Using metabolic fingerprinting of plants for evaluating nitrogen deposition impacts on the landscape level

Gidman, Eleanor A.; Goodacre, Royston; Broadhurst, David; Emmett, Bridget A.; Gwynn‐Jones, Dylan

doi:10.1111/j.1365-2486.2006.01190.x

Cited by 22 publications

(17 citation statements)

References 30 publications

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“…This process is called metabolic fingerprinting, which now in an ecological context we propose to name ecometabolic fingerprinting. This ''holistic'' method enables unbiased exploration and examination of sample molecular biochemistry and through suitable interpretation can be used to study plant responses to environmental changes (Gidman et al 2005(Gidman et al , 2006. When the aim is to obtain information about the whole metabolome (the total number of metabolites in one biological system) by identifying and quantifying as many metabolites as possible, we must conduct an analysis approach such as NMR or GC-MS that enables determining and quantifying the maximum number of metabolites.…”

Section: Ecometabolomicsmentioning

confidence: 99%

“…This was done both in an experimentally manipulated N deposition gradient under common garden conditions using Calluna vulgaris and in a natural gradient across the United Kingdom (Gidman et al 2005(Gidman et al , 2006 ( Table 2). In both studies FT-IR fingerprinting was able to correlate metabolome changes with N deposition levels.…”

Section: Hypoxiamentioning

confidence: 99%

“…A principal component analysis of metabolite fingerprints revealed different metabolic phenotypes for each population. At the landscape level, Gidman et al (2006) have shown that different metabolic fingerprints measured with rapid Fourier transform-infrared spectroscopy in tissue samples of Galium saxatile are correlated with a gradient of N deposition across the entire UK landscape. Ecometabolomics thus allows the investigation of complex ecological systems and provides a rapid and sensitive indicator of ecosystem health.…”

Section: From Individuals To Populations and Ecosystemsmentioning

confidence: 99%

See 2 more Smart Citations

Ecological metabolomics: overview of current developments and future challenges

2011

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Ecometabolomics, which aims to analyze the metabolome, the total number of metabolites and its shifts in response to environmental changes, is gaining importance in ecological studies because of the increasing use of new technical advances, such as modern HNMR spectrometers and GC-MS coupled to bioinformatic advances. We review here the state of the art and the perspectives of ecometabolomics. The studies available demonstrate ecometabolomic techniques have great sensitivity in detecting the phenotypic mechanisms and key molecules underlying organism responses to abiotic environmental changes to biotic interactions. But such studies are still scarce, and in most cases they are limited to the direct effects of a single abiotic factor or of biotic interactions between two trophic levels under controlled conditions. Several exciting challenges remain to be achieved through the use of ecometabolomics in field conditions, involving more than two trophic levels, or combining the effects of abiotic gradients with intra-and inter-specific relationships. The coupling of ecometabolomic studies with genomics, transcriptomics, ecosystem stoichiometry, community biology and biogeochemistry may provide a further step forward in many areas of ecological sciences, including stress responses, species lifestyle, life history variation, population structure, trophic interaction, nutrient cycling, ecological niche and global change.

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Section: Ecometabolomicsmentioning

confidence: 99%

Section: Hypoxiamentioning

confidence: 99%

Section: From Individuals To Populations and Ecosystemsmentioning

confidence: 99%

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Ecological metabolomics: overview of current developments and future challenges

2011

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“…Then, an MS approach was tested in the form of flow injection electrospray (FIE)-MS , where bioextracts are infused without liquid chromatography (LC). Finally, the vibrational spectroscopy Fourier transform infrared (FTIR) gives the option of analyzing whole-tissue preparations (Gidman et al, 2006), although with less molecular information. These technologies were compared for phenotyping in parallel analyses of replicate Arabidopsis plants grown simultaneously in a single environment.…”

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confidence: 99%

Enhancement of Plant Metabolite Fingerprinting by Machine Learning

et al. 2010

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Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by 1 H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, 1 H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true-and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted.

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“…Initially, metabolomics was mainly applied for the analysis of foodstuffs, plant species with pharmaceutical prospects, crop plants and genetic model species. By now, however, metabolomics has also become widely available to ecologists and has been applied to wild plant species to analyse effects at extremely different scales, from nitrogen deposition on the landscape scale (Gidman et al, 2006) to the identification of specific genes regulating the production of secondary metabolites (Hirai et al, 2007;Keurentjes, 2009). Metabolomics is exceptionally suited for plant ecology because the techniques used are universally applicable.…”

Section: Applying Metabolomics To Wild Plant Species: Yes We Can!mentioning

confidence: 99%

A Role for Metabolomics in Plant Ecology

Dam

Meijden

2011

Annual Plant Reviews Volume 43

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Plant metabolomic analyses have been mostly applied to analyse metabolomes of crops and model species. However, metabolomic analyses may also be extremely valuable for plant ecologists. Because of the untargeted nature of most metabolomic platforms, the analyses will provide a comprehensive overview of both the primary and secondary plant metabolome. This is especially valuable for chemical ecologists studying plant-herbivore interactions, as both primary and secondary metabolites together determine the performance and preference of herbivores feeding on a plant. A few examples from the recent scientific literature exemplify how metabolomics may yield novel knowledge on the effects of hybridization on plant chemistry, insect resistance and genotype × environment interactions that could not have been so easily obtained with targeted studies alone. Theories and hypotheses regarding optimal defence strategies in plants and invasive plant biology may also benefit from metabolomic analysis. Testable hypotheses built on our current knowledge of plant responses to herbivores and competition may be substantiated and expanded based on metabolomic analyses. Despite some of the limitations that metabolomics analyses may have, the implementation of broad-spectrum chemical analysis in plant ecology will definitively open up new horizons for plant ecologists and enhance our understanding of the importance of plant chemistry in plant-environment interactions for fundamental and applied research.

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Using metabolic fingerprinting of plants for evaluating nitrogen deposition impacts on the landscape level

Cited by 22 publications

References 30 publications

Ecological metabolomics: overview of current developments and future challenges

Ecological metabolomics: overview of current developments and future challenges

Enhancement of Plant Metabolite Fingerprinting by Machine Learning

A Role for Metabolomics in Plant Ecology

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