Quantitative analysis of changes in amino acids levels for cucumber (Cucumis sativus) exposed to nano copper

Abstract

The increasing usage of nanopesticides in agriculture poses a concern to plant crops due to unknown implications of engineered nanomaterials (ENMs). Targeted metabolomics can provide both qualitative and quantitative information at a molecular level to investigate the response of plants to emerging environmental stressors, such as nanopesticides. Here we describe a detailed protocol for the extraction and analysis of plant metabolites, specifically 23 amino acids in plants, using hydrophilic interaction liquid chromatography coupled to triple quadrupole mass spectrometry (HILIC-LC-MS/MS). Sufficient separation of 23 amino acids was achieved, without the need for derivatization, on an HILIC column with an MS/MS detector in a single run of 12 min with high sensitivity, selectivity and robustness and low LOD (0.005-15 ng/mL) and LOQ (0.02-50 ng/mL). A simple and efficient method to effectively extract amino acids from plant tissues was developed with a high recovery rate (80-120%). The protocol was then applied to determine the levels of amino acids in cucumber plants exposed to various environmentally-relevant levels of nano copper (nCu at 0, 200, 400, and 800 mg/kg soil; harvested in 60 days). Dose-dependent changes in amino acid levels were found; 13 amino acids were upregulated due to nCu stress, particularly, tyrosine increased 6.1, 8.2, and 11.0 fold after exposure to 200, 400 and 800 mg/kg nCu, respectively. The change in amino acid levels suggests an active defense response of the cucumber plant to nCu stress. We demonstrate that the HILIC-LC-MS/MS method is an effective and efficient technique to analyze underivatized amino acids in plant samples.

Surface coating determines the response of soybean plants to cadmium sulfide quantum dots

“…The photosynthetic pigments including carotenoid and chlorophyll a and b were determined spectrophotometrically in the 80% acetone extracts of first true leaves (Ni et al, 2008. Total soluble protein and amino acid content were determined in the second true leaves from the control and treated plants (Bradford, 1976;Huang et al, 2018). Secondary metabolites including the total phenolic content in the leaves and total lignin content in the roots were also determined in the plants (Zheng and Wang, 2001;Moreira-Vilar et al, 2014).…”

Omics to address the opportunities and challenges of nanotechnology in agriculture

“…Advances in QqQ in single reaction monitoring (SRM) 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 (RP) and hydrophilic interaction liquid chromatography (HILIC) are used to cover broad range of metabolites with contrasting polarity (Huang et al, 2018(Huang et al, , 2019Jorge et al, 2016). 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.…”

“…Here, we have reviewed fifteen publications that have applied untargeted metabolomics to elucidate underlying mechanisms in plants exposed to various metal/metal oxide nanoparticles (nCu, nCu(OH) 2 , nCeO 2 , nFe 3 O 4 , nTiO 2 , nSiO 2 , nAg and nCdO,) by acute or chronic application via root or leaves (Table 1). A few studies have also employed targeted metabolomics on selected group of metabolites, based on the findings from untargeted analyses (Huang et al, 2018(Huang et al, , 2019Majumdar et al, 2019).…”

Shaping Durum Wheat for the Future: Gene Expression Analyses and Metabolites Profiling Support the Contribution of BCAT Genes to Drought Stress Response

“…Metabolomic is a complementary tool for selecting favorable genotypes on the basis of metabolomic variations. To date, a targeted metabolomic approach has been frequently used, focused on a restricted group of metabolites ( Huang et al, 2018 , 2019 ; Zhao et al, 2018 ). In our work, the starting point was the availability of the barley and common wheat genomes that have furnished new tools for mining useful genetic variation ( Mayer et al, 2011 ; Xu et al, 2018 ).…”