Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices

Long-Term Consumption of Purified Water Altered Amino Acid, Fatty Acid and Energy Metabolism in Livers of Rats

The consumption of low-mineral water has been increasing worldwide. Drinking low-mineral water is associated with cardiovascular disease, osteopenia, and certain neurodegenerative diseases. However, the specific mechanism remains unclear. The liver metabolic alterations in rats induced by drinking purified water for 3 months were investigated with a metabolomics-based strategy. Compared with the tap water group, 74 metabolites were significantly changed in the purified water group (6 increased and 68 decreased), including 29 amino acids, 11 carbohydrates, 10 fatty acids, 7 short chain fatty acids (SCFAs), and 17 other biomolecules. Eight metabolic pathways were significantly changed, namely aminoacyl-tRNA biosynthesis; nitrogen metabolism; alanine, aspartate and glutamate metabolism; arginine and proline metabolism; histidine metabolism; biosynthesis of unsaturated fatty acids; butanoate metabolism; and glycine, serine and threonine metabolism. These changes suggested that consumption of purified water induced negative nitrogen balance, reduced expression of some polyunsaturated fatty acids and SCFAs, and disturbed energy metabolism in rats. These metabolic disturbances may contribute to low-mineral-water-associated health risks. The health risk of consuming low-mineral water requires attention.

Metabolite Profiling and Biological Activity Assessment of Paeonia ostii Anthers and Pollen Using UPLC-QTOF-MS

Paeonia ostii is an important economic oil and medicinal crop. Its anthers are often used to make tea in China with beneficial effects on human health. However, the metabolite profiles, as well as potential biological activities of P. ostii anthers and the pollen within anthers have not been systematically analyzed, which hinders the improvement of P. ostii utilization. With comprehensive untargeted metabolomic analysis using UPLC-QTOF-MS, we identified a total of 105 metabolites in anthers and pollen, mainly including phenylpropanoids, polyketides, organic acids, benzenoids, lipids, and organic oxygen compounds. Multivariate statistical analysis revealed the metabolite differences between anthers and pollen, with higher carbohydrates and flavonoids content in pollen and higher phenolic content in anthers. Meanwhile, both anthers and pollen extracts exhibited antioxidant activity, antibacterial activity, α-glucosidase and α-amylase inhibitory activity. In general, the anther stage of S4 showed the highest biological activity among all samples. This study illuminated the metabolites and biological activities of anthers and pollen of P. ostii, which supports the further utilization of them.

Photoperiod-Dependent Nutrient Accumulation in Rice Cultivated in Plant Factories: A Comparative Metabolomic Analysis

Plant factories offer a promising solution to some of the challenges facing traditional agriculture, allowing for year-round rapid production of plant-derived foods. However, the effects of conditions in plant factories on metabolic nutrients remain to be explored. In this study, we used three rice accessions (KongYu131, HuangHuaZhan, and Kam Sweet Rice) as objectives, which were planted in a plant factory with strict photoperiods that are long-day (12 h light/12 h dark) or short-day (8 h light/16 h dark). A total of 438 metabolites were detected in the harvested rice grains. The difference in photoperiod leads to a different accumulation of metabolites in rice grains. Most metabolites accumulated significantly higher levels under the short-day condition than the long-day condition. Differentially accumulated metabolites were enriched in the amino acids and vitamin B6 pathway. Asparagine, pyridoxamine, and pyridoxine are key metabolites that accumulate at higher levels in rice grains harvested from the short-day photoperiod. This study reveals the photoperiod-dependent metabolomic differences in rice cultivated in plant factories, especially the metabolic profiling of taste- and nutrition-related compounds.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Diagnostic utility of clinicodemographic, biochemical and metabolite variables to identify viable pregnancies in a symptomatic cohort during early gestation

A significant number of pregnancies are lost in the first trimester and 1-2% are ectopic pregnancies (EPs). Early pregnancy loss in general can cause significant morbidity with bleeding or infection, while EPs are the leading cause of maternal mortality in the first trimester. Symptoms of pregnancy loss and EP are very similar (including pain and bleeding); however, these symptoms are also common in live normally sited pregnancies (LNSP). To date, no biomarkers have been identified to differentiate LNSP from pregnancies that will not progress beyond early gestation (non-viable or EPs), defined together as combined adverse outcomes (CAO). In this study, we present a novel machine learning pipeline to create prediction models that identify a composite biomarker to differentiate LNSP from CAO in symptomatic women. This prospective cohort study included 370 participants. A single blood sample was prospectively collected from participants on first emergency presentation prior to final clinical diagnosis of pregnancy outcome: LNSP, miscarriage, pregnancy of unknown location (PUL) or tubal EP (tEP). Miscarriage, PUL and tEP were grouped together into a CAO group. Human chorionic gonadotrophin β (β-hCG) and progesterone concentrations were measured in plasma. Serum samples were subjected to untargeted metabolomic profiling. The cohort was randomly split into train and validation data sets, with the train data set subjected to variable selection. Nine metabolite signals were identified as key discriminators of LNSP versus CAO. Random forest models were constructed using stable metabolite signals alone, or in combination with plasma hormone concentrations and demographic data. When comparing LNSP with CAO, a model with stable metabolite signals only demonstrated a modest predictive accuracy (0.68), which was comparable to a model of β-hCG and progesterone (0.71). The best model for LNSP prediction comprised stable metabolite signals and hormone concentrations (accuracy = 0.79). In conclusion, serum metabolite levels and biochemical markers from a single blood sample possess modest predictive utility in differentiating LNSP from CAO pregnancies upon first presentation, which is improved by variable selection and combination using machine learning. A diagnostic test to confirm LNSP and thus exclude pregnancies affecting maternal morbidity and potentially life-threatening outcomes would be invaluable in emergency situations.

Comparative metabolite analysis of Piper sarmentosum organs approached by LC-MS-based metabolic profiling

Piper sarmentosum Roxb. (Piperaceae) is a traditional medicinal and food plant widely distributed in the tropical and subtropical regions of Asia, offering both health and culinary benefits. In this study the secondary metabolites in different organs of P. sarmentosum were identified and their relative abundances were characterized. The metabolic profiles of leaves, roots, stems and fruits were comprehensively investigated by liquid chromatography high-resolution mass spectrometry (LC-HR-MS) and the data subsequently analyzed using multivariate statistical methods. Manual interpretation of the tandem mass spectrometric (MS/MS) fragmentation patterns revealed the presence of 154 tentatively identified metabolites, mostly represented by alkaloids and flavonoids. Principle component analysis and hierarchical clustering indicated the predominant occurrence of flavonoids, lignans and phenyl propanoids in leaves, aporphines in stems, piperamides in fruits and lignan-amides in roots. Overall, this study provides extensive data on the metabolite composition of P. sarmentosum, supplying useful information for bioactive compounds discovery and patterns of their preferential biosynthesis or storage in specific organs. This can be used to optimize production and harvesting as well as to maximize the plant's economic value as herbal medicine or in food applications.

Molecular Mechanism of Exogenous ABA to Enhance UV-B Resistance in Rhododendron chrysanthum Pall. by Modulating Flavonoid Accumulation

With the depletion of the ozone layer, the intensity of ultraviolet B (UV-B) radiation reaching the Earth's surface increases, which in turn causes significant stress to plants and affects all aspects of plant growth and development. The aim of this study was to investigate the mechanism of response to UV-B radiation in the endemic species of Rhododendron chrysanthum Pall. (R. chrysanthum) in the Changbai Mountains and to study how exogenous ABA regulates the response of R. chrysanthum to UV-B stress. The results of chlorophyll fluorescence images and OJIP kinetic curves showed that UV-B radiation damaged the PSII photosystem of R. chrysanthum, and exogenous ABA could alleviate this damage to some extent. A total of 2148 metabolites were detected by metabolomics, of which flavonoids accounted for the highest number (487, or 22.67%). KEGG enrichment analysis of flavonoids that showed differential accumulation by UV-B radiation and exogenous ABA revealed that flavonoid biosynthesis and flavone and flavonol biosynthesis were significantly altered. GO analysis showed that most of the DEGs produced after UV-B radiation and exogenous ABA were distributed in the cellular process, cellular anatomical entity, and catalytic activity. Network analysis of key DFs and DEGs associated with flavonoid synthesis identified key flavonoids (isorhamnetin-3-O-gallate and dihydromyricetin) and genes (TRINITY_DN2213_c0_g1_i4-A1) that promote the resistance of R. chrysanthum to UV-B stress. In addition, multiple transcription factor families were found to be involved in the regulation of the flavonoid synthesis pathway under UV-B stress. Overall, R. chrysanthum actively responded to UV-B stress by regulating changes in flavonoids, especially flavones and flavonols, while exogenous ABA further enhanced its resistance to UV-B stress. The experimental results not only provide a new perspective for understanding the molecular mechanism of the response to UV-B stress in the R. chrysanthum, but also provide a valuable theoretical basis for future research and application in improving plant adversity tolerance.

Production of Polyphenolic Natural Products by Bract-Derived Tissue Cultures of Three Medicinal Tilia spp.: A Comparative Untargeted Metabolomics Study

Medicinal plant tissue cultures are potential sources of bioactive compounds. In this study, we report the chemical characterization of the callus cultures of three medicinal Tilia spp. (Tilia cordata, Tilia vulgaris and Tilia tomentosa), along with the comparison to bracts and flowers of the same species. Our aim was to show that calli of Tilia spp. are good alternatives to the calli of T. americana for the production of polyphenols and are better sources of a subset of polyphenolic metabolites, compared to the original organs. Calli were initiated from young bracts and grown on woody plant medium containing 1 mg L-1 2,4-D and 0.1 mg L-1 BAP. For chemical characterization, a quality-controlled untargeted metabolomics approach and the quantification of several bioactive compounds was performed with the use of LC-ESI-MS/MS. While bracts and flowers contained flavonoid glycosides (astragalin, isoquercitrin) as major polyphenols, calli of all species contained catechins, coumarins (fraxin, esculin and scopoletin) and flavane aglyca. T. tomentosa calli contained 5397 µg g DW-1 catechin, 201 µg g DW-1 esculin, 218 µg g DW-1 taxifolin and 273 µg g DW-1 eriodictyol, while calli from other species contained lower amounts. T. cordata and T. tomentosa flowers were rich in isoquercitrin, containing 8134 and 6385 µg g DW-1, respectively. The currently tested species contained many of the bioactive metabolites described from T. americana. The production of catechin was shown to be comparable to the most efficient tissue cultures reported. Flowers and bracts contained flavonoid glycosides, including tiliroside, resembling bioactive fractions of T. americana. In addition, untargeted metabolomics has shown fingerprint-like differences among species, highlighting possible chemotaxonomic and quality control applications, especially for bracts.

Precision Cardio-oncology: Update on Omics-Based Diagnostic Methods

OPINION STATEMENT

Cardio-oncology is an emerging interdisciplinary field dedicated to the early detection and treatment of adverse cardiovascular events associated with anticancer treatment, and current clinical management of anticancer-treatment-related cardiovascular toxicity (CTR-CVT) remains limited by a lack of detailed phenotypic data. However, the promise of diagnosing CTR-CVT using deep phenotyping has emerged with the development of precision medicine, particularly the use of omics-based methodologies to discover sensitive biomarkers of the disease. In the future, combining information produced by a variety of omics methodologies could expand the clinical practice of cardio-oncology. In this review, we demonstrate how omics approaches can improve our comprehension of CTR-CVT deep phenotyping, discuss the positive and negative aspects of available omics approaches for CTR-CVT diagnosis, and outline how to integrate multiple sets of omics data into individualized monitoring and treatment. This will offer a reliable technical route for lowering cardiovascular morbidity and mortality in cancer patients and survivors.

Spatial metabolomics and its application in the liver

Hepatocytes work in highly structured, repetitive hepatic lobules. Blood flow across the radial axis of the lobule generates oxygen, nutrient, and hormone gradients, which result in zoned spatial variability and functional diversity. This large heterogeneity suggests that hepatocytes in different lobule zones may have distinct gene expression profiles, metabolic features, regenerative capacity, and susceptibility to damage. Here, we describe the principles of liver zonation, introduce metabolomic approaches to study the spatial heterogeneity of the liver, and highlight the possibility of exploring the spatial metabolic profile, leading to a deeper understanding of the tissue metabolic organization. Spatial metabolomics can also reveal intercellular heterogeneity and its contribution to liver disease. These approaches facilitate the global characterization of liver metabolic function with high spatial resolution along physiological and pathological time scales. This review summarizes the state of the art for spatially resolved metabolomic analysis and the challenges that hinder the achievement of metabolome coverage at the single-cell level. We also discuss several major contributions to the understanding of liver spatial metabolism and conclude with our opinion on the future developments and applications of these exciting new technologies.

Development of Nontargeted Workflow of Occupational Exposure by Infrared Ion Spectroscopy and Silicone Wristbands' Passive Sampling

A new approach using orthogonal analytical techniques is developed for chemical identification. High resolution mass spectrometry and infrared ion spectroscopy are applied through a 5-level confidence paradigm to demonstrate the effectiveness of nontargeted workflow for the identification of hazardous organophosphates. Triphenyl phosphate is used as a surrogate organophosphate for occupational exposure, and silicone wristbands are used to represent personal samplers. Spectral data of a target compound is combined with spectral data of the sodium adduct and quantum chemical calculations to achieve a confirmed identification. Here, we demonstrate a nontargeted workflow that identifies organophosphate exposure and provides a mechanism for selecting validated methods for quantitative analyses.

Analysis of the mechanism of curcumin against osteoarthritis using metabolomics and transcriptomics

Curcumin, a polyphenolic compound derived from the turmeric plant (Curcuma longa), has been extensively studied for its anti-inflammatory and anti-proliferative properties. The safety and efficacy of curcumin have been thoroughly validated. Nevertheless, the underlying mechanism for treating osteoarthritis remains ambiguous. This study aims to reveal the potential mechanism of curcumin in treating osteoarthritis by using metabolomics and transcriptomics. Firstly, we validated the effect of curcumin on inflammatory factors in human articular chondrocytes. Secondly, we explored the cellular metabolism mechanism of curcumin against osteoarthritis using cell metabolomics. Thirdly, we assessed the differences in gene expression of human articular chondrocytes through transcriptomics. Lastly, to evaluate the essential targets and elucidate the potential mechanism underlying the therapeutic effects of curcumin in osteoarthritis, we conducted a screening of the proteins within the shared pathway of metabolomics and transcriptomics. Our results demonstrated that curcumin significantly decreased the levels of inflammatory markers, such as IL-β, IL-6, and TNF-α, in human articular chondrocytes. Cell metabolomics identified 106 differential metabolites, including beta-aminopropionitrile, 3-amino-2-piperidone, pyrrole-2-carboxaldehyde, and various other components. The transcriptomic analysis yielded 1050 differential mRNAs. Enrichment analysis showed that the differential metabolites and mRNAs were significantly enriched in seven pathways, including glycine, serine, and threonine metabolism; pentose and glucuronate interconversions; glycerolipid metabolism; histidine metabolism; mucin-type o-glycan biosynthesis; inositol phosphate metabolism; and cysteine and methionine metabolism. A total of 23 key targets were identified to be involved in these pathways. We speculate that curcumin may alleviate osteoarthritis by targeting key proteins involved in glycine, serine, and threonine metabolism; inhibiting pyruvate production; and modulating glycolysis.

SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Computational methods for processing and interpreting mass spectrometry-based metabolomics

Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

The role of insect gut microbiota in host fitness, detoxification and nutrient supplementation

Insects are incredibly diverse, ubiquitous and have successfully flourished out of the dynamic and often unpredictable nature of evolutionary processes. The resident microbiome has accompanied the physical and biological adaptations that enable their continued survival and proliferation in a wide array of environments. The host insect and microbiome's bidirectional relationship exhibits their capability to influence each other's physiology, behavior and characteristics. Insects are reported to rely directly on the microbial community to break down complex food, adapt to nutrient-deficit environments, protect themselves from natural adversaries and control the expression of social behavior. High-throughput metagenomic approaches have enhanced the potential for determining the abundance, composition, diversity and functional activities of microbial fauna associated with insect hosts, enabling in-depth investigation into insect-microbe interactions. We undertook a review of some of the major advances in the field of metagenomics, focusing on insect-microbe interaction, diversity and composition of resident microbiota, the functional capability of endosymbionts and discussions on different symbiotic relationships. The review aims to be a valuable resource on insect gut symbiotic microbiota by providing a comprehensive understanding of how insect gut symbionts systematically perform a range of functions, viz., insecticide degradation, nutritional support and immune fitness. A thorough understanding of manipulating specific gut symbionts may aid in developing advanced insect-associated research to attain health and design strategies for pest management.

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

Integrated Omics Analysis Reveals Key Pathways in Cotton Defense against Mirid Bug (Adelphocoris suturalis Jakovlev) Feeding

The recent dominance of Adelphocoris suturalis Jakovlev as the primary cotton field pest in Bt-cotton-cultivated areas has generated significant interest in cotton pest control research. This study addresses the limited understanding of cotton defense mechanisms triggered by A. suturalis feeding. Utilizing LC-QTOF-MS, we analyzed cotton metabolomic changes induced by A. suturalis, and identified 496 differential positive ions (374 upregulated, 122 downregulated) across 11 categories, such as terpenoids, alkaloids, phenylpropanoids, flavonoids, isoflavones, etc. Subsequent iTRAQ-LC-MS/MS analysis of the cotton proteome revealed 1569 differential proteins enriched in 35 metabolic pathways. Integrated metabolome and proteome analysis highlighted significant upregulation of 17 (89%) proteases in the α-linolenic acid (ALA) metabolism pathway, concomitant with a significant increase in 14 (88%) associated metabolites. Conversely, 19 (73%) proteases in the fructose and mannose biosynthesis pathway were downregulated, with 7 (27%) upregulated proteases corresponding to the downregulation of 8 pathway-associated metabolites. Expression analysis of key regulators in the ALA pathway, including allene oxidase synthase (AOS), phospholipase A (PLA), allene oxidative cyclase (AOC), and 12-oxophytodienoate reductase3 (OPR3), demonstrated significant responses to A. suturalis feeding. Finally, this study pioneers the exploration of molecular mechanisms in the plant-insect relationship, thereby offering insights into potential novel control strategies against this cotton pest.

Challenges in the Metabolomics-Based Biomarker Validation Pipeline

As end-products of the intersection between the genome and environmental influences, metabolites represent a promising approach to the discovery of novel biomarkers for diseases. However, many potential biomarker candidates identified by metabolomics studies fail to progress beyond analytical validation for routine implementation in clinics. Awareness of the challenges present can facilitate the development and advancement of innovative strategies that allow improved and more efficient applications of metabolite-based markers in clinical settings. This minireview provides a comprehensive summary of the pre-analytical factors, required analytical validation studies, and kit development challenges that must be resolved before the successful translation of novel metabolite biomarkers originating from research. We discuss the necessity for strict protocols for sample collection, storage, and the regulatory requirements to be fulfilled for a bioanalytical method to be considered as analytically validated. We focus especially on the blood as a biological matrix and liquid chromatography coupled with tandem mass spectrometry as the analytical platform for biomarker validation. Furthermore, we examine the challenges of developing a commercially viable metabolomics kit for distribution. To bridge the gap between the research lab and clinical implementation and utility of relevant metabolites, the understanding of the translational challenges for a biomarker panel is crucial for more efficient development of metabolomics-based precision medicine.

Cyclocodon lancifolius fruit prolongs the lifespan of Caenorhabditis elegans via antioxidation and regulation of purine metabolism

Cyclocodon lancifolius fruit is a promising commercial fruit with antioxidant activity and is rich in polyphenolic compounds. In this study, the anti-aging activity of C. lancifolius fruit extract (CF) on Caenorhabditis elegans (C. elegans) was evaluated by observing the longevity, stress response, reproduction, oscillation, lipofuscin, and antioxidant enzymes of worms. Moreover, the effects and potential mechanisms of CF on delaying C. elegans senescence at the mRNA and metabolite levels were investigated. The results showed that CF treatment significantly increased the lifespan and stress resistance, decreased the levels of lipofuscin and reactive oxygen species (ROS), and improved the antioxidant system of C. elegans. The extension of the lifespan of C. elegans was remarkably correlated with the upregulation of mtl-1 and Hsp-16.2, along with the downregulation of age-1, daf-2, and akt-1. Metabolomics analysis revealed that purine metabolism is a key regulatory pathway for CF to exert anti-aging effects. The present study suggests that C. lancifolius fruit has potential for use as a functional food to enhance antioxidant capacity and delay aging.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Metabolomic profiling of deep vein thrombosis

Deep vein thrombosis (DVT) of the lower extremities is one of the most common peripheral vascular diseases, with significant complications and sequelae. Metabolomics aims to identify small molecules in biological samples. It can serve as a promising method for screening compounds that can be used for early disease detection, diagnosis, treatment response prediction, and prognosis. In addition, high-throughput metabolomics screening can yield significant insights into the pathophysiological pathways of DVT. Currently, the metabolomic profiles of DVT have yielded inconsistent expression patterns. This article examines the recent advancements in metabolomic studies of DVT and analyzes the factors that may influence the results.

Resource competition predicts assembly of gut bacterial communities in vitro

Microbial community dynamics arise through interspecies interactions, including resource competition, cross-feeding and pH modulation. The individual contributions of these mechanisms to community structure are challenging to untangle. Here we develop a framework to estimate multispecies niche overlaps by combining metabolomics data of individual species, growth measurements in spent media and mathematical models. We applied our framework to an in vitro model system comprising 15 human gut commensals in complex media and showed that a simple model of resource competition accounted for most pairwise interactions. Next, we built a coarse-grained consumer-resource model by grouping metabolomic features depleted by the same set of species and showed that this model predicted the composition of 2-member to 15-member communities with reasonable accuracy. Furthermore, we found that incorporation of cross-feeding and pH-mediated interactions improved model predictions of species coexistence. Our theoretical model and experimental framework can be applied to characterize interspecies interactions in bacterial communities in vitro.

Essentials in the acquisition, interpretation, and reporting of plant metabolite profiles

Plant metabolite profiling reveals the diversity of secondary or specialized metabolites in the plant kingdom with its hundreds of thousands of species. Specialized plant metabolites constitute a vast class of chemicals posing significant challenges in analytical chemistry. In order to be of maximum scientific relevance, reports dealing with these compounds and their source species must be transparent, make use of standards and reference materials, and be based on correctly and traceably identified plant material. Essential aspects in qualitative plant metabolite profiling include: (i) critical review of previous literature and a reasoned sampling strategy; (ii) transparent plant sampling with wild material documented by vouchers in public herbaria and, optimally, seed banks; (iii) if possible, inclusion of generally available reference plant material; (iv) transparent, documented state-of-the art chemical analysis, ideally including chemical reference standards; (v) testing for artefacts during preparative extraction and isolation, using gentle analytical methods; (vi) careful chemical data interpretation, avoiding over- and misinterpretation and taking into account phytochemical complexity when assigning identification confidence levels, and (vii) taking all previous scientific knowledge into account in reporting the scientific data. From the current stage of the phytochemical literature, selected comments and suggestions are given. In the past, proposed revisions of botanical taxonomy were sometimes based on metabolite profiles, but this approach ("chemosystematics" or "chemotaxonomy") is outdated due to the advent of DNA sequence-based phylogenies. In contrast, systematic comparisons of plant metabolite profiles in a known phylogenetic framework remain relevant. This approach, known as chemophenetics, allows characterizing species and clades based on their array of specialized metabolites, aids in deducing the evolution of biosynthetic pathways and coevolution, and can serve in identifying new sources of rare and economically interesting natural products.

Etiologic Evaluation of Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is clinically and etiologically heterogeneous. A causal genetic variant can be identified in approximately 20% to 25% of affected individuals with current clinical genetic testing, and all patients with an ASD diagnosis should be offered genetic etiologic evaluation. We suggest that exome sequencing with copy number variant coverage should be the first-line etiologic evaluation for ASD. Neuroimaging, neurophysiologic, metabolic, and other biochemical evaluations can provide insight into the pathophysiology of ASD but should be recommended in the appropriate clinical circumstances.

Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment

The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.

Integrated omics analysis reveals a correlation between gut microbiota and egg production in captive African penguins (Spheniscus demersus)

The egg production of captive African penguins differs considerably between individuals. An understanding of the physiological differences in African penguins with relatively greater and lesser egg production is meaningful for the captive breeding program of this endangered species. The objective of this study was to investigate differential microbial composition and metabolites in captive African penguins with different egg production. Fecal samples were collected from captive female African penguins during the breeding season. The results of 16 S rRNA gene sequencing showed that African penguins with different egg production had similar microbial diversities, whereas a significant difference was observed between their microbial community structure. African penguins with relatively greater egg production exhibited a higher relative abundance of Alphaproteobacteria, Rhizobiales, Bradyrhizobiaceae, Bradyrhizobium and Bosea. Meanwhile, penguins with relatively lesser egg production had an increased proportion of Klebsiella and Plesiomonas. We further identified a total of 1858 metabolites in female African penguins by liquid chromatography-mass spectrometry analysis. Among these metabolites, 13 kinds of metabolites were found to be significantly differential between African penguins with different egg production. In addition, the correlation analysis revealed that the egg production had significant correlations with most of the differential microbial bacteria and metabolites. Our findings might aid in understanding the potential mechanism underlying the phenomenon of abnormal egg production in captive African penguins, and provide novel insights into the relationship between gut microbiota and reproduction in penguins.

Metabolomics-guided utilization of beneficial microbes for climate-resilient crops

In the rhizosphere, plants and microbes communicate chemically, especially under environmental stress. Over millions of years, plants and their microbiome have coevolved, sharing various chemicals, including signaling molecules. This mutual exchange impacts bacterial communication and influences plant metabolism. Inter-kingdom signal crosstalk affects bacterial colonization and plant fitness. Beneficial microbes and their metabolomes offer eco-friendly ways to enhance plant resilience and agriculture. Plant metabolites are pivotal in this dynamic interaction between host plants and their interacting beneficial microbes. Understanding these associations is key to engineering a robust microbiome for stress mitigation and improved plant growth. This review explores mechanisms behind plant-microbe interactions, the role of beneficial microbes and metabolomics, and the practical applications for addressing climate change's impact on agriculture. Integrating beneficial microbes' activities and metabolomics' application to study metabolome-driven interaction between host plants and their corresponding beneficial microbes holds promise for enhancing crop resilience and productivity.

Organic acid and sugar components accumulation and flavor associated metabolites dynamic changes in yellow- and white-fleshed seedless loquats (Eriobotrya japonica)

Triploid loquats are divided into yellow- and white-fleshed cultivars. To better understand taste variations in triploid loquat fruits, we used a UPLC-ESI-QTRAP-MS/MS-based widely targeted metabolomic analysis to examine the metabolic composition of two different color fleshed triploid loquats with a sample size of 54 and external validation method within a confidence level of P＜0.05. We identified key flavor-related differentially accumulated metabolites using the variable importance in projection (VIP) value (VIP ≥ 1.0) and fold change ≥ 2 or ≤ 0.5. Furthermore, the results of the HPLC analysis showed that white-fleshed loquats had a low malic acid content. We also performed the UPLC-MS/MS system to investigate the carotenoids contents and lipidome in four triploid cultivars. In the fruits of white-fleshed varieties, the carotenoids contents were significantly downregulated, but the contents of most glycerolphospholipids were increased. Our results reveal the metabolomic changes between the yellow- and white-fleshed cultivars.

Co-occurrence network analysis reveals the alterations of the skin microbiome and metabolome in adults with mild to moderate atopic dermatitis

Skin microbiome can be altered in patients with atopic dermatitis (AD). An understanding of the changes from healthy to atopic skin can help develop new targets for treatment by identifying microbial and molecular biomarkers. This study investigates the skin microbiome and metabolome of healthy adult subjects and lesion (ADL) and non-lesion (ADNL) of AD patients by 16S rRNA gene sequencing and mass spectrometry, respectively. Samples from AD patients showed alterations in the diversity and composition of the skin microbiome, with ADL skin having the greatest divergence. Staphylococcus species, especially S. aureus, were significantly increased in AD patients. Metabolomic profiles were also different between the groups. Dipeptide derivatives are more abundant in ADL, which may be related to skin inflammation. Co-occurrence network analysis of the microbiome and metabolomics data revealed higher co-occurrence of metabolites and bacteria in healthy ADNL compared to ADL. S. aureus co-occurred with dipeptide derivatives in ADL, while phytosphingosine-derived compounds showed co-occurrences with commensal bacteria, for example, Paracoccus sp., Pseudomonas sp., Prevotella bivia, Lactobacillus iners, Anaerococcus sp., Micrococcus sp., Corynebacterium ureicelerivorans, Corynebacterium massiliense, Streptococcus thermophilus, and Roseomonas mucosa, in healthy and ADNL groups. Therefore, these findings provide valuable insights into how AD affects the human skin metabolome and microbiome.IMPORTANCEThis study provides valuable insight into changes in the skin microbiome and associated metabolomic profiles in an adult population with mild to moderate atopic dermatitis. It also identifies new therapeutic targets that may be useful for developing personalized treatments for individuals with atopic dermatitis based on their unique skin microbiome and metabolic profiles.

Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum

The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.

Imaging plant metabolism in situ

Mass spectrometry imaging (MSI) has emerged as an invaluable analytical technique for investigating the spatial distribution of molecules within biological systems. In the realm of plant science, MSI is increasingly employed to explore metabolic processes across a wide array of plant tissues, including those in leaves, fruits, stems, roots, and seeds, spanning various plant systems such as model species, staple and energy crops, and medicinal plants. By generating spatial maps of metabolites, MSI has elucidated the distribution patterns of diverse metabolites and phytochemicals, encompassing lipids, carbohydrates, amino acids, organic acids, phenolics, terpenes, alkaloids, vitamins, pigments, and others, thereby providing insights into their metabolic pathways and functional roles. In this review, we present recent MSI studies that demonstrate the advances made in visualizing the plant spatial metabolome. Moreover, we emphasize the technical progress that enhances the identification and interpretation of spatial metabolite maps. Within a mere decade since the inception of plant MSI studies, this robust technology is poised to continue as a vital tool for tackling complex challenges in plant metabolism.

Metabolomics to understand metabolic regulation underpinning fruit ripening, development, and quality

Classically fruit ripening and development was studied using genetic approaches, with understanding of metabolic changes that occurred in concert largely focused on a handful of metabolites including sugars, organic acids, cell wall components, and phytohormones. The advent and widespread application of metabolomics has, however, led to far greater understanding of metabolic components that play a crucial role not only in this process but also in influencing the organoleptic and nutritive properties of the fruits. Here we review how the study of natural variation, mutants, transgenics, and gene-edited fruits has led to a considerable increase in our understanding of these aspects. We focus on fleshy fruits such as tomato but also review berries, receptacle fruits, and stone-bearing fruits. Finally, we offer a perspective as to how comparative analyses and machine learning will likely further improve our comprehension of the functional importance of various metabolites in the future.

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.

Metabolites: a converging node of host and microbe to explain meta-organism

Meta-organisms encompassing the host and resident microbiota play a significant role in combatting diseases and responding to stress. Hence, there is growing traction to build a knowledge base about this ecosystem, particularly to characterize the bidirectional relationship between the host and microbiota. In this context, metabolomics has emerged as the major converging node of this entire ecosystem. Systematic comprehension of this resourceful omics component can elucidate the organism-specific response trajectory and the communication grid across the ecosystem embodying meta-organisms. Translating this knowledge into designing nutraceuticals and next-generation therapy are ongoing. Its major hindrance is a significant knowledge gap about the underlying mechanisms maintaining a delicate balance within this ecosystem. To bridge this knowledge gap, a holistic picture of the available information has been presented with a primary focus on the microbiota-metabolite relationship dynamics. The central theme of this article is the gut-brain axis and the participating microbial metabolites that impact cerebral functions.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

Metabolomics-driven investigation of plant defense response against pest and pathogen attack

The advancement of metabolomics has assisted in the identification of various bewildering characteristics of the biological system. Metabolomics is a standard approach, facilitating crucial aspects of system biology with absolute quantification of metabolites using minimum samples, based on liquid/gas chromatography, mass spectrometry and nuclear magnetic resonance. The metabolome profiling has narrowed the wide gaps of missing information and has enhanced the understanding of a wide spectrum of plant-environment interactions by highlighting the complex pathways regulating biochemical reactions and cellular physiology under a particular set of conditions. This high throughput technique also plays a prominent role in combined analyses of plant metabolomics and other omics datasets. Plant metabolomics has opened a wide paradigm of opportunities for developing stress-tolerant plants, ensuring better food quality and quantity. However, despite advantageous methods and databases, the technique has a few limitations, such as ineffective 3D capturing of metabolites, low comprehensiveness, and lack of cell-based sampling. In the future, an expansion of plant-pathogen and plant-pest response towards the metabolite architecture is necessary to understand the intricacies of plant defence against invaders, elucidation of metabolic pathway operational during defence and developing a direct correlation between metabolites and biotic stresses. Our aim is to provide an overview of metabolomics and its utilities for the identification of biomarkers or key metabolites associated with biotic stress, devising improved diagnostic methods to efficiently assess pest and pathogen attack and generating improved crop varieties with the help of combined application of analytical and molecular tools.

Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics

Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.

Integrative multi-omics analysis reveals the crucial biological pathways involved in the adaptive response to NaCl stress in peanut seedlings

Plant growth is restricted by salt stress, which is a significant abiotic factor, particularly during the seedling stage. The aim of this study was to investigate the mechanisms underlying peanut adaptation to salt stress by transcriptomic and metabolomic analysis during the seedling stage. In this study, phenotypic variations of FH23 and NH5, two peanut varieties with contrasting tolerance to salt, changed obviously, with the strongest differences observed at 24 h. FH23 leaves wilted and the membrane system was seriously damaged. A total of 1470 metabolites were identified, with flavonoids being the most common (21.22%). Multi-omics analyses demonstrated that flavonoid biosynthesis (ko00941), isoflavones biosynthesis (ko00943), and plant hormone signal transduction (ko04075) were key metabolic pathways. The comparison of metabolites in isoflavone biosynthesis pathways of peanut varieties with different salt tolerant levels demonstrated that the accumulation of naringenin and formononetin may be the key metabolite leading to their different tolerance. Using our transcriptomic data, we identified three possible reasons for the difference in salt tolerance between the two varieties: (1) differential expression of LOC112715558 (HIDH) and LOC112709716 (HCT), (2) differential expression of LOC112719763 (PYR/PYL) and LOC112764051 (ABF) in the abscisic acid (ABA) signal transduction pathway, then (3) differential expression of genes encoding JAZ proteins (LOC112696383 and LOC112790545). Key metabolites and candidate genes related to improving the salt tolerance in peanuts were screened to promote the study of the responses of peanuts to NaCl stress and guide their genetic improvement.

Mutation in Arabidopsis mitochondrial Pentatricopeptide repeat 40 gene affects tolerance to water deficit

MAIN CONCLUSION

The Arabidopsis Pentatricopeptide repeat 40 (PPR40) insertion mutants have increased tolerance to water deficit compared to wild-type plants. Tolerance is likely the consequence of ABA hypersensitivity of the mutants. Plant growth and development depend on multiple environmental factors whose alterations can disrupt plant homeostasis and trigger complex molecular and physiological responses. Water deficit is one of the factors which can seriously restrict plant growth and viability. Mitochondria play an important role in cellular metabolism, energy production, and redox homeostasis. During drought and salinity stress, mitochondrial dysfunction can lead to ROS overproduction and oxidative stress, affecting plant growth and survival. Alternative oxidases (AOXs) and stabilization of mitochondrial electron transport chain help mitigate ROS damage. The mitochondrial Pentatricopeptide repeat 40 (PPR40) protein was implicated in stress regulation as ppr40 mutants were found to be hypersensitive to ABA and high salinity during germination. This study investigated the tolerance of the knockout ppr40-1 and knockdown ppr40-2 mutants to water deprivation. Our results show that these mutants display an enhanced tolerance to water deficit. The mutants had higher relative water content, reduced level of oxidative damage, and better photosynthetic parameters in water-limited conditions compared to wild-type plants. ppr40 mutants had considerable differences in metabolic profiles and expression of a number of stress-related genes, suggesting important metabolic reprogramming. Tolerance to water deficit was also manifested in higher survival rates and alleviated growth reduction when watering was suspended. Enhanced sensitivity to ABA and fast stomata closure was suggested to lead to improved capacity for water conservation in such environment. Overall, this study highlights the importance of mitochondrial functions and in particular PPR40 in plant responses to abiotic stress, particularly drought.

An assessment of AcquireX and Compound Discoverer software 3.3 for non-targeted metabolomics

We used the Exploris 240 mass spectrometer for non-targeted metabolomics on Saccharomyces cerevisiae strain BY4741 and tested AcquireX software for increasing the number of detectable compounds and Compound Discoverer 3.3 software for identifying compounds by MS2 spectral library matching. AcquireX increased the number of potentially identifiable compounds by 50% through six iterations of MS2 acquisition. On the basis of high-scoring MS2 matches made by Compound Discoverer, there were 483 compounds putatively identified from nearly 8000 candidate spectra. Comparisons to 20 amino acid standards, however, revealed instances whereby compound matches could be incorrect despite strong scores. Situations included the candidate with the top score not being the correct compound, matching the same compound at two different chromatographic peaks, assigning the highest score to a library compound much heavier than the mass for the parent ion, and grouping MS2 isomers to a single parent ion. Because the software does not calculate false positive and false discovery rates at these multiple levels where such errors can propagate, we conclude that manual examination of findings will be required post software analysis. These results will interest scientists who may use this platform for metabolomics research in diverse disciplines including medical science, environmental science, and agriculture.

Chemical-tag-based semi-annotated metabolomics facilitates gene identification and specialized metabolic pathway elucidation in wheat

The importance of metabolite modification and species-specific metabolic pathways has long been recognized. However, linking the chemical structure of metabolites to gene function in order to explore the genetic and biochemical basis of metabolism has not yet been reported in wheat (Triticum aestivum). Here, we profiled metabolic fragment enrichment in wheat leaves and consequently applied chemical-tag-based semi-annotated metabolomics in a genome-wide association study in accessions of wheat. The studies revealed that all 1,483 quantified metabolites have at least one known functional group whose modification is tailored in an enzyme-catalyzed manner and eventually allows efficient candidate gene mining. A Triticeae crop-specific flavonoid pathway and its underlying metabolic gene cluster were elucidated in further functional studies. Additionally, upon overexpressing the major effect gene of the cluster TraesCS2B01G460000 (TaOMT24), the pathway was reconstructed in rice (Oryza sativa), which lacks this pathway. The reported workflow represents an efficient and unbiased approach for gene mining using forward genetics in hexaploid wheat. The resultant candidate gene list contains vast molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets and will ultimately aid in achieving wheat crop improvement.

Comparison of Amine-Modified Polymeric Stationary Phases for Polar Metabolomic Analysis Based on Unified-Hydrophilic Interaction/Anion Exchange Liquid Chromatography/High-Resolution Mass Spectrometry (Unified-HILIC/AEX/HRMS)

In metabolomic analysis, one of the most commonly used techniques to support the detection sensitivity and quantitation of mass spectrometry is combining it with liquid chromatography. Recently, we developed a method that enables comprehensive single-run measurement of hydrophilic metabolites using unified-hydrophilic interaction/anion exchange liquid chromatography/high-resolution mass spectrometry (unified-HILIC/AEX/HRMS) with a polymer-based mixed amines column (Gelpack GL-HilicAex). However, the importance of stationary phase functional groups and mobile phase conditions for the separation mechanisms and sensitive detection in unified-HILIC/AEX/HRMS is not yet fully understood. This study aimed to understand the importance of the mobile and stationary phases in unified-HILIC/AEX/HRMS. Two different alkali-resistant polymer-based amines-modified columns (Gelpack GL-HilicAex, primary, secondary, tertiary, and quaternary amine-modified polyglycerol dimethacrylate gel; Asahipak NH2P-50 2D, secondary amine-modified polyvinyl alcohol gel) and two eluents (acetonitrile and ammonium bicarbonate solution, pH 9.8) were used for comparative validation. A comparison of mobile phase conditions using both columns confirmed that the two-step separation from HILIC to AEX characteristic of unified-HILIC/AEX requires a linear gradient condition from acetonitrile to nearly 50% water and AEX with up to 40 mM bicarbonate ions. We found that when alkali-resistant hydrophilic polymer packing materials are modified with amines, unified-HILIC/AEX separation can be reproduced if at least one secondary amine associated with the amine series is present in the stationary phase. Furthermore, the difference in sensitivity in the HILIC and AEX modes owing to the different columns indicates the need for further improvements in the mobile phase composition and stationary phase.

Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.

SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism

The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.

Optimized combination of MALDI MSI and immunofluorescence for neuroimaging of lipids within cellular microenvironments

Proper neurological function relies on the cellular and molecular microenvironment of the brain, with perturbations of this environment leading to neurological disorders. However, studying the microenvironments of neurological tissue has proven difficult because of its inherent complexity. Both the cell type and metabolomic underpinnings of the cell have crucial functional roles, thus making multimodal characterization methods key to acquiring a holistic view of the brain's microenvironment. This study investigates methods for combining matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) and immunofluorescence (IF) microscopy to enable the concurrent investigation of cell types and lipid profiles on the same sample. In brief, 1,5-diaminonaphthalene (DAN), α-cyano-4-hydroxy-cinnamic acid (CHCA), and 2,5-dihydroxybenzoic acid (DHB) were tested in addition to instrument-specific parameters for compatibility with IF. Alternatively, the effects of IF protocols on MALDI MSI were also tested, showing significant signal loss with all tested permutations. Ultimately, the use of CHCA for MALDI MSI resulted in the best IF images, while the use of DAN gave the lowest quality IF images. Overall, increasing the laser power and number of shots per laser burst resulted in the most tissue ablation. However, optimized parameter settings allowed for minimal tissue ablation while maintaining sufficient MALDI MSI signal.

Weighting Low-Intensity MS/MS Ions and m/z Frequency for Spectral Library Annotation

Calculating spectral similarity is a fundamental step in MS/MS data analysis in untargeted metabolomics experiments, as it facilitates the identification of related spectra and the annotation of compounds. To improve matching accuracy when querying an experimental mass spectrum against a spectral library, previous approaches have proposed increasing peak intensities for high m/z ranges. These high m/z values tend to be smaller in magnitude, yet they offer more crucial information for identifying the chemical structure. Here, we evaluate the impact of using these weights for identifying structurally related compounds and mass spectral library searches. Additionally, we propose a weighting approach that (i) takes into account the frequency of the m/z values within a spectral library in order to assign higher importance to the most common peaks and (ii) increases the intensity of lower peaks, similar to previous approaches. To demonstrate our approach, we applied weighting preprocessing to modified cosine, entropy, and fidelity distance metrics and benchmarked it against previously reported weights. Our results demonstrate how weighting-based preprocessing can assist in annotating the structure of unknown spectra as well as identifying structurally similar compounds. Finally, we examined scenarios in which the utilization of weights resulted in diminished performance, pinpointing spectral features where the application of weights might be detrimental.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Plastid ancestors lacked a complete Entner-Doudoroff pathway, limiting plants to glycolysis and the pentose phosphate pathway

The Entner-Doudoroff (ED) pathway provides an alternative to glycolysis. It converts 6-phosphogluconate (6-PG) to glyceraldehyde-3-phosphate and pyruvate in two steps consisting of a dehydratase (EDD) and an aldolase (EDA). Here, we investigate its distribution and significance in higher plants and determine the ED pathway is restricted to prokaryotes due to the absence of EDD genes in eukaryotes. EDDs share a common origin with dihydroxy-acid dehydratases (DHADs) of the branched chain amino acid pathway (BCAA). Each dehydratase features strict substrate specificity. E. coli EDD dehydrates 6-PG to 2-keto-3-deoxy-6-phosphogluconate, while DHAD only dehydrates substrates from the BCAA pathway. Structural modeling identifies two divergent domains which account for their non-overlapping substrate affinities. Coupled enzyme assays confirm only EDD participates in the ED pathway. Plastid ancestors lacked EDD but transferred metabolically promiscuous EDA, which explains the absence of the ED pathway from the Viridiplantae and sporadic persistence of EDA genes across the plant kingdom.