COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access

Different extraction methods shape the phenolic signature and biological activity of Morinda lucida extracts: A novel source of bioactive compounds preparing functional applications

The extraction of bioactive compounds is based on the application of various extraction techniques. Therefore, the stem and root bark of the plant species Morinda lucida L. were used in this research, while the extraction procedure was performed using three extraction techniques: HAE (homogenizer extraction), UAE (ultrasound extraction) as modern, and MAC (maceration) as conventional extraction technique. The presence of different classes of secondary metabolites was determined using the UHPLC method, while the content of total phenols and flavonoids was determined spectrophotometrically. The biological potential was investigated by in vitro antioxidant and enzyme assays. Different extraction technologies showed significant differences in only two classes of phenols, namely lignans and phenolic acids, which were significantly higher in HAE than in UAE and MAC. These findings highlight the significant effect of stem and bark extracts of M. lucida, opening the way for innovative industrial exploitation of these matrices.

A multi-omics approach to unravel the interaction between heat and drought stress in the Arabidopsis thaliana holobiont

The impact of combined heat and drought stress was investigated in Arabidopsis thaliana and compared to individual stresses to reveal additive effects and interactions. A combination of plant metabolomics and root and rhizosphere bacterial metabarcoding were used to unravel effects at the plant holobiont level. Hierarchical cluster analysis of metabolomics signatures pointed out two main clusters, one including heat and combined heat and drought, and the second cluster that included the control and drought treatments. Overall, phenylpropanoids and nitrogen-containing compounds, hormones and amino acids showed the highest discriminant potential. A decrease in alpha-diversity of Bacteria was observed upon stress, with stress-dependent differences in bacterial microbiota composition. The shift in beta-diversity highlighted the pivotal enrichment of Proteobacteria, including Rhizobiales, Enterobacteriales and Azospirillales. The results corroborate the concept of stress interaction, where the combined heat and drought stress is not the mere combination of the single stresses. Intriguingly, multi-omics interpretations evidenced a good correlation between root metabolomics and root bacterial microbiota, indicating an orchestrated modulation of the whole holobiont.

Lipidomics of sheep and goat Milk-based infant formulae during in vitro dynamic digestion

Lipid hydrolysis process during IF digestion, particularly the characterization of the lipidome and the resulting lipid breakdown products, has not been thoroughly investigated. This study aimed to compare the lipid hydrolysis profiles during the in vitro dynamic digestion of IFs made from whole sheep and goat milk. Using a lipidomics platform and multivariate statistical analysis, we observed changes in complex lipid levels during digestion. In the gastric compartment, we noted a progressive hydrolysis of triacylglycerols, phosphatidylcholines, and sphingomyelins. Conversely, lipolysis breakdown products like monoacylglycerols (e.g., MG(16:0), MG(18:0)), diacylglycerols, lysophosphatidylcholines (LPC 16:0, LPC 18:1, LPC 18:2), and free fatty acids increased in the intestinal compartment. The lipolysis trends were similar for both types of infant formulas, with long-chain fatty acid triglycerides (C > 46) exhibiting lower digestibility compared to medium-chain fatty acid triglycerides. Overall, these results indicate that sheep milk can be used as an ingredient in the manufacturing of IF.

Potential of pre-diagnostic metabolomics for colorectal cancer risk assessment or early detection

This systematic review investigates the efficacy of metabolite biomarkers for risk assessment or early detection of colorectal cancer (CRC) and its precursors, focusing on pre-diagnostic biospecimens. Searches in PubMed, Web of Science, and SCOPUS through December 2023 identified relevant prospective studies. Relevant data were extracted, and the risk of bias was assessed with the QUADAS-2 tool. Among the 26 studies included, significant heterogeneity existed for case numbers, metabolite identification, and validation approaches. Thirteen studies evaluated individual metabolites, mainly lipids, while eleven studies derived metabolite panels, and two studies did both. Nine panels were internally validated, resulting in an area under the curve (AUC) ranging from 0.69 to 0.95 for CRC precursors and 0.72 to 1.0 for CRC. External validation was limited to one panel (AUC = 0.72). Metabolite panels and lipid-based biomarkers show promise for CRC risk assessment and early detection but require standardization and extensive validation for clinical use.

Seasonal influence on tomato fruit metabolome profile: Implications for ABA signaling in multi-stress resilience

The increasing effects of climate change are leading to an increase in the number and intensity of extreme events, making it essential to study how plants respond to various stresses occurring simultaneously. A crucial regulator of plant responses to abiotic stress is abscisic acid (ABA), as its accumulation in response to stress leads to transcriptomic and metabolomic changes that contribute to plant stress tolerance. In the present study, we investigated how ABA, stress conditions (salinity, water deficit and their combination) and seasons (autumn-winter and spring-summer) regulate tomato fruit yield and metabolism using tomato wild type (WT) and the ABA-deficient flacca mutant (flc) under stress conditions in cold and warm seasons. Our results showed that the applied stresses did not have the same effect in the warm season as in the cold season. In WT plants, the levels of other flavonoids, lignans and other polyphenols were higher in summer fruits, whereas the levels of anthocyanins, flavanols, flavonols, phenolic acids and stilbenes were higher in winter fruits. Furthermore, the significant increase in anthocyanins and flavonols was associated with the combination of salinity + water deficit in both seasons. Additionally, under certain conditions, flc mutants showed an enrichment of the superclasses of benzenoids and organosulphur compounds. The synthesis of phenolic compounds in flc fruits was also significantly different compared to WT plants. Thus, the metabolic profile of tomato fruits varies significantly with endogenous ABA levels, season of cultivation and applied stress treatments, highlighting the multifactorial nature of plant responses to combined environmental factors.

Simulated LC-MS Data Set for Assessing the Metabolomics Data Processing Pipeline Implemented into MVAPACK

Metabolomics commonly relies on using one-dimensional (1D) 1H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation ≤ 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture andMycobacterium smegmatiscell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

Peculiarity of the early metabolomic response in tomato after urea, ammonium or nitrate supply

Nitrogen (N) is the nutrient most applied in agriculture as fertilizer (as nitrate, Nit; ammonium, A; and/or urea, U, forms) and its availability strongly constrains the crop growth and yield. To investigate the early response (24 h) of N-deficient tomato plants to these three N forms, a physiological and molecular study was performed. In comparison to N-deficient plants, significant changes in the transcriptional, metabolomic and ionomic profiles were observed. As a probable consequence of N mobility in plants, a wide metabolic modulation occurred in old leaves rather than in young leaves. The metabolic profile of U and A-treated plants was more similar than Nit-treated plant profile, which in turn presented the lowest metabolic modulation with respect to N-deficient condition. Urea and A forms induced some changes at the biosynthesis of secondary metabolites, amino acids and phytohormones. Interestingly, a specific up-regulation by U and down-regulation by A of carbon synthesis occurred in roots. Along with the gene expression, data suggest that the specific N form influences the activation of metabolic pathways for its assimilation (cytosolic GS/AS and/or plastidial GS/GOGAT cycle). Urea induced an up-concentration of Cu and Mn in leaves and Zn in whole plant. This study highlights a metabolic reprogramming depending on the N form applied, and it also provide evidence of a direct relationship between urea nutrition and Zn concentration. The understanding of the metabolic pathways activated by the different N forms represents a milestone in improving the efficiency of urea fertilization in crops.

Metabolomics and Cardiovascular Risk in Patients with Heart Failure: A Systematic Review and Meta-Analysis

The associations of plasma metabolites with adverse cardiovascular (CV) outcomes are still underexplored and may be useful in CV risk stratification. We performed a systematic review and meta-analysis to establish correlations between blood metabolites and adverse CV outcomes in patients with heart failure (HF). Four cohorts were included, involving 83 metabolites and 37 metabolite ratios, measured in 1158 HF patients. Hazard ratios (HR) of 42 metabolites and 3 metabolite ratios, present in at least two studies, were combined through meta-analysis. Higher levels of histidine (HR 0.74, 95% CI [0.64; 0.86]) and tryptophan (HR 0.82 [0.71; 0.96]) seemed protective, whereas higher levels of symmetric dimethylarginine (SDMA) (HR 1.58 [1.30; 1.93]), N-methyl-1-histidine (HR 1.56 [1.27; 1.90]), SDMA/arginine (HR 1.38 [1.14; 1.68]), putrescine (HR 1.31 [1.06; 1.61]), methionine sulfoxide (HR 1.26 [1.03; 1.52]), and 5-hydroxylysine (HR 1.25 [1.05; 1.48]) were associated with a higher risk of CV events. Our findings corroborate important associations between metabolic imbalances and a higher risk of CV events in HF patients. However, the lack of standardization and data reporting hampered the comparison of a higher number of studies. In a future clinical scenario, metabolomics will greatly benefit from harmonizing sample handling, data analysis, reporting, and sharing.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

Integrated multi-omic approach reveals the effect of a Graminaceae-derived biostimulant and its lighter fraction on salt-stressed lettuce plants

Plant biostimulants are widely applied in agriculture for their ability to improve plant fitness. In the present work, the impact of Graminaceae-derived protein hydrolysate (P) and its lighter molecular fraction F3 (< 1 kDa) on lettuce plants, subjected to either no salt or high salt conditions, was investigated through the combination of metabolomics and transcriptomics. The results showed that both treatments significantly modulated the transcriptome and metabolome of plants under salinity stress, highlighting an induction of the hormonal response. Nevertheless, P and F3 also displayed several peculiarities. F3 specifically modulated the response to ethylene and MAPK signaling pathway, whereas P treatment induced a down-accumulation of secondary metabolites, albeit genes controlling the biosynthesis of osmoprotectants and antioxidants were up-regulated. Moreover, according with the auxin response modulation, P promoted cell wall biogenesis and plasticity in salt-stressed plants. Notably, our data also outlined an epigenetic control of gene expression induced by P treatment. Contrarily, experimental data are just partially in agreement when not stressed plants, treated with P or F3, were considered. Indeed, the reduced accumulation of secondary metabolites and the analyses of hormone pathways modulation would suggest a preferential allocation of resources towards growth, that is not coherent with the down-regulation of the photosynthetic machinery, the CO2 assimilation rate and leaves biomass. In conclusion, our data demonstrate that, although they might activate different mechanisms, both the P and F3 can result in similar benefits, as far as the accumulation of protective osmolytes and the enhanced tolerance to oxidative stress are concerned. Notably, the F3 fraction exhibits slightly greater growth promotion effects under high salt conditions. Most importantly, this research further corroborates that biostimulants' mode of action is dependent on plants' physiological status and their composition, underscoring the importance of investigating the bioactivity of the different molecular components to design tailored applications for the agricultural practice.

Comprehensive blood metabolomics profiling of Parkinson's disease reveals coordinated alterations in xanthine metabolism

Parkinson's disease (PD) is a highly heterogeneous disorder influenced by several environmental and genetic factors. Effective disease-modifying therapies and robust early-stage biomarkers are still lacking, and an improved understanding of the molecular changes in PD could help to reveal new diagnostic markers and pharmaceutical targets. Here, we report results from a cohort-wide blood plasma metabolic profiling of PD patients and controls in the Luxembourg Parkinson's Study to detect disease-associated alterations at the level of systemic cellular process and network alterations. We identified statistically significant changes in both individual metabolite levels and global pathway activities in PD vs. controls and significant correlations with motor impairment scores. As a primary observation when investigating shared molecular sub-network alterations, we detect pronounced and coordinated increased metabolite abundances in xanthine metabolism in de novo patients, which are consistent with previous PD case/control transcriptomics data from an independent cohort in terms of known enzyme-metabolite network relationships. From the integrated metabolomics and transcriptomics network analysis, the enzyme hypoxanthine phosphoribosyltransferase 1 (HPRT1) is determined as a potential key regulator controlling the shared changes in xanthine metabolism and linking them to a mechanism that may contribute to pathological loss of cellular adenosine triphosphate (ATP) in PD. Overall, the investigations revealed significant PD-associated metabolome alterations, including pronounced changes in xanthine metabolism that are mechanistically congruent with alterations observed in independent transcriptomics data. The enzyme HPRT1 may merit further investigation as a main regulator of these network alterations and as a potential therapeutic target to address downstream molecular pathology in PD.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging

BACKGROUND

The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse.

AIM OF REVIEW

The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Exploring the allelopathic autotoxicity mechanism of ginsenosides accumulation under ginseng decomposition based on integrated analysis of transcriptomics and metabolomics

Continuous cropping obstacles seriously constrained the sustainable development of the ginseng industry. The allelopathic autotoxicity of ginsenosides is the key "trigger" of continuous cropping obstacles in ginseng. During harvest, the ginseng plants could be broken and remain in the soil. The decomposition of ginseng residue in soil is one of the important release ways of ginsenosides. Therefore, the allelopathic mechanism of ginsenosides through the decomposed release pathway needs an in-depth study. To investigate this allelopathic regulation mechanism, the integrated analysis of transcriptomics and metabolomics was applied. The prototype ginsenosides in ginseng were detected converse to rare ginsenosides during decomposition. The rare ginsenosides caused more serious damage to ginseng hairy root cells and inhibited the growth of ginseng hairy roots more significantly. By high-throughput RNA sequencing gene transcriptomics study, the significantly differential expressed genes (DEGs) were obtained under prototype and rare ginsenoside interventions. These DEGs were mainly enriched in the biosynthesis of secondary metabolites and metabolic pathways, phytohormone signal transduction, and protein processing in endoplasmic reticulum pathways. Based on the functional enrichment of DEGs, the targeted metabolomics analysis based on UPLC-MS/MS determination was applied to screen endogenous differential metabolized phytohormones (DMPs). The influence of prototype and rare ginsenosides on the accumulation of endogenous phytohormones was studied. These were mainly involved in the biosynthesis of diterpenoid, zeatin, and secondary metabolites, phytohormone signal transduction, and metabolic pathways. After integrating the transcriptomics and metabolomics analysis, ginsenosides could regulate the genes in phytohormone signaling pathways to influence the accumulation of JA, ABA, and SA. The conclusion was that the prototype ginsenosides were converted into rare ginsenosides by ginseng decomposition and released into the soil, which aggravated its allelopathic autotoxicity. The allelopathic mechanism was to intervene in the response regulation of genes related to the metabolic accumulation of endogenous phytohormones in ginseng. This result provides a reference for the in-depth study of continuous cropping obstacles of ginseng.

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

Mutations in starch biosynthesis genes affect chloroplast development in wheat pericarp

Starch bioengineering in cereals has produced a plethora of genotypes with new nutritional and technological functionalities. Modulation of amylose content from 0 to 100% was inversely correlated with starch digestibility and promoted a lower glycemic index in food products. In wheat, starch mutants have been reported to exhibit various side effects, mainly related to the seed phenotype. However, little is known about the impact of altered amylose content and starch structure on plant metabolism. Here, three bread wheat starch mutant lines with extreme phenotypes in starch branching and amylose content were used to study plant responses to starch structural changes. Omics profiling of gene expression and metabolic patterns supported changes, confirmed by ultrastructural analysis in the chloroplast of the immature seeds. In detail, the identification of differentially expressed genes belonging to functional categories related to photosynthesis, chloroplast and thylakoid (e.g. CURT1), the alteration in the accumulation of photosynthesis-related compounds, and the chloroplast alterations (aberrant shape, grana stacking alteration, and increased number of plastoglobules) suggested that the modification of starch structure greatly affects starch turnover in the chloroplast, triggering oxidative stress (ROS accumulation) and premature tissue senescence. In conclusion, this study highlighted a correlation between starch structure and chloroplast functionality in the wheat kernel.

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehensive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks.

Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences

Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.

Precision fermentation with mass spectrometry-based spent media analysis

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

Metabolomics Insight into the Variety-Mediated Responses to Aspergillus carbonarius Infection in Grapevine Berries

Limited knowledge regarding the susceptibility of grape varieties to ochratoxin A (OTA)-producing fungi is available to date. This study aimed to investigate the susceptibility of different grape varieties to Aspergillus carbonarius concerning OTA contamination and modulation at the metabolome level. Six grape varieties were selected, sampled at early veraison and ripening, artificially inoculated with A. carbonarius, and incubated at two temperature regimes. Significant differences were observed across cultivars, with Barbera showing the highest incidence of moldy berries (around 30%), while Malvasia and Ortrugo showed the lowest incidence (about 2%). OTA contamination was the lowest in Ortrugo and Malvasia, and the highest in Croatina, although it was not significantly different from Barbera, Merlot, and Sauvignon Blanc. Fungal development and mycotoxin production changed with grape variety; the sugar content in berries could also have played a role. Unsupervised multivariate statistical analysis from metabolomic fingerprints highlighted cultivar-specific responses, although a more generalized response was observed by supervised OPLS-DA modeling. An accumulation of nitrogen-containing compounds (alkaloids and glucosinolates), phenylpropanoids, and terpenoids, in addition to phytoalexins, was observed in all samples. A broader modulation of the metabolome was observed in white grapes, which were less contaminated by OTA. Jasmonates and oxylipins were identified as critical upstream modulators in metabolomic profiles. A direct correlation between the plant defense machinery and OTA was not observed, but the information was acquired and can contribute to optimizing preventive actions.

A 3-year application of different mycorrhiza-based plant biostimulants distinctively modulates photosynthetic performance, leaf metabolism, and fruit quality in grapes (Vitis vinifera L.)

The use of microbial biostimulants in agriculture is recognized as a sustainable approach to promoting crop productivity and quality due to improved nutrient uptake, enhanced stress tolerance, and improved ability to cope with non-optimal environments. The present paper aimed to comparatively investigate the effect of seven different commercial mycorrhizal-based treatments in terms of yield, phytochemical components, and technological traits of Malvasia di Candia Aromatica grape (Vitis vinifera L.) plants. Metabolomic analysis and photosynthetic performance were first investigated in leaves to point out biochemical differences related to plant growth. Higher photosynthetic efficiency and better PSII functioning were found in biostimulant-treated vines, reflecting an overall decrease in photoinhibition compared to untreated plants. Untargeted metabolomics followed by multivariate statistics highlighted a robust reprogramming of primary (lipids) and secondary (alkaloids and terpenoids) metabolites in treated plants. The analysis of berry yield and chemical components exhibited significant differences depending on the biostimulant product. Generally, berries obtained from treated plants displayed improved contents of polyphenols and sugars, while yield remained unchanged. These results elucidated the significant role of microbial biostimulants in determining the quality of grape berries and eliciting biochemical changes in vines.

Copper boosts the biostimulant activity of a vegetal-derived protein hydrolysate in basil: morpho-physiological and metabolomics insights

In addition to be used as a plant protection agent, copper (Cu) is also an essential micronutrient for plant growth and development. The bioavailability of Cu in agricultural systems can be limited due to its specific physical-chemical characteristics, leading to imbalances in plant production. To address this issue, an experimental trial was conducted on Genovese basil (Ocimum basilicum L.) in protected conditions to comparatively evaluate the effects of a vegetable protein hydrolysate (VPH), free Cu and Cu complexed with peptides and amino acids of vegetal origin (Cu and Cu-VPH, respectively), and a combination of VPH and Cu-VPH (VPH+Cu-VPH). The study showed that the combined application of VPH+Cu-VPH led to a significant average increase of 16.3% in fresh yield compared to the untreated Control and Cu treatment. This finding was supported by an improved photosynthetic performance in ACO2 (+29%) and Fv/Fm (+7%). Furthermore, mineral analysis using ICP OES demonstrated that Cu and Cu-VPH treatments determined, on average, a 15.1-, 16.9-, and 1.9-fold increase in Cu in plant tissues compared to control, VPH, and VPH+Cu-VPH treatments, respectively. However, the VPH+Cu-VPH treatment induced the highest contents of the other analyzed ions, except for P. In particular, Mg, Mn, Ca, and Fe, which take part in the constitution of chlorophylls, water splitting system, and photosynthetic electron transport chain, increased by 23%, 21%, 25%, and 32% compared to respective controls. Indeed, this improved the photosynthetic efficiency and the carboxylation capacity of the plants, and consequently, the physiological and productive performance of Genovese basil, compared to all other treatments and control. Consistently, the untargeted metabolomics also pointed out a distinctive modulation of phytochemical signatures as a function of the treatment. An accumulation of alkaloids, terpenoids, and phenylpropanoids was observed following Cu treatment, suggesting an oxidative imbalance upon metal exposure. In contrast, a mitigation of oxidative stress was highlighted in Cu-VPH and VPH+Cu-VPH, where the treatments reduced stress-related metabolites. Overall, these results highlight an interaction between Cu and VPH, hence paving the way towards the combined use of Cu and biostimulants to optimize agronomic interventions.

Trichoderma spp.-mediated mitigation of heat, drought, and their combination on the Arabidopsis thaliana holobiont: a metabolomics and metabarcoding approach

Introduction

The use of substances to increase productivity and resource use efficiency is now essential to face the challenge of feeding the rising global population with the less environmental impact on the ecosystems. Trichoderma-based products have been used as biopesticides, to inhibit pathogenic microorganisms, and as biostimulants for crop growth, nutrient uptake promotion, and resistance to abiotic stresses.

Methods

In this work, plant metabolomics combined with roots and rhizosphere bacterial metabarcoding were exploited to inspect the performance of Trichoderma spp. biostimulants on Arabidopsis thaliana under drought, heat and their combination and its impact on plant holobiont.

Results and discussion

An overall modulation of N-containing compounds, phenylpropanoids, terpenes and hormones could be pointed out by metabolomics. Moreover, metabarcoding outlined an impact on alpha and beta-diversity with an abundance of Proteobacteria, Pseudomonadales, Burkholderiales, Enterobacteriales and Azospirillales. A holobiont approach was applied as an integrated analytical strategy to resolve the coordinated and complex dynamic interactions between the plant and its rhizosphere bacteria using Arabidopsis thaliana as a model host species.

Exploratory data fusion of untargeted multimodal LC-HRMS with annotation by LCMS-TOF-ion mobility: White wine case study

Applied sciences have increased focus on omics studies which merge data science with analytical tools. These studies often result in large amounts of data produced and the objective is to generate meaningful interpretations from them. This can sometimes mean combining and integrating different datasets through data fusion techniques. The most strategic course of action when dealing with products of unknown profile is to use exploratory approaches. For omics, this means using untargeted analytical methods and exploratory data analysis techniques. The current study aimed to perform data fusion on untargeted multimodal (negative and positive mode) liquid chromatography-high-resolution mass spectrometry data using multiple factor analysis. The data fusion results were interpreted using agglomerative hierarchical clustering on biplot projections. The study reduced the thousands of spectral signals processed to less than a hundred features (a primary parameter combination of retention time and mass-to-charge ratios, RT_m/z). The correlations between cluster members (samples and features from) were calculated and the top 10% highly correlated features were identified for each cluster. These features were then tentatively identified using secondary parameters (drift time, ion mobility constant and collision cross-section values) from the ion mobility spectra. These ion mobility (secondary) parameters can be used for future studies in wine chemical analysis and added to the growing list of annotated chemical signals in applied sciences.

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

Studies combining metabolomics and genetics, known as metabolite genome-wide association studies (mGWAS), have provided valuable insights into our understanding of the genetic control of metabolite levels. However, the biological interpretation of these associations remains challenging due to a lack of existing tools to annotate mGWAS gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we computed the shortest reactional distance (SRD) based on the curated knowledge of the KEGG database to explore its utility in enhancing the biological interpretation of results from three independent mGWAS, including a case study on sickle cell disease patients. Results show that, in reported mGWAS pairs, there is an excess of small SRD values and that SRD values and p-values significantly correlate, even beyond the standard conservative thresholds. The added-value of SRD annotation is shown for identification of potential false negative hits, exemplified by the finding of gene-metabolite associations with SRD ≤1 that did not reach standard genome-wide significance cut-off. The wider use of this statistic as an mGWAS annotation would prevent the exclusion of biologically relevant associations and can also identify errors or gaps in current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs that can be used to integrate statistical evidence to biological networks.

Liquid chromatography-mass spectrometry metabolite library for metabolomics: Evaluating column suitability using a scoring approach

Untargeted metabolomic studies require an extensive set of analyte (metabolic) information to be obtained from each analyzed sample. Thus, highly selective, and efficient analytical methodologies together with reversed-phase (RP) or hydrophilic interaction liquid chromatography (HILIC) are usually applied in these approaches. Here, we present a performance comparison of five different chromatographic columns (C18, C8, RP Amide, zicHILIC, OH5 HILIC phases) to evaluate their sufficiency of analysis for a large analyte library, consisting of 817 authentic standards. By taking into account experimental chromatographic parameters (i.e. retention time, peak tailing and asymmetry, FWHM, signal-to-noise ratio and peak area and intensity), the proposed column scoring approach provides a simple criterion that may assist analysis in the select of a stationary phase for those metabolites of interest. RPLC methods offered better results regarding metabolic library coverage, while the zicHILIC stationary phase delivered a bigger number of properly eluted compounds. This study demonstrates the importance of choosing the most suitable configuration for the analysis of different metabolic classes.

The differential modulation of secondary metabolism induced by a protein hydrolysate and a seaweed extract in tomato plants under salinity

Climate change and abiotic stress challenges in crops are threatening world food production. Among others, salinity affects the agricultural sector by significantly impacting yield losses. Plant biostimulants have received increasing attention in the agricultural industry due to their ability to improve health and resilience in crops. The main driving force of these products lies in their ability to modulate plant metabolic processes involved in the stress response. This study's purpose was to investigate the effect of two biostimulant products, including a protein hydrolysate (Clever HX^®) and a seaweed extract with high amino acids content (Ascovip^®), and their combination, on the metabolomics profile of tomato crops grown under salt stress (150 mM NaCl). Several stress indicators (leaf relative water content, membrane stability index, and photosynthesis activity) and leaf mineral composition after salinity stress exposure were assessed to evaluate stress mitigation, together with growth parameters (shoot and root biomasses). After that, an untargeted metabolomics approach was used to investigate the mechanism of action of the biostimulants and their link with the increased resilience to stress. The application of the biostimulants used reduced the detrimental effect of salinity. In saline conditions, protein hydrolysate improved shoot dry weight while seaweed extracts improved root dry weight. Regarding stress indicators, the application of the protein hydrolysate was found to alleviate the membrane damage caused by salinity stress compared to untreated plants. Surprisingly, photosynthetic activity significantly improved after treatment with seaweed extracts, suggesting a close correlation between root development, root water assimilation capacity and photosynthetic activity. Considering the metabolic reprogramming after plant biostimulants application, protein hydrolysates and their combination with seaweed extracts reported a distinctive metabolic profile modulation, mainly in secondary metabolite, lipids and fatty acids, and phytohormones biosynthetic pathways. However, treatment with seaweed extract reported a similar metabolic reprogramming trend compared to salinity stress. Our findings indicate a different mechanism of action modulated by protein hydrolysate and seaweed extract, suggesting stronger activity as a stress mitigator of protein hydrolysate in tomato crops under salinity stress.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for &gt;40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Integrated Metabolomics and Morpho-Biochemical Analyses Reveal a Better Performance of Azospirillum brasilense over Plant-Derived Biostimulants in Counteracting Salt Stress in Tomato

Increased soil salinity is one of the main concerns in agriculture and food production, and it negatively affects plant growth and crop productivity. In order to mitigate the adverse effects of salinity stress, plant biostimulants (PBs) have been indicated as a promising approach. Indeed, these products have a beneficial effect on plants by acting on primary and secondary metabolism and by inducing the accumulation of protective molecules against oxidative stress. In this context, the present work is aimed at comparatively investigating the effects of microbial (i.e., Azospirillum brasilense) and plant-derived biostimulants in alleviating salt stress in tomato plants by adopting a multidisciplinary approach. To do so, the morphological and biochemical effects were assessed by analyzing the biomass accumulation and root characteristics, the activity of antioxidant enzymes and osmotic stress protection. Furthermore, modifications in the metabolomic profiles of both leaves and root exudates were also investigated by ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS). According to the results, biomass accumulation decreased under high salinity. However, the treatment with A. brasilense considerably improved root architecture and increased root biomass by 156% and 118% in non-saline and saline conditions, respectively. The antioxidant enzymes and proline production were enhanced in salinity stress at different levels according to the biostimulant applied. Moreover, the metabolomic analyses pointed out a wide set of processes being affected by salinity and biostimulant interactions. Crucial compounds belonging to secondary metabolism (phenylpropanoids, alkaloids and other N-containing metabolites, and membrane lipids) and phytohormones (brassinosteroids, cytokinins and methylsalicylate) showed the most pronounced modulation. Overall, our results suggest a better performance of A. brasilense in alleviating high salinity than the vegetal-derived protein hydrolysates herein evaluated.

Impact of preanalytical freezing delay time on the stability of metabolites in oral squamous cell carcinoma tissue samples

INTRODUCTION

Metabolite stability is critical for tissue metabolomics. However, changes in metabolites in tissues over time from the operating room to the laboratory remain underexplored.

OBJECTIVES

In this study, we evaluated the effect of postoperative freezing delay time on the stability of metabolites in normal and oral squamous cell carcinoma (OSCC) tissues.

METHODS

Tumor and paired normal tissues from five OSCC patients were collected after surgical resection, and samples was sequentially quenched in liquid nitrogen at 30, 40, 50, 60, 70, 80, 90 and 120 min (80 samples). Untargeted metabolic analysis by liquid chromatography-mass spectrometry/mass spectrometry in positive and negative ion modes was used to identify metabolic changes associated with delayed freezing time. The trends of metabolite changes at 30-120 and 30-60 min of delayed freezing were analyzed.

RESULTS

190 metabolites in 36 chemical classes were detected. After delayed freezing for 120 min, approximately 20% of the metabolites changed significantly in normal and tumor tissues, and differences in the metabolites were found in normal and tumor tissues. After a delay of 60 min, 29 metabolites had changed significantly in normal tissues, and 84 metabolites had changed significantly in tumor tissues. In addition, we constructed three tissue freezing schemes based on the observed variation trends in the metabolites.

CONCLUSION

Delayed freezing of tissue samples has a certain impact on the stability of metabolites. For metabolites with significant changes, we suggest that the freezing time of tissues be reasonably selected according to the freezing schemes and the actual clinical situation.

Current Knowledge in Skin Metabolomics: Updates from Literature Review

Metabolomic profiling is an emerging field consisting of the measurement of metabolites in a biological system. Since metabolites can vary in relation to different stimuli, specific metabolic patterns can be closely related to a pathological process. In the dermatological setting, skin metabolomics can provide useful biomarkers for the diagnosis, prognosis, and therapy of cutaneous disorders. The main goal of the present review is to present a comprehensive overview of the published studies in skin metabolomics. A search for journal articles focused on skin metabolomics was conducted on the MEDLINE, EMBASE, Cochrane, and Scopus electronic databases. Only research articles with electronically available English full text were taken into consideration. Studies specifically focused on cutaneous microbiomes were also excluded from the present search. A total of 97 papers matched all the research criteria and were therefore considered for the present work. Most of the publications were focused on inflammatory dermatoses and immune-mediated cutaneous disorders. Skin oncology also turned out to be a relevant field in metabolomic research. Only a few papers were focused on infectious diseases and rarer genetic disorders. All the major metabolomic alterations published so far in the dermatological setting are described extensively in this review.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography-mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

Between Light and Shading: Morphological, Biochemical, and Metabolomics Insights Into the Influence of Blue Photoselective Shading on Vegetable Seedlings

High nursery densities reduce the seedling quality due to the competition for light. High light intensity, shading, and blue light depletion activate morphophysiological and metabolomic responses in plants, resulting in size modification to gain an advantage over neighboring plants. Our research aimed to unravel the effects of light intensity and quality on nursery seedlings at the morphological and biochemical levels. To this aim, the effect of black shading and blue photoselective shading nets were investigated in terms of morphometric, ionomic, and untargeted metabolomics signatures in Cucurbita pepo L., Citrullus lanatus L., Solanum lycopersicum L., and Solanum melongena L. seedlings. Plant height, diameter, sturdiness index, leaf area, specific leaf area, shoot/root ratio, and mineral content (by ion chromatography-IC) were evaluated. In C. pepo L and C. lanatus L., the blue net reduced the shoot/root and chlorophyll a/b ratios and increased stem diameter and total chlorophyll content. The black net increased plant height, stem diameter, and sturdiness index in Solanum lycopersicum L. and Solanum melongena L. At the same time, unshading conditions reduced leaf area, specific leaf area, shoot/root ratio, and total chlorophyll content. The blue net improved the sturdiness index and quality of C. pepo L. and C. lanatus L. Such impact on morphological parameters induced by the different shading conditions was corroborated by a significant modulation at the metabolomics level. Untargeted metabolomic phytochemical signatures of the selected plants, and the subsequent multivariate analysis coupled to pathway analysis, allowed highlighting a broad and diverse biochemical modulation. Metabolomics revealed that both primary and secondary metabolism were largely affected by the different shading conditions, regardless of the species considered. A common pattern arose to point at the activation of plant energy metabolism and lipid biosynthesis, together with a generalized down accumulation of several secondary metabolites, particularly phenylpropanoids. Our findings indicate an intriguing scientific interest in the effects of selective shading and its application to other species and different phenological stages.

Nitrogen use efficiency, rhizosphere bacterial community, and root metabolome reprogramming due to maize seed treatment with microbial biostimulants

Seed inoculation with beneficial microorganisms has gained importance as it has been proven to show biostimulant activity in plants, especially in terms of abiotic/biotic stress tolerance and plant growth promotion, representing a sustainable way to ensure yield stability under low input sustainable agriculture. Nevertheless, limited knowledge is available concerning the molecular and physiological processes underlying the root-inoculant symbiosis or plant response at the root system level. Our work aimed to integrate the interrelationship between agronomic traits, rhizosphere microbial population and metabolic processes in roots, following seed treatment with either arbuscular mycorrhizal fungi (AMF) or Plant Growth-Promoting Rhizobacteria (PGPR). To this aim, maize was grown under open field conditions with either optimal or reduced nitrogen availability. Both seed treatments increased nitrogen uptake efficiency under reduced nitrogen supply revealed some microbial community changes among treatments at root microbiome level and limited yield increases, while significant changes could be observed at metabolome level. Amino acid, lipid, flavone, lignan, and phenylpropanoid concentrations were mostly modulated. Integrative analysis of multi-omics datasets (Multiple Co-Inertia Analysis) highlighted a strong correlation between the metagenomics and the untargeted metabolomics datasets, suggesting a coordinate modulation of root physiological traits.

The Complex Metabolomics Crosstalk Triggered by Four Molecular Elicitors in Tomato

The elicitation of plant secondary metabolism may offer interesting opportunities in the framework of sustainable approaches in plant science and in terms of their ability to prime resistance to biotic and abiotic stressors. The broad metabolic reprogramming triggered by different molecular elicitors, namely salicylate (SA), polyamines (PAs), and chitosan, was comprehensively investigated using a metabolomics approach and the tomato (Solanum lycopersicum L.) as the model crop. Six different treatments were compared: a negative control (no treatments), a second negative control treated with 1 M acetic acid (the reference for chitosan, since chitosan was solubilized in acetic acid), and four molecular elicitors, 1 mM 2,1,3-benzothiadiazole (BTH, a positive control), 10 mg/mL chitosan, 0.01 mM SA, and a 0.1 mM PA (putrescine, spermidine, and spermine). All treatments determined a slight increase in biomass, in particular following PA treatment. A broad reprogramming of secondary metabolism could be observed, including membrane lipid remodeling, phenylpropanoid antioxidants, and phytohormone crosstalk. Overall, our results suggest that PAs, SA, and BTH shared a systemic acquired resistance (SAR)-related response, whereas chitosan induced a more distinct induced systemic resistance (ISR)-like jasmonate-related response. These results pave the way towards the possible use of elicitors as a sustainable tool in plant science and agriculture by increasing crop resilience to biotic and abiotic stressors without detrimental effects on plant biomass.

Untargeted Phenolic Profiling and Functional Insights of the Aerial Parts and Bulbs of Drimia maritima (L.) Stearn

Drimia maritima (L.) Stearn (squill), belonging to the Asparagaceae family, is acknowledged as a medicinally valuable species from the Drimia genera. In this study, water, methanol, and ethyl acetate extracts of D. maritima aerial parts and bulbs were investigated for their polyphenols profile and evaluated for their antioxidant and enzyme inhibition properties. Phenolics were profiled through an untargeted metabolomics approach using an ultra-high pressure liquid chromatograph coupled to quadrupole-time-of-flight mass spectrometry (UHPLC-QTOF-MS). This analysis revealed an enrichment of low molecular weight phenolics and flavonoids in the aerial parts of D. maritima, while lignans mainly characterized bulb extracts. Antioxidant capacity was investigated by different assays, including phosphomolybdenum assays, radical scavenging (DPPH: 2,2-diphenyl-1-picrylhydrazyl; ABTS: 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)), as well as reducing ability (CUPRAC: cupric reducing antioxidant capacity; FRAP: ferric reducing antioxidant power), and metal chelating. In radical scavenging and reducing power assays, the water extract of aerial parts exhibited the strongest ability (DPPH: 36.99 mg trolox equivalent (TE)/g; ABTS: 85.96 mg TE/g; CUPRAC: 87.37 mg TE/g; FRAP: 55.43 mg TE/g). In general, the ethyl acetate extracts from aerial parts and bulbs provided the weakest antioxidant capacity. Concerning enzyme inhibitory activities, the water extracts of the bulb were poorly active, while the ethyl acetate extracts from both plant portions displayed the best α-amylase inhibitory abilities. The best acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) abilities were recorded by ethyl acetate extract of aerial parts (2.36 mg galantamine equivalent (GALAE)/g) and bulbs (5.10 mg GALAE/g), respectively. Overall, these results support the medicinal aptitude of D. maritima and its possible use as a natural source of antioxidants and enzyme inhibitors with functional potential.

Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature

Metabolite identification remains a bottleneck and a still unregulated area in untargeted LC-MS metabolomics. The metabolomics research community and, in particular, the metabolomics standards initiative (MSI) proposed minimum reporting standards for metabolomics including those for reporting metabolite identification as long ago as 2007. Initially, four levels were proposed ranging from level 1 (unambiguously identified analyte) to level 4 (unidentified analyte). This scheme was expanded in 2014, by independent research groups, to give five levels of confidence. Both schemes provided guidance to the researcher and described the logical steps that had to be made to reach a confident reporting level. These guidelines have been presented and discussed extensively, becoming well-known to authors, editors, and reviewers for academic publications. Despite continuous promotion within the metabolomics community, the application of such guidelines is questionable. The scope of this meta-analysis was to systematically review the current LC-MS-based literature and effectively determine the proportion of papers following the proposed guidelines. Also, within the scope of this meta-analysis was the measurement of the actual identification levels reported in the literature, that is to find how many of the published papers really reached full metabolite identification (level 1) and how many papers did not reach this level.

Review of the recent developments in metabolomics-based phytochemical research

Phytochemicals are important bioactive components present in natural products. Although the health benefits of many food products are well-known and accepted as a common knowledge, the identity of the main bioactive molecules and the mechanism by which they interact in the body of human are often unknown. It was only in the last 30 years when the field of metabolomics had matured that the identification of such molecules with bioactivity has been made possible through the development of instruments to separate and computational techniques to characterize complex samples. This in turn has enabled in vitro studies to quantify the biological activity of the respective phytochemical either in mice models or in humans. In this review, the importance of key dietary phytochemicals such as phenolic acids, flavonoids, carotenoids, resveratrol, curcumin, and capsaicinoids are discussed together with their potential functions for human health. Untargeted metabolomics, in particular, liquid chromatography mass spectrometry, is the most used method to isolate, identify and profile bioactive compounds in the study of phytochemicals in foods. The application of metabolomics in drug discovery is a common practice nowadays and has boosted the drug and/or supplement manufacturing sector.HighlightsPhytochemicals are beneficial compounds for human healthPhytochemicals are plant-based bioactive and obtainable from natural productsUntargeted metabolomics has boosted the discovery of phytochemicals from foodTargeted metabolomics is key in the authentication and screening of phytochemicalsMetabolomics of phytochemicals is reshaping the road to drug and supplement manufacture.

A metabolomics insight into the Cyclic Nucleotide Monophosphate signaling cascade in tomato under non-stress and salinity conditions

Cyclic Nucleotides Monophosphate (cNMP) are key signalling compounds whose role in plant cell signal transduction is still poorly understood. In this work we used sildenafil, a phosphodiesterase (PDE) inhibitor used in human, to amplify the signal cascade triggered by cNMP using tomato as model plant. Metabolomics was then used, together with plant growth and root architecture parameters, to unravel the changes elicited by PDE inhibition either under non-stress and 100 mM NaCl salinity conditions. The PDE inhibitor elicited a significant increase in biomass (+62 %) and root length (+56 %) under no stress conditions, and affected root architecture in terms of distribution over diameter classes. Together with cGMP, others cNMP were modulated by the treatment. Moreover, PDE inhibition triggered a broad metabolic reprogramming involving photosynthesis and secondary metabolism. A complex crosstalk network of phytohormones and other signalling compounds could be observed in treated plants. Nonetheless, metabolites related to redox imbalance processes and NO signalling could be highlighted in tomato following PDE application. Despite salinity damped down the growth-promoting effects of sildenafil, interesting implications in plant mitigation to stress-related detrimental effects could be observed.

Seed Priming With Protein Hydrolysates Improves Arabidopsis Growth and Stress Tolerance to Abiotic Stresses

The use of plant biostimulants contributes to more sustainable and environmentally friendly farming techniques and offers a sustainable alternative to mitigate the adverse effects of stress. Protein hydrolysate-based biostimulants have been described to promote plant growth and reduce the negative effect of abiotic stresses in different crops. However, limited information is available about their mechanism of action, how plants perceive their application, and which metabolic pathways are activating. Here we used a multi-trait high-throughput screening approach based on simple RGB imaging and combined with untargeted metabolomics to screen and unravel the mode of action/mechanism of protein hydrolysates in Arabidopsis plants grown in optimal and in salt-stress conditions (0, 75, or 150 mM NaCl). Eleven protein hydrolysates from different protein sources were used as priming agents in Arabidopsis seeds in three different concentrations (0.001, 0.01, or 0.1 μl ml-1). Growth and development-related traits as early seedling establishment, growth response under stress and photosynthetic performance of the plants were dynamically scored throughout and at the end of the growth period. To effectively classify the functional properties of the 11 products a Plant Biostimulant Characterization (PBC) index was used, which helped to characterize the activity of a protein hydrolysate based on its ability to promote plant growth and mitigate stress, and to categorize the products as plant growth promoters, growth inhibitors and/or stress alleviator. Out of 11 products, two were identified as highly effective growth regulators and stress alleviators because they showed a PBC index always above 0.51. Using the untargeted metabolomics approach, we showed that plants primed with these best performing biostimulants had reduced contents of stress-related molecules (such as flavonoids and terpenoids, and some degradation/conjugation compounds of phytohormones such as cytokinins, auxins, gibberellins, etc.), which alleviated the salt stress response-related growth inhibition.

A Milk Foodomics Investigation into the Effect of Pseudomonas fluorescens Growth under Cold Chain Conditions

Pseudomonas fluorescens is a psychrotrophic species associated with milk spoilage because of its lipolytic and proteolytic activities. Consequently, monitoring P. fluorescens or its antecedent activity in milk is critical to preventing quality defects of the product and minimizing food waste. Therefore, in this study, untargeted metabolomics and peptidomics were used to identify the changes in milk related to P. fluorescens activity by simulating the low-temperature conditions usually found in milk during the cold chain. Both unsupervised and supervised multivariate statistical approaches showed a clear effect caused by the P. fluorescens inoculation on milk samples. Our results showed that the levels of phosphatidylglycerophosphates and glycerophospholipids were directly related to the level of contamination. In addition, our metabolomic approach allowed us to detect lipid and protein degradation products that were directly correlated with the degradative metabolism of P. fluorescens. Peptidomics corroborated the proteolytic propensity of P. fluorescens-contaminated milk, but with lower sensitivity. The results obtained from this study provide insights into the alterations related to P. fluorescens 39 contamination, both pre and post heat treatment. This approach could represent a potential tool to retrospectively understand the actual quality of milk under cold chain storage conditions, either before or after heat treatments.

Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

The variety, terroir, and harvest types affect the yield and the phenolic and sterolic profiles of hemp seed oil

In this work, considering the rising interest towards the exploitation of hemp seed oil in human nutrition, 45 hemp seeds from mono-variety fields were analyzed for their yield, oil content, in vitro antioxidant activity, followed by a comprehensive assessment of phenolic and sterolic composition. The results demonstrated that seed dimension is inversely correlated to total oil content, thus being a potential reference for quality assessment of seeds and for further improvement of hemp varieties. The UHPLC-QTOF metabolomic analysis revealed a large abundance of phytosterols, lower-molecular-weight phenolic acids, and lignanamides. Differences across varieties could be described, with Diana hemp seed oil having the highest cumulative abundance of phytochemicals, recording 6.04 mg/g. Overall, the in vitro antioxidant activity results indicated that hemp seed oil antioxidants have a low potential for preventing oil rancidity, with phenolic acids being the most active radical scavengers. Besides, in the group of Futura 75 samples cultivated across Italy, the type of harvesting affected the acidity value significantly as a consequence of mechanical harvest and post-harvest handling. Finally, multivariate statistics following untargeted metabolomic analysis showed that variety, geographical origin, and harvest-type were able to affect the phytochemical profiles with different incidences, with some phytochemicals proposed for the first time as potential discriminant markers.

The mwtab Python Library for RESTful Access and Enhanced Quality Control, Deposition, and Curation of the Metabolomics Workbench Data Repository

The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. MW has been constantly evolving; updating its ‘mwTab’ text file format, adding a JavaScript Object Notation (JSON) file format, implementing a REpresentational State Transfer (REST) interface, and nearly quadrupling the number of datasets hosted on the repository within the last three years. In order to keep up with the quickly evolving state of the MW repository, the ‘mwtab’ Python library and package have been continuously updated to mirror the changes in the ‘mwTab’ and JSONized formats and contain many new enhancements including methods for interacting with the MW REST interface, enhanced format validation features, and advanced features for parsing and searching for specific metabolite data and metadata. We used the enhanced format validation features to evaluate all available datasets in MW to facilitate improved curation and FAIRness of the repository. The ‘mwtab’ Python package is now officially released as version 1.0.1 and is freely available on GitHub and the Python Package Index (PyPI) under a Clear Berkeley Software Distribution (BSD) license with documentation available on ReadTheDocs.

Metabolomics, metabolic flux analysis and cancer pharmacology

Metabolic reprogramming is a hallmark of cancer and increasing evidence suggests that reprogrammed cell metabolism supports tumor initiation, progression, metastasis and drug resistance. Understanding metabolic dysregulation may provide therapeutic targets and facilitate drug research and development for cancer therapy. Metabolomics enables the high-throughput characterization of a large scale of small molecule metabolites in cells, tissues and biofluids, while metabolic flux analysis (MFA) tracks dynamic metabolic activities using stable isotope tracer methods. Recent advances in metabolomics and MFA technologies make them powerful tools for metabolic profiling and characterizing metabolic activities in health and disease, especially in cancer research. In this review, we introduce recent advances in metabolomics and MFA analytical technologies, and provide the first comprehensive summary of the most commonly used isotope tracing methods. In addition, we highlight how metabolomics and MFA are applied in cancer pharmacology studies particularly for discovering targetable metabolic vulnerabilities, understanding the mechanisms of drug action and drug resistance, exploring potential strategies with dietary intervention, identifying cancer biomarkers, as well as enabling precision treatment with pharmacometabolomics.

Gas exchange, vine performance and modulation of secondary metabolism in Vitis vinifera L. cv Barbera following long-term nitrogen deficit

A reprogramming of secondary metabolism to acclimate to nitrogen deficiency was seen in grapevine eliciting an accumulation of strigolactones and jasmonate. This response links with photosynthetic compensation and enhanced ripening. In addition to the metabolism directly related to nitrogen assimilation, long-term nitrogen depletion may affect plant secondary metabolism, in turn affecting grapevine performance. In this work, the effect of nitrogen deficit was investigated in V. vinifera cv. Barbera potted vines following three years of deprivation, using a combination of morpho-physiological assessments and mass spectrometry-based untargeted metabolomics. Plants grown under nitrogen limitation showed reduced growth and even more curtailed yields, lowered SPAD values, and a quite preserved leaf gas exchange, compared to plants grown under non-limiting nitrogen availability. Ripening was decidedly accelerated, and berry composition improved in terms of higher sugar and phenolic contents under nitrogen-limiting conditions. Metabolomics showed the broad involvement of secondary metabolism in acclimation to nitrogen deficiency, including a distinctive modulation of the phytohormone profile. Several nitrogen-containing metabolites were down accumulated under nitrogen-limiting conditions, including alkaloids, glucosinolates, hypoxanthine, and inosine. On the other hand, phenylpropanoids showed an accumulation trend. Concerning the recruitment of hormones, nitrogen deprivation elicited an accumulation of strigolactones and jasmonate. Noteworthy, both strigolactones and jasmonates have been previously related to increased photosynthetic efficiency under abiotic stress. Furthermore, the severe reduction of lateral shoot development we recorded in N-deprived vines is consistent with the accumulation of strigolactones. Overall, our results suggest that nitrogen deprivation induced a rather broad metabolic reprogramming, mainly including secondary metabolism and hormones profile, reflected in the modulation of photosynthetic performance, canopy growth, and possibly fruit quality.

Integration of Gas Exchange With Metabolomics: High-Throughput Phenotyping Methods for Screening Biostimulant-Elicited Beneficial Responses to Short-Term Water Deficit

Biostimulants are emerging as a feasible tool for counteracting reduction in climate change-related yield and quality under water scarcity. As they are gaining attention, the necessity for accurately assessing phenotypic variables in their evaluation is emerging as a critical issue. In light of this, high-throughput phenotyping techniques have been more widely adopted. The main bottleneck of these techniques is represented by data management, which needs to be tailored to the complex, often multifactorial, data. This calls for the adoption of non-linear regression models capable of capturing dynamic data and also the interaction and effects between multiple factors. In this framework, a commercial glycinebetaine- (GB-) based biostimulant (Vegetal B60, ED&F Man) was tested and distributed at a rate of 6 kg/ha. Exogenous application of GB, a widely accumulated and documented stress adaptor molecule in plants, has been demonstrated to enhance the plant abiotic stress tolerance, including drought. Trials were conducted on tomato plants during the flowering stage in a greenhouse. The experiment was designed as a factorial combination of irrigation (water-stressed and well-watered) and biostimulant treatment (treated and control) and adopted a mixed phenotyping-omics approach. The efficacy of a continuous whole-canopy multichamber system coupled with generalized additive mixed modeling (GAMM) was evaluated to discriminate between water-stressed plants under the biostimulant treatment. Photosynthetic performance was evaluated by using GAMM, and was then correlated to metabolic profile. The results confirmed a higher photosynthetic efficiency of the treated plants, which is correlated to biostimulant-mediated drought tolerance. Furthermore, metabolomic analyses demonstrated the priming effect of the biostimulant for stress tolerance and detoxification and stabilization of photosynthetic machinery. In support of this, the overaccumulation of carotenoids was particularly relevant, given their photoprotective role in preventing the overexcitation of photosystem II. Metabolic profile and photosynthetic performance findings suggest an increased effective use of water (EUW) through the overaccumulation of lipids and leaf thickening. The positive effect of GB on water stress resistance could be attributed to both the delayed onset of stress and the elicitation of stress priming through the induction of H₂O₂-mediated antioxidant mechanisms. Overall, the mixed approach supported by a GAMM analysis could prove a valuable contribution to high-throughput biostimulant testing.

Dataset on the Effects of Different Pre-Harvest Factors on the Metabolomics Profile of Lettuce (Lactuca sativa L.) Leaves

The study of the relationship between cultivated plants and environmental factors can provide information ranging from a deeper understanding of the plant biological system to the development of more effective management strategies for improving yield, quality, and sustainability of the produce. In this article, we present a comprehensive metabolomics dataset of two phytochemically divergent lettuce (Lactuca sativa L.) butterhead varieties under different growing conditions. Plants were cultivated in hydroponics in a growth chamber with ambient control. The pre-harvest factors that were independently investigated were light intensity (two levels), the ionic strength of the nutrient solutions (three levels), and the molar ratio of three macroelements (K, Mg, and Ca) in the nutrient solution (three levels). We used an untargeted, mass-spectrometry-based approach to characterize the metabolomics profiles of leaves harvested 19 days after transplant. The data revealed the ample impact on both primary and secondary metabolism and its range of variation. Moreover, our dataset is useful for uncovering the complex effects of the genotype, the environmental factor(s), and their interaction, which may deserve further investigation.

Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study

The variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups or by a primary-replica strategy where models are developed on a 'primary' setup and the test data are generated on 'replicate' setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable. However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if the same samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumental configuration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correct for them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35 Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST (European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allows various instrumental configurations ranging from highly confocal setups to fibre-optic based systems with different excitation wavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts, intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improve the inter-laboratory studies.