Migration time correction for dual pressure capillary electrophoresis in semi-targeted metabolomics study.

Migration time fluctuation strongly affects peak alignment and identification of unknown compounds, making migration time correction an essential step in capillary electrophoresis (CE)-based metabolomics. To obtain more reliable information, metabolites with different apparent mobilities are analyzed by tandem mass spectrometry. Applying a small pressure is a common practice for reducing the analysis time of anions in a positive mode CE, known as the pressure-assisted CE. However, applying pressure may reduce the separation efficiency and can be undesirable for cation analysis. A simple way to address this issue is to increase the pressure after a certain time, during the separation. We term this practice as dual pressure CE. However, changing the pressure during the CE separation complicates migration time correction. Previous migration time correction methods were established based on a consistent electroosmotic flow and a constant pressure-driven bulk-flow velocity. We proposed a new correction method to support the peak alignment when dual pressure CE is used. A Python-based script was developed to implement dual pressure CE migration time correction for semi-targeted metabolomics study performed by a multiple reaction monitoring-based method. This script can help select suitable endogenous metabolites as correction markers, perform migration time correction, and conduct peak alignment. A case study showed that migration time precision of 156 metabolites in 32 samples can be improved from 4.8 to 11.4%RSD (relative standard deviation) to less than 1.8%RSD.

Semi-Targeted Metabolomics to Validate Biomarkers of Grape Downy Mildew Infection Under Field Conditions.

Grape downy mildew is a devastating disease worldwide and new molecular phenotyping tools are required to detect metabolic changes associated to plant disease symptoms. In this purpose, we used UPLC-DAD-MS-based semi-targeted metabolomics to screen downy mildew symptomatic leaves that expressed oil spots (6 dpi, days post-infection) and necrotic lesions (15 dpi) under natural infections in the field. Leaf extract analyses enabled the identification of 47 metabolites belonging to the primary metabolism including 6 amino acids and 1 organic acid, as well as an important diversity of specialized metabolites including 9 flavonols, 11 flavan-3-ols, 3 phenolic acids, and stilbenoids with various degree of polymerization (DP) including 4 stilbenoids DP1, 8 stilbenoids DP2, and 4 stilbenoids DP3. Principal component analysis (PCA) was applied as unsupervised multivariate statistical analysis method to reveal metabolic variables that were affected by the infection status. Univariate and multivariate statistics revealed 33 and 27 metabolites as relevant infection biomarkers at 6 and 15 dpi, respectively. Correlation-based networks highlighted a general decrease of flavonoid-related metabolites, whereas stilbenoid DP1 and DP2 concentrations increased upon downy mildew infection. Stilbenoids DP3 were identified only in necrotic lesions representing late biomarkers of downy mildew infection.

A phenylpropanoid diglyceride associates with the leaf rust resistance Lr34res gene in wheat.

The gene Lr34res is one of the most long-lasting sources of quantitative fungal resistance in wheat. It is shown to be effective against leaf, stem, and stripe rusts, as well as powdery mildew and spot blotch. Recent biochemical characterizations of the encoded ABC transporter have outlined a number of allocrites, including phospholipids and abscisic acid, consistent with the established general promiscuity of ABC transporters, but ultimately leaving its mechanism of rust resistance unclear. Working with flag leaves of Triticum aestivum L. variety 'Thatcher' (Tc) and a near-isogenic line of 'Thatcher' into which the Lr34res allele was introgressed (Tc+Lr34res; RL6058), a comparative semi-targeted metabolomics analysis of flavonoid-rich extracts revealed virtually identical profiles with the exception of one metabolite accumulating in Tc+Lr34res, which was not present at comparable levels in Tc. Structural characterization of the purified metabolite revealed a phenylpropanoid diglyceride structure, 1-O-p-coumaroyl-3-O-feruloylglycerol (CFG). Additional profiling of CFG across a collection of near-isogenic lines and representative Lr34 haplotypes highlighted a broad association between the presence of Lr34res and elevated accumulations of CFG. Depletion of CFG upon infection, juxtaposed to its relatively lower anti-fungal activity, suggests CFG may serve as a storage form of the more potent anti-microbial hydroxycinnamic acids that are accessed during defense responses. Altogether these findings suggest a role for the encoded LR34res ABC transporter in modifying the accumulation of CFG, leading to increased accumulation of anti-fungal metabolites, essentially priming the wheat plant for defense.

Integrated semi-targeted metabolomics analysis reveals distinct metabolic dysregulation in pleural effusion caused by tuberculosis and malignancy.

BACKGROUND: Tuberculous pleural effusion (TPE) and malignant pleural effusion (MPE) are the 2 most frequent causes of exudative pleural effusions (PEs). However, the clinical differentiation is challenging. METHODS: Metabolic signatures in pleural effusion from 156 patients were profiled. An integrated semi-targeted metabolomics platform was incorporated for high throughput metabolite identification and quantitation. In this platform, orbitrap based mass spectrometry with data dependent MS/MS acquisition was applied in the analysis. In-house database containing ~1000MS/MS spectra were established and "MetaInt" was developed for metabolite alignment. RESULTS: Using this strategy, lower levels of amino acids, citric acid cycle intermediates and free fatty acids accompanied with elevated acyl-carnitines and bile acids were observed, demonstrating increased energy expenditure caused by TPE. Kynurenine pathway from tryptophan was significantly enhanced in TPE. The ratio of tryptophan/kynurenine exhibited decent performance in differentiating TPE from MPE with sensitivity of 92.7% and specificity of 86.1%. After two further independent validations, it turns out that the ratio of tryptophan/kynurenine can be applied confidently as a potential biomarker together with adenosine deaminase (ADA) for clinical diagnosis of TPE. CONCLUSIONS: Conclusively, the integrated in-house platform for high throughput semi-targeted metabolomics analysis reliably identified great potential of tryptophan/kynurenine ratio as a novel diagnostic biomarker to distinguish pleural effusion caused by tuberculosis and malignancy.