Insects\' host preferences are regulated by multiple factors whose interactions are only partly understood. Here we make use of an in-depth, untargeted metabolomic approach combining molecular networking (MN) and supervised Analysis of variance Multiblock Orthogonal Partial Least Squares (AMOPLS) to untangle egg-laying preferences of Drosophila suzukii, an invasive, highly polyphagous and destructive fruit pest originating from Southeast Asia. Based on behavioural experiments in the laboratory as well as field observation, we selected eight genetically related Vitis vinifera cultivars (e.g., Ancellotta, Galotta, Gamaret, Gamay, Gamay précoce, Garanoir, Mara and Reichensteiner) exhibiting significant differences in their susceptibility toward D. suzukii. The two most and the two least attractive red cultivars were chosen for further metabolomic analyses of their grape skins. The combination of MN and statistical AMOPLS findings with semi-quantitative detection information enabled us to identify flavonoids as interesting markers for differences in the attractiveness of the four studied grape cultivars towards D. suzukii. Overall, dihydroflavonols were accumulated in unattractive grape cultivars, while attractive grape cultivars were richer in flavonols. Crucially, both dihydroflavonols and flavonols were abundant metabolites in the semi-quantitative analysis of the extracted molecules from the grape skin. We discuss how these two flavonoid classes might influence the egg-laying behaviour of D. suzukii females and how they could serve as potential markers for D. suzukii infestations in grapes that can be potentially extended to other fruits. We believe that our novel, integrated analytical approach could also be applied to the study of other biological relationships characterised by multiple evolving parameters.

The leaf apoplast contains several compounds that play important roles in the regulation of different physiological processes in plants. However, this compartment has been neglected in several experimental and modelling studies, which is mostly associated to the difficulty to collect apoplast washing fluid (AWF) in sufficient amount for metabolomics analysis and as free as possible from symplastic contamination. Here, we established an approach based in an infiltration-centrifugation technique that use little leaf material but allows sufficient AWF collection for gas chromatography mass spectrometry (GC-MS)-based metabolomics analysis in both tobacco and Arabidopsis. Up to 54 metabolites were annotated in leaf and apoplast samples from both species using either 20% (v/v) methanol (20% MeOH) or distilled deionized water (ddH2O) as infiltration fluids. The use of 20% MeOH increased the yield of the AWF collected but also the level of symplastic contamination, especially in Arabidopsis. We propose a correction factor and recommend the use of multiple markers such as MDH activity, protein content and conductivity measurements to verify the level of symplastic contamination in MeOH-based protocols. Neither the concentration of sugars nor the level of primary metabolites differed between apoplast samples extracted with ddH2O or 20% MeOH. This indicates that ddH2O can be preferentially used, given that it is a non-toxic and highly accessible infiltration fluid. The infiltration-centrifugation-based approach established here uses little leaf material and ddH2O as infiltration fluid, being suitable for GC-MS-based metabolomics analysis in tobacco and Arabidopsis, with great possibility to be extended for other plant species and tissues.

Metabolic perturbation has been associated with depression. An untargeted metabolomics approach using liquid chromatography-high resolution mass spectrometry was employed to detect and measure the rat serum metabolic changes following chronic social isolation (CSIS), an animal model of depression, and effective antidepressant fluoxetine (Flx) treatment. Univariate and multivariate statistics were used for metabolic data analysis and differentially expressed metabolites (DEMs) determination. Potential markers and predictive metabolites of CSIS-induced depressive-like behavior and Flx efficacy in CSIS were evaluated by the receiver operating characteristic (ROC) curve, and machine learning (ML) algorithms, such as support vector machine with linear kernel (SVM-LK) and random forest (RF). Upregulated choline following CSIS may represent a potential marker of depressive-like behavior. Succinate, stachydrine, guanidinoacetate, kynurenic acid, and 7-methylguanine were revealed as potential markers of effective Flx treatment in CSIS rats. RF yielded better accuracy than SVM-LK (98.50% vs. 85.70%, respectively) in predicting Flx efficacy in CSIS vs. CSIS, however, it performed almost identically in classifying CSIS vs. control (75.83% and 75%, respectively). Obtained DEMs combined with ROC curve and ML algorithms provide a research strategy for assessing potential markers or predictive metabolites for the designation or classification of stress-induced depressive phenotype and mode of drug action.

The fungus Phialomyces macrosporus was cultured using the One Strain Many Compounds (OSMAC) strategies to evaluate its metabolome. Variations in the nutrient culture media, culture regime, and cultivation parameters can significantly influence fungal extract quantity and chemical diversity. This study aimed to explore the mycobolome of P. macrosporus in five different culture media and two different cultivation conditions using NMR-based metabolomics. Principal component analysis (PCA) of 1H NMR spectra revealed clear differentiation between these samples, highlighting the rice dextrose agar medium (RDA) and potato dextrose broth (PDB) as standard complex media for conducting a fungal metabolite screening program.

BACKGROUND: Untargeted metabolomics is a powerful tool that provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites, especially when combining different metabolomic platforms. Vanilla is one of the world\'s most popular flavors originating from cured pods of the orchid Vanilla planifolia. However, only a few studies have investigated the metabolome of V. planifolia, and no LC-MS or GC-MS metabolomics studies with respect to leaves have been performed.
OBJECTIVE: The aim of the study was to comprehensively characterize the metabolome of different organs (leaves, internodes, and aerial roots) of V. planifolia.
METHODS: Characterization of the metabolome was achieved using two complementary platforms (GC × GC-MS, LC-QToF-MS), and metabolite identification was based on a comparison with in-house databases or curated external spectral libraries.
RESULTS: In total, 127 metabolites could be identified with high certainty (confidence level 1 or 2) including sugars, amino acids, fatty acids, organic acids, and amines/amides but also secondary metabolites such as vanillin-related metabolites, flavonoids, and terpenoids. Ninty-eight metabolites showed significantly different intensities between the plant organs. Most strikingly, aglycons of flavonoids and vanillin-related metabolites were elevated in aerial roots, whereas its O-glycoside forms tended to be higher in leaves and/or internodes. This suggests that the more bioactive aglycones may accumulate where preferably needed, e.g. for defense against pathogens.
CONCLUSIONS: The results derived from the study substantially expand the knowledge regarding the vanilla metabolome forming a valuable basis for more targeted investigations in future studies, e.g. towards an optimization of vanilla plant cultivation.

C. madagascariensis, an unexplored species of Burseraceae is used by local population for the management of inflammation and throat pain. The disease alleviation by this plant could be due to the presence of rich repository of active compounds with various pharmacological importances. In this study, therefore, the profiling of metabolites and isolation of active compounds of C. madagascariensis was performed. Furthermore, the ethanol, ethyl acetate extracts and a selected active compound was subjected for in vitro and in vivo anti-inflammatory activities. Metabolomic analysis identified and quantified 116 metabolites from leaves, young stem and gum-resins of C. madagascariensis (Burseraceae) followed by multivariate PCA analysis. NMR, GC-MS and HPLC were used to analyze primary and secondary metabolites. Subsequently, five main isolated compounds were identified as trimethoxy tetrahydrobenzo dioxolo isochromene (TTDI), butyl phenol, butyl propionate phenol, germacrone and β-elemenone. Amongst them, TTDI was found to be a novel compound. Hence, a process was developed to obtain the enriched fraction of TTDI in ethanol and ethyl acetate extracts of leaves. Furthermore, TTDI and extracts were subjected for their in vitro anti-inflammatory activity in LPS sensitized murine splenocytes. The results showed that TTDI and both extracts significantly suppressed the levels of pro-inflammatorycytokines (TNF-α, IFN-γ). Interestingly, the suppression of pro-inflammatory cytokines was evenmore significant by the similar concentration of TTDI when compared with colchicine. However, the level of anti-inflammatory cytokine (IL-10) was found to be unchanged. Additionally, in vivo anti-inflammatory study revealed a significant reduction in carrageenan induced paw edema by TTDI and both the extracts. In the docking study, TTDI was more active than colchicine with strong binding affinity to COX-2, PLA2, and 5β reductase. Our results highlighted that the presence of metabolites with medicinal and nutraceutical importance in C. madagascariensis, could provide opportunities for the development of a new plant-based therapeutics for inflammation.

This study analyzed the metabolite profiles and antioxidant capacities of two waxy and non-waxy Korean red rice accessions newly bred. Fifteen phenolic compounds were detected in the rice samples. Accession1 had high fatty acids, phytosterols, and vitamin E; accession3 had high vitamin E and phytosterol; and accession4 had a high total flavonoid. The correlation analysis findings from this study validated the positive association between all the metabolites and antioxidant activity. in silico results revealed that protocatechuic acid had a docking score of -9.541, followed by luteolin, quercetin, and caffeic acid, all of which had significant docking scores and a significant number of contacts. Similarly, molecular dynamics simulations showed that phytochemicals had root mean square deviation values of <2.8 Å with Keap 1, indicating better stability. This study provides valuable insights into potential directions for future investigations and improvements in the functional qualities of other colored rice varieties.

Cassava (Manihot esculenta Crantz) produces edible roots, a major carbohydrate source feeding more than 800 million people in Africa, Latin America, Oceania and Asia. Post-harvest physiological deterioration (PPD) renders harvested cassava roots unpalatable and unmarketable. Decades of research on PPD have elucidated several genetic, enzymatic and metabolic processes involved. Breeding populations were established to enable verification of robust biomarkers for PPD resistance. For comparison, these PPD populations have been cultivated concurrently with diversity population for carotenoid (β-carotene) content. Results highlighted a significant variation of the chemotypes due to environmental factors. Less than 3% of the detected molecular features showed consistent trends between the two harvest years and were putatively identified as phenylpropanoid derived compounds (e.g. caffeoyl rutinoside). The data corroborated that ∼20 μg β-carotene/g DW can reduced the PPD response of the cassava roots to a score of ∼1. Correlation analysis showed a significant correlation of β-carotene content at harvest to PPD response (R2 -0.55). However, the decrease of β-carotene over storage was not significantly correlated to initial content or PPD response. Volatile analysis observed changes of apocarotenoids derived from β-carotene, lipid oxidation products (alkanes, alcohols and carbonyls and esters) and terpenes. The majority of these volatiles (>90%) showed no significant correlation to β-carotene or PPD. Observed data indicated an increase (∼2-fold) of alkanes in varieties with β-carotene >10 μg/g DW and a decrease (∼60%) in varieties with less β-carotene. Fatty acid methyl esters with a chain length > C9 were detected solely after storage and show lower levels in varieties with higher β-carotene content. In combination with correlation values to PPD (R2 ∼0.3; P-value >0.05), the data indicated a more efficient ROS quenching mechanism in PPD resistant varieties.

Neural networks (NNs) are emerging as a rapid and scalable method for quantifying metabolites directly from nuclear magnetic resonance (NMR) spectra, but the nonlinear nature of NNs precludes understanding of how a model makes predictions. This study implements an explainable artificial intelligence algorithm called integrated gradients (IG) to elucidate which regions of input spectra are the most important for the quantification of specific analytes. The approach is first validated in simulated mixture spectra of eight aqueous metabolites and then investigated in experimentally acquired lipid spectra of a reference standard mixture and a murine hepatic extract. The IG method revealed that, like a human spectroscopist, NNs recognize and quantify analytes based on an analyte\'s respective resonance line-shapes, amplitudes, and frequencies. NNs can compensate for peak overlap and prioritize specific resonances most important for concentration determination. Further, we show how modifying a NN training dataset can affect how a model makes decisions, and we provide examples of how this approach can be used to de-bug issues with model performance. Overall, results show that the IG technique facilitates a visual and quantitative understanding of how model inputs relate to model outputs, potentially making NNs a more attractive option for targeted and automated NMR-based metabolomics.

The human gastrointestinal (GI) tract harbors diverse microbes, and the family Lachnospiraceae is one of the most abundant and widely occurring bacterial groups in the human GI tract. Beneficial and adverse effects of the Lachnospiraceae on host health were reported, but the diversities at species/strain levels as well as their metabolites of Lachnospiraceae have been, so far, not well documented. In the present study, we report on the collection of 77 human-originated Lachnospiraceae species (please refer hLchsp, https://hgmb.nmdc.cn/subject/lachnospiraceae) and the in vitro metabolite profiles of 110 Lachnospiraceae strains (https://hgmb.nmdc.cn/subject/lachnospiraceae/metabolites). The Lachnospiraceae strains in hLchsp produced 242 metabolites of 17 categories. The larger categories were alcohols (89), ketones (35), pyrazines (29), short (C2-C5), and long (C > 5) chain acids (31), phenols (14), aldehydes (14), and other 30 compounds. Among them, 22 metabolites were aromatic compounds. The well-known beneficial gut microbial metabolite, butyric acid, was generally produced by many Lachnospiraceae strains, and Agathobacter rectalis strain Lach-101 and Coprococcus comes strain NSJ-173 were the top 2 butyric acid producers, as 331.5 and 310.9 mg/L of butyric acids were produced in vitro, respectively. Further analysis of the publicly available cohort-based volatile-metabolomic data sets of human feces revealed that over 30% of the prevailing volatile metabolites were covered by Lachnospiraceae metabolites identified in this study. This study provides Lachnospiraceae strain resources together with their metabolic profiles for future studies on host-microbe interactions and developments of novel probiotics or biotherapies.