Tree metabolite identification combats beetle devastation

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  • Published: Feb 1, 2018
  • Author: Ryan De Vooght-Johnson
  • Channels: Laboratory Informatics / Chemometrics & Informatics
thumbnail image: Tree metabolite identification combats beetle devastation

Emerald ash borer threatens ash trees

Across the USA and Canada, ash trees are being wiped out by a small green beetle, the emerald ash borer (Agrilus planipennis). This species is native to Asia, where it does comparatively little harm, but most American species of ash are very susceptible to attack, with nearly all affected trees eventually dying. The beetle larvae do the damage, boring into the bark and killing the tree. The beetle has not currently reached the UK but has become established in the Moscow region of Russia, causing the loss of many ash trees.

Rapid diagnosis of emerald ash borer infestation is important but beetle attack is not obvious in its initial stages. The Trent University researchers devised a method to detect emerald ash borers by examining the metabolites present in the leaf. Sampling leaves is a rapid method of diagnosing a large number of trees, without the lengthy task of searching for larvae in the bark.

LC-MS and chemometrics used to examine metabolites after beetle attack

Samples of ash leaves were taken from trees under attack by ash borer and also from trees in areas that the beetle had not yet reached. Two groups of samples were taken from various sites in Ontario: sample set 1 consisted of 26 infested and 26 unaffected trees (collected in 2012), while sample set 2 consisted of 14 infested and 14 unaffected trees (collected in 2013 to 2014). The leaves were cut into pieces and extracted with methanol under sonication, prior to HPLC.

HPLC employed an Agilent 1100 pump and autosampler, along with a Grace Genesis C18 column protected by a Restek Pinnacle II C18 guard cartridge. The two mobile phases were methanol and 20 mM aqueous ammonium acetate; the proportion of the former was increased from 10 to 80% over 10 mins, from 80 to 100% over 10 mins and then kept at 100% for 10 mins. The solvent flow rate was 200 μL per minute. Mass spectrometry employed a Sciex 5500 Qtrap instrument, using a Turbo Ion electrospray source. The samples were analysed in full scan mode, collecting m/z from 60 to 650.

Visually, the chromatograms from infested and unaffected leaves did not look too different so chemometric methods were employed to distinguish between them. Principal component analysis (PCA) using Sciex’s MarkerView software allowed infested and healthy trees to be distinguished: two-component plots for set 1 and also for the combined sets both showed all the healthy trees clustered together, while nearly all the infested trees were outside this area, but spread fairly widely over the plot.

Partial least squares discriminant analysis (PLS-DA) was also carried out on the data using XLSTAT version 19.4 (Addinsoft). This gave a model that correctly identified at least 90% of the samples in the groups used for validation (validation was carried out firstly with 28 samples with an equal number from affected and unaffected trees, then with a group of 28 random samples and lastly with a group of 40 random samples). Following the chemometric results, 13 metabolites were shown to be appropriate biomarkers for the presence or absence of beetle attack.

LC-MS allows beetles to be detected from leaf samples

The new method will speed up identification of this harmful pest since it only requires leaf samples, rather than a careful examination of the tree bark. Confirmatory detection of the beetle can then be carried out when positive results are obtained. Further refinement of the method may increase its accuracy, which is already reasonably high. Despite such analytical advances, the fight against this destructive insect remains an uphill battle.

Related Links

Rapid Communications in Mass Spectrometry, Early View paper. Stock et al. Liquid chromatography-mass spectrometry for the detection of ash tree metabolites following emerald ash borer infestation.

Wikipedia, Partial Least Squares Regression

Wikipedia, Principal Component Analysis

Article by Ryan De Vooght-Johnson

The views represented in this article are solely those of the author and do not necessarily represent those of John Wiley and Sons, Ltd.

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