and annotation of important pathway-associated metabolites based on the modified metabolome and probe molecules].Se Pu. 2022 Sep;40(9):788-796. doi: 10.3724/SP.J.1123.2022.03025. Se Pu. 2022. PMID: 36156625 Free PMC article. Chinese.
References
-
- Asensio-Ramos, M., Fanali, C., D’Orazio, G., & Fanali, S. (2017). Chapter 27 - Nano-liquid chromatography. In S. Fanali, P. R. Haddad, C. F. Poole, & M.-L. Riekkola (Eds.), Liquid chromatography (2nd ed., pp. 637–695). Amsterdam: Elsevier. - DOI
-
- Babele, P. K., & Young, J. D. (2019). Applications of stable isotope-based metabolomics and fluxomics toward synthetic biology of cyanobacteria. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. https://doi.org/10.1002/wsbm.1472 . - DOI - PubMed
-
- Barros, E., Lezar, S., Anttonen, M. J., van Dijk, J. P., Rohlig, R. M., Kok, E. J., & Engel, K. H. (2010). Comparison of two GM maize varieties with a near-isogenic non-GM variety using transcriptomics, proteomics and metabolomics. Plant Biotechnology Journal, 8(4), 436–451. https://doi.org/10.1111/j.1467-7652.2009.00487.x . - DOI - PubMed
-
- Bennett, R. N., & Wallsgrove, R. M. (1994). Secondary metabolites in plant defence mechanisms. New Phytologist, 127(4), 617–633. https://doi.org/10.1111/j.1469-8137.1994.tb02968.x . - DOI
-
- Berg, J. M., Stryer, L., & Tymoczko, J. L. (2012). Biochemistry (7th ed.). New York: W.H. Freeman.
MeSH terms
Substances
LinkOut - more resources
Other Literature Sources
Research Materials
Miscellaneous