Evidence map›Paper›PMID 33609206›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2021

LC-MS untargeted metabolomics assesses the delayed response of glufosinate treatment of transgenic glufosinate resistant (GR) buffalo grasses (Stenotaphrum secundatum L.).

Siriwat Boonchaisri, Simone Rochfort, Trevor Stevenson, Daniel A Dias

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Article in Metabolomics : Official journal of the Metabolomic Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.8field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Siriwat BoonchaisriDivision of Biology, School of Sciences, University of Phayao, Phayao, 56000, Thailand.
Simone RochfortAgriculture Research Victoria, AgriBio, Bundoora, VIC, 3083, Australia.
Trevor StevensonSchool of Science, RMIT University, Bundoora, VIC, 3083, Australia.
Daniel A DiasSchool of Health and Biomedical Sciences, Discipline of Laboratory Medicine, RMIT University, PO Box 71, Bundoora, VIC, 3083, Australia. daniel.dias@rmit.edu.au.ORCID http://orcid.org/0000-0003-0129-3178
RMIT University · AULa Trobe University · AUUniversity of Phayao · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionGlufosinate 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.

objectiveOne 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.

methodsEight-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.

resultsLC-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.

conclusionsGR 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.

Indexed as

AgricultureAminobutyratesAnimalsBuffaloesChromatography, High Pressure LiquidChromatography, LiquidCrops, AgriculturalGas Chromatography-Mass SpectrometryHerbicide ResistanceHerbicidesMetabolomeMetabolomicsPlant WeedsPoaceaeTandem Mass SpectrometryAminobutyratesHerbicidesphosphinothricinBuffalo grassesGlufosinate resistanceHigh resolution liquid chromatography–mass spectrometryUntargeted metabolomics

Identifiers

PMID33609206
OpenAlexW3133214773

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.