Non-targeted discovery of class-distinguishing metabolites in Argentinian pacu fish by comprehensive two-dimensional gas chromatography with principal component analysis

Simulating comprehensive two-dimensional gas chromatography mass spectrometry data with realistic run-to-run shifting to evaluate the robustness of tile-based Fisher ratio analysis

Untargeted analysis of comprehensive two-dimensional (2D) gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) data has the potential to be hindered by run-to-run retention time shifting. To address this challenge, tile-based Fisher ratio (F-ratio) analysis (FRA) has been developed, which utilizes a supervised, untargeted approach involving a chromatographic segmentation routine termed "tiling" combined with the ANOVA F-ratio statistic to discover class-distinguishing analytes while minimizing false positives arising from shifting. The tiling algorithm is designed to account for retention shifting in both separation dimensions. Although applications of FRA have been reported, there remains a need to thoroughly evaluate the robustness of FRA for different levels of run-to-run retention shifting in order to broaden the scope of its application. To this end, a novel method of simulating GC×GC-TOFMS chromatograms with realistic run-to-run shifting is presented by random generation of low-frequency "shift functions". The dimensionless retention-time precision, <δ_r>, which is four times the standard deviation in retention time normalized to the peak width-at-base is used as a key modeling variable along with the 2D chromatographic saturation, α_e,2D, and within-class relative standard deviation in peak area, RSD_wc. We demonstrate that all three of these variables operate together to impact true positive discovery. To quantify the "success" of true positive discovery, GC×GC-TOFMS datasets for various combinations of <δ_r>, α_e,2D, and RSD_wc were simulated and then analyzed by FRA using a wide range of relative tile areas (RTA), which is a dimensionless measure of tile size. Since each hit in the FRA hit list was known a priori as either a true or false positive based on the simulation inputs, receiver operating characteristic (ROC) curves were readily constructed. Then, the area under the ROC curve (AUROC) was used as a metric for discovery "success" for various combinations of the modeling variables. Based on the results of this study, recommendations for tile size selection and experimental design are provided, and further supported by comparison to previous tile-based FRA applications. For instance, values for <δ_r>, α_e,2D, and RSD_wc obtained from a GC×GC-TOFMS dataset of yeast metabolites suggested an optimum RTA of 6.25, corresponding closely to the RTA of 4.00 employed in the study, implying the simulation results obtained here can be generalized to real datasets.

Exogenous Proline Optimizes Osmotic Adjustment Substances and Active Oxygen Metabolism of Maize Embryo under Low-Temperature Stress and Metabolomic Analysis

Maize (Zea mays L.) is more sensitive to low-temperature stress in the early growth period. The study was to explore the response mechanism of proline to low-temperature stress during maize seed germination. Maize varieties Xinxin 2 (low-temperature insensitive) and Damin 3307 (low-temperature sensitive) were chosen as the test materials, setting the normal temperature for germination (22 °C/10 °C, 9d), low-temperature germination (4 °C/4 °C, 5d) and normal temperature recovery (22 °C/10 °C, 4d), combined with proline (15 mmol·L−1) soaking treatment, to study its effects on the osmotic regulation system and antioxidant protection system of maize embryos. Metabolomics analysis was carried out to initially reveal the basis of the metabolic regulation mechanism. The results showed that the activities of superoxide dismutase (SOD), peroxidase (POD), ascorbic acid peroxidase (APX) and glutathione reductase (GR) were induced to some extent under low-temperature stress. The activities of SOD, POD, APX and GR were further enhanced in the soaking seeds with proline. Proline treatment improved the activities of catalase (CAT), monodehydrated ascorbic acid reductase (MDHAR) and dehydroascorbic acid (DHAR), increased the contents of ascorbic acid (AsA) and glutathione (GSH) and decreased the contents of oxidized ascorbic acid (DHA) and reduced glutathione (GSSG) under low-temperature stress. The ratio of AsA/DHA and GSH/GSSG increased. The increase in antioxidant enzyme activity and the content of antioxidants can help to maintain the stability of the AsA-GSH cycle, and effectively reduce the production rate of superoxide anion (O2•−), hydrogen peroxide (H2O2) and malondialdehyde (MDA). Based on the UPLC-MS/MS detection platform and self-built database, 589 metabolites were detected in each treated maize embryo; 262 differential metabolites were obtained, including 32 organic acids, 28 amino acids, 20 nucleotides and their derivatives, 26 sugars and alcohols, 46 lipids, 51 alkaloids, 44 phenols and 15 other metabolites. Sixty-eight metabolic pathways involving different metabolites were obtained by KEGG enrichment analysis. The results showed that proline increased the accumulation of sorbitol, planteose, erythritose 4-phosphate, arabinose and other saccharides and alcohols in response to low-temperature stress, increased the content of osmoregulation substances under low-temperature stress. Proline also restored the TCA cycle by increasing the content of α-ketoglutarate and fumaric acid. Proline increased the contents of some amino acids (ornithine, proline, glycine, etc.), alkaloids (cocamidopropyl betaine, vanillylamine, 6-hydroxynicotinic acid, etc.), phenols (phenolic ayapin, chlorogenic acid, etc.) and vitamins (ascorbic acid, etc.) in the embryo under low-temperature stress. Combined with pathway enrichment analysis, proline could enhance the low-temperature stress resistance of germinated maize embryos by enhancing starch and sucrose metabolism, arginine and proline metabolism, biosynthesis of secondary metabolites, flavonoid biosynthesis and pentose phosphate pathway.

Advanced tuning of the ion management parameters in GC × GC-HRMS using a Fourier transform Orbitrap mass analyzer for pixel-based data handling and multivariate analysis

GC × GC investigations are well known to generate a substantial amount of information-rich and structurally complex data, requiring advanced data processing strategies like chemometrics. Many workflows are available for data handling and processing, such as the peak-table and pixel-based approaches. The goal of this work is to present a solution based on method development to solve the missing pixel problem that may be encountered in experiments performed with GC and GC × GC coupled to the Fourier transform orbital ion trap (FT-Orbitrap) mass analyzer. Data input is vital for pixel-based chemometric analyses, as some post-processing solutions may lead to significant loss of chemical information in the data set. Hence, a key requisite is that the chemical information is consistently indexed in the data arrays for proper pixel-based data handling and analysis. In this study, we carefully evaluated the ion management parameters to preserve the intrinsic structure and information of the data arrays of the GC × GC-FT-Orbitrap for future pixel-oriented chemometric analysis. The most acceptable conditions yielded acquisition rates up to 42.6 spectra per s, while a routine setting of 24.7 Hz was successfully employed in analyses of different petroleum fractions, producing both consistent tensor sizes and acceptable peak reconstructions. A data acquisition rate of 24.7 spectra per s and a mass resolving power of 15 000 allowed the resolution of a mass split of only 0.004 Da - which is an interesting configuration for challenging applications in petroleomics. Using such advanced settings, the missing pixel problem was reduced from up to 30% to much less than 0.04% of the data array dimension. Thus, the proposed configuration can be employed in studies that require pixel-oriented multivariate data analysis.