HS-SPME-GC×GC-qMS volatile metabolite profiling of Chrysolina herbacea frass and Mentha spp. leaves

Abstract

The definitive version is available at: La versione definitiva è disponibile alla URL: [http://link.springer.com/article/10.1007/s00216-011-5600-4#page AbstractHeadspace solid-phase microextraction (HS-SPME) comprehensive two-dimensional (2D) gas chromatography combined with quadrupole-mass spectrometry (GCxGC-qMS) with dedicated comparative data elaboration was applied to separate chemical patterns arising from the interaction between some Mentha species and the herbivore Chrysolina herbacea, also known as the mint bug.Upon feeding on different Mentha species (M. spicata L., M. x piperita L. and M. longifolia L.), C. herbacea produced frass (faeces) which were characterized by a typical volatile fraction. HS-SPME GCxGC-qMS analysis of the complex volatile fraction of both mint leaf and C. herbacea frass was submitted to advanced fingerprinting analysis of 2D chromatographic data. 1,8-Cineole, found in the leaves of all the Mentha species examined, was oxidized and C. herbacea frass yielded high rates of several hydroxy-1,8-cineoles, including 2α-hydroxy-, 3α-hydroxy-, 3β-hydroxy-and 9-hydroxy-1,8-cineole. Upon insect feeding, several unknown oxidized monoterpenes, a p-menthane diol and three unknown phenylpropanoids were also detected in the frass volatiles. In M. longifolia, the occurrence of the monoterpene piperitenone oxide was found to be toxic and associated with insect death. The results of this work show that high throughput techniques such as HS-SPME and GCxGC-qMS fingerprint analysis are ideal tools to analyze complex volatile matrices, and provide a sensitive method for the direct comparison and chemical visualization of plant and insect emitted volatile components.

<em>Origanum Vulgare</em> Terpenoids Induce Oxidative Stress and Reduce the Feeding Activity of <em>Spodoptera Littoralis</em>

“…litura larvae are able to detoxify terpenes, including γ-terpinene [53][54][55]. The absence of chemical derivatives of O. vulgare terpenes and the assimilation of more than 50% of ingested terpenes by S. littoralis, as evidenced by our insect's frass analyses, show that S. littoralis catabolizes the plant terpenes and shows a limited ability to biotransform these molecules, in contrast to the typical behavior of specialist herbivores [56]. However, the catabolism of O. vulgare terpenes can produce also non-volatile derivatives which might not have been detected by our GC-MS and GC-FID analyses.…”

Pharmacological and therapeutic effects of Mentha Longifolia L. and its main constituent, menthol

“…Piperitenoneoxide is the main integral that is attributed to the insecticidal activity of the plant (LC50, 9.95 mg/L). [35] It is similarly shown that M. longifolia essential oil has 100% repellence against Sitophilus zeamais (10, 15, 20 days old),[3637] and Tribolium castaneum (25 days old). [38] Two studies have reported the high efficacy of the ethanolic extract of M. longifolia against third- and fourth-instar larvae of house mosquito Culex pipiens (LC50-26.8 ppm),[39] and against Sitophilus oryzae (24.2% repellency).…”

Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography–mass spectrometry data

“…The utility of GC×GC for targeted analyses of biological samples as well as compound discovery in complex matrices, including untargeted metabolomics [3, 4], is well recognized and applicable to many areas of inquiry. For example, Hartman, et al report using GC×GC in targeted analyses to quantify 3,4-methylenedioxymethamphetamine and three of its metabolites in blood and serum [5], and in an untargeted metabolomics analysis, Cordero and colleagues applied GC×GC to the detection of previously unidentified volatile metabolites from the leaves of Mentha species [6]. Turning the large data sets that are generated by MDC methods into information (e.g., quantifiable peaks and compound identities) is becoming less time-intensive and more reliable through advancements in commercial software packages (e.g., ChromaTOF, GCImage, and ChromSquare), as well as new unsupervised data processing and statistical analysis methods that are being developed or retooled for MDC (recently reviewed in [7–12]).…”