Plant and rhizosphere metabolomics thus provide a snapshot of the entire plant-associated metabolome, that can be directly correlated with real-time functional footprint of the cellular state. The need for measuring metabolite levels in plants in response to ENMs was realized early on by the nanotoxicology community. However, most studies evaluated the total content of classes of metabolites such as sugars, phenolics, flavonoids, chlorophylls, non-enzymatic antioxidants, lignin, etc. using less sensitive biochemical assays . In some studies, profiling of specific classes of compounds like carbohydrates, fatty acids, and amino acids was also performed . However, based on the evaluation of a few metabolic parameters, a comprehensive understanding of the underlying mechanisms of ENM transport and effects is rather limited and biased by author’s interpretations. To address these challenges and identify the crucial role of the affected metabolites in response to ENMs, it is important to increase the analytical coverage of the plant metabolome.Metabolomic analysis can be categorized as untargeted or targeted, the choice of which depends on the scope of the study . Key factors that separate untargeted from targeted metabolomics are: extent of sample preparation required; number of metabolites detected; level of quantification; and extent of data processing post acquisition. Untargeted metabolomics is a discovery-based approach that screens the entire pool of metabolites in an organism. In nanotoxicity studies, untargeted metabolomics can be used to discover unknown metabolites of significance and identify markers in order to generate hypothesis on biological pathways involved in response to ENM exposures . Metabolite identification is based on available literature or in house experimental database,grow strawberry in containers and the quantitation is either relative or semi-quantitative, aided with extensive data processing . However, untargeted metabolomics also suffers from practical challenges.
Plant metabolites span over a broad range of composition and physio-chemical properties, which makes it challenging to extract and identify all of them simultaneously with acceptable recovery using a single analytical procedure. In addition, low molecular weight plant metabolites differ by several orders of magnitude, ranging from femto- or picomolar to millimolar concentrations , which present sensitivity and accuracy challenges for the detection of less abundant metabolites . Targeted metabolomics, on the other hand, focus on selected classes of chemically characterized and biologically annotated metabolites. Targeted metabolomics is hypothesis-driven, in which a defined set of known metabolites are analyzed with significantly higher selectivity and sensitivity, focusing on a specific biochemical question. Calibration and isotopically-labelled internal standards are used for absolute quantitation of the metabolites under investigation. Metabolomic analysis demands careful attention to details at each analytical step including sampling, metabolite extraction, storage, instrumental analysis, data processing, and interpretation for factual representation and reproducibility.Hence, a robust experimental design, randomization, efficient reporting of experimental details as per the Metabolomics Standards Initiative guidelines, and submission of results in data repositories are highly advisable, to ensure reproducibility and consistency of the metadata obtained from metabolomic analysis . To compensate for the qualitative and quantitative variations among plant samples, biological replicates are essential, which results in more powerful statistical analysis. However, in case of high variability and limited sample availability, sample pooling is a common procedure to represent the population, which nonetheless should be reported and considered during data analysis . Although these requirements are quintessential for any metabolomic experiment, nanotoxicity studies should also consider ENM stability in the exposure media throughout the experiment duration, environmentally or agriculturally relevant dosing, ambient fluctuations, use of appropriate positive and negative controls, and comparison with ionic and bulk-particle controls to identify nano-specific effects.
Representative techniques and key challenges in each step of the metabolomic analysis in ENM-plant interaction research are summarized below .In a metabolomic study, a robust sampling protocol is critical in order to obtain maximum information from the plants exposed to ENMs. Some of the factors considered during sampling are: scope of the study, route of exposure , kinetic of ENM transport in the species under investigation, growth stage of the plant and time of the day when harvesting is done. Different plant organs such as root, leaf or fruit have distinct metabolic footprint owing to varying cellular organization. Thus, to prevent loss of information about chemical signaling across organs, it is necessary to analyze them separately. The metabolite profile of a tissue also varies significantly depending on the growth stage of the plant; hence, results from an ENM exposure study in a germinating seedling cannot be extended to compare or interpret metabolic events in the plant in its vegetative or matured stage, or vice versa. Tissues collected from untreated and ENM-treated plants should be immediately quenched in liquid nitrogen or freeze-dried to capture the metabolic state of the plant at the desired moment. Prior to freezing, tissues may be washed with water at room temperature, if necessary; cold or hot solvents must be avoided to prevent leakage of intracellular metabolites. Frozen tissues are ground to homogenous dry powder using precooled homogenizing apparatus, and appropriate extraction methods must be used depending on the analytical technique used. To prevent loss of unstable metabolites and to maximize analytical recovery through all the steps of extraction, adequate sample storage and handling is important. In untargeted analysis, the aim is to broaden the coverage of metabolites and maximize identification. The most common strategy is to extract a wide range of metabolites in solvent mixtures, such as methanol/ methyl tert-butyl ether/water or methanol/chloroform/water, followed by separation of the polar and non-polar metabolites by biphasic partitioning . Each phase is collected for analysis using suitable analytical platforms. In targeted analysis, specific extraction protocols are optimized to retain the metabolites of interest. Additional sample processing such as filtration or solid phase extraction are also used to reduce matrix effects and remove undesired metabolites.However, GC-MS is best suitable for volatile and thermally stable metabolites, and requires derivatization/ chemical modification of polar metabolites such as sugars, amino acids and organic acids .
In contrast, LC-MS allows analysis of polar and high molecular weight metabolites without any derivatization. For untargeted analysis, LC is hyphenated with high resolution MS like time-of-flight or Orbitrap mass analyzers with electrospray ionization . Nevertheless, LC-MS also suffers from drawbacks due to ion suppression, differential adduct formation and retention time shifts due to matrix effects. Different analytical approaches are however complementary, and can be used together to obtain a comprehensive coverage of metabolites with high confidence. Extraction of plant metabolites in solvent mixtures results in an immediate loss of inter- and sub-cellular resolution. Advances in MS imaging techniques have made it possible to map metabolites in intact plant tissues at sub-cellular level and connect the spatial complexity of the plant molecular organization to phenotypical features. Some techniques that have been hyphenated with MSI for imaging metabolites in plant specimens are matrix-assisted laser desorption/ionization and laser ablation electrospray ionization . However, these approaches have not yet been utilized in ENM plant interaction studies.In targeted metabolomics, low resolution MS such as linear ion traps or triple quadrupole are predominantly used . Advances in QqQ in single reaction monitoring mode allows robust absolute quantification of metabolites with high sensitivity and selectivity, even at trace levels with relatively high throughput. Column chemistry and its retention mechanism are extremely critical for targeted analysis of metabolites using LC-MS; and hence, reverse phase and hydrophilic interaction liquid chromatography are used to cover broad range of metabolites with contrasting polarity . Targeted analysis is particularly useful as a follow up from untargeted analysis, in order to focus on specific metabolic pathways or testing application of ENMs in pathogen defense, nutrient acquisition, photosynthetic efficiency or productivity.Data processing in metabolomics comprises baseline correction, feature extraction, spectral alignment across samples, identification and interpretation . Post data-acquisition, baseline correction is performed on the datasets to remove low frequency artifacts and aberrations across multiple samples that could be generated by experimental or instrumental variation. As chromatographic retention time may vary between samples, spectral alignment is performed across the dataset, which may precede or follow feature extraction process. In MS-based studies, peak-based algorithms are used to extract and quantify all relevant chromatographic and spectral information for all known and unknown metabolites in each sample. Upon detection, related ions indicative of a single-component chromatographic peak are identified and grouped . Several open source and commercial MS reference and MS/MS library matching databases are available; but the databases on plant metabolites is limited and they are not fully optimized for all analytical platforms. Electron ionization fragmentation patterns and indexed retention times for an extensive array of compounds using GC-MS and NMR spectral library are available; however, LC-MS data lack standardization in the available resources . However, due to an increasing number of studies in plant metabolism, high quality spectral and chromatographic data are continuously being added to and curated within these spectral libraries, which will eventually improve the routine peak identification in non-model plants. Software such as XCMS, PRIMe, MeltDB etc. are used for data processing including feature detection, retention time correction, alignment, annotation, statistical analysis, and data visualization. Another online tool, Metabo Analyst,hydroponic nft system is used to process identified metabolites for data normalization, statistic alanalysis, and visualization . Statistical analysis of the metabolomics data helps to identify features that are differentially regulated compared to control samples. The identified markers are then projected on to the available metabolic networks, which can be used in a phenotypic context to generate mechanistic hypothesis.Kocide-3000 is a commercial micron-sized pesticide, composed of copper hydroxide 2) nanosheets, which can be applied using aerial or ground spray in the field. Zhao et al. investigated the metabolomes of lettuce, cucumber, maize and spinach plants chronically sprayed with Kocide .
Twenty four-days-old lettuce plants were foliar sprayed with 1050, 1550, 2100 mg/l Kocide twice a week for four weeks . Although the chronic exposure did not induce overt response, metabolomic analysis of the leaves revealed differential accumulation of 50 metabolites ultimately affecting six biological pathways, including Gly/Se/Thr metabolism, Ala/Asp/Glu metabolism, TCA cycle, pantothenate and CoA biosynthesis, glycolysis or gluconeogenesis, and pyruvate metabolism. Chronic exposure to Kocide deteriorated the antioxidant defense mechanism in the lettuce plants expressed by decreasing levels of phenolic compounds like cis-caffeic acid, chlorogenic acid, 3,4- hydroxycinnamic acid. Dehydroascorbic acid, its oxidation products and GABA levels were also diminished at high exposure concentrations, suggesting impairment of the antioxidant system in lettuce. A chronic exposure of spinach leaves for 7 days to 1000 mg/l Kocide also resulted in depreciated levels of antioxidant or defense associated metabolites, including ascorbic acid, a-tocopherol, GABA, threonic acid, b-sitosterol, 4-hydroxybutyric acid, and ferulic acid, resulting in the perturbation of phenylpropanoid and ascorbate/aldarate pathways . However, the decreased levels of antioxidants and activation of antioxidant defense system in spinach leaves were driven by Cu ion release. In addition to amino acids , accumulation of phytol, a chlorophyll degradation product, was also decreased in Kocide-exposed spinach leaves, similar to Kocide-exposed maize and cucumber leaves . Metabolomic response in artificial soil-grown three-weeks old cucumber and maize plants exposed to 100 and 1000 mg/lKocide by foliar spray for a week was compared; however it is to be noted that the final dose in maize plants were higher than the cucumber plants due to hydrophobicity of maize leaves . In maize leaves, Kocide treatment induced accumulation of glycolysis and TCA intermediates thereby enhancing energy metabolism and enhance branched chain amino acids accumulation, which play important role in oxidative phosphorylation energy source. Kocide resulted in increased levels of polyphenols such as 4-hydroxycinnamic acid, 1,2,4-benezenetiol, and their amino acid precursors , thereby activating shikimate phenylpropanoid pathway in maize leaves suggesting induction of antioxidative processes. In cucumber plants, Kocide significantly affected the Arg/Pro pathway intermediates . In both plants, Kocide exposure resulted in reduced levels of unsaturated fatty acids and increased saturated fatty acid indicating an alteration of plasma membrane homeostasis.Metabolomic studies suggest that the toxicity effects in plants exposed to nAg is primarily attributed to the released Agþ ions . In 7-day old Arabidopsis thaliana plants, a 24 hexposure to 1 mg/L of citrate-nAg, polyvinylpyrrolidone -nAg and AgNO3 via roots resulted in alteration in six, three, and nine metabolites respectively, which were identified as fatty acid derivates.