An Overview of Metabolomics Data Analysis: Current Tools and Future Perspectives

Serum metabolic signatures for Alzheimer's Disease reveal alterations in amino acid composition: a validation study

INTRODUCTION

Alzheimer's Disease (AD) is complex and novel approaches are urgently needed to aid in diagnosis. Blood is frequently used as a source for biomarkers; however, its complexity prevents proper detection. The analytical power of metabolomics, coupled with statistical tools, can assist in reducing this complexity.

OBJECTIVES

Thus, we sought to validate a previously proposed panel of metabolic blood-based biomarkers for AD and expand our understanding of the pathological mechanisms involved in AD that are reflected in the blood.

METHODS

In the validation cohort serum and plasma were collected from 25 AD patients and 25 healthy controls. Serum was analysed for metabolites using nuclear magnetic resonance (NMR) spectroscopy, while plasma was tested for markers of neuronal damage and AD hallmark proteins using single molecule array (SIMOA).

RESULTS

The diagnostic performance of the metabolite biomarker panel was confirmed using sparse-partial least squares discriminant analysis (sPLS-DA) with an area under the curve (AUC) of 0.73 (95% confidence interval: 0.59-0.87). Pyruvic acid and valine were consistently reduced in the discovery and validation cohorts. Pathway analysis of significantly altered metabolites in the validation set revealed that they are involved in branched-chain amino acids (BCAAs) and energy metabolism (glycolysis and gluconeogenesis). Additionally, strong positive correlations were observed for valine and isoleucine between cerebrospinal fluid p-tau and t-tau.

CONCLUSIONS

Our proposed panel of metabolites was successfully validated using a combined approach of NMR and sPLS-DA. It was discovered that cognitive-impairment-related metabolites belong to BCAAs and are involved in energy metabolism.

Multi-dimensional metabolomic profiling reveals dysregulated ornithine metabolism hallmarks associated with a severe acute pancreatitis phenotype

To reveal dysregulated metabolism hallmark that was associated with a severe acute pancreatitis (SAP) phenotype. In this study, LC-MS/MS-based targeted metabolomics was used to analyze plasma samples from 106 acute pancreatitis (AP) patients (34 mild, 38 moderate, and 34 severe) admitted within 48 hours from abdominal pain onset and 41 healthy controls. Temporal metabolic profiling was performed on days 1, 3, and 7 after admission. A random forest (RF) was performed to significantly determine metabolite differences between SAP and non-SAP (NSAP) groups. Mass spectrometry imaging (MSI) and immunohistochemistry were conducted for the examination of pancreatic metabolite and metabolic enzyme alterations, respectively, on necrosis and paracancerous tissues. Simultaneously determination of serum and pancreatic tissue metabolic alterations using an L-ornithine-induced AP model to discover metabolic commonalities. Twenty-two significant differential metabolites screened by RF were selected to build an accurate model for the prediction of SAP from NSAP (AUC = 0.955). Six of 22 markers were found by MSI with significant alterations in pancreatic lesions, reduced ornithine-related metabolites were also identified. The abnormally expressed arginase2 and ornithine transcarboxylase were further discovered in combination with time-course metabolic profiling in the SAP animal models, the decreased ornithine catabolites were found at a late stage of inflammation, but ornithine-associated metabolic enzymes were activated during the inflammatory process. The plasma metabolome of AP patients is distinctive, which shows promise for early SAP diagnosis. AP aggravation is linked to the activated ornithine metabolic pathway and its inadequate levels of catabolites in in-situ lesion.

Sex-Specific Perturbation of Systemic Lipidomic Profile in Newborn Lambs Impacted by Prenatal Testosterone Excess

Gestational hyperandrogenism adversely impacts offspring health. Using an ovine model, we found that prenatal testosterone (T) excess adversely affects growth and cardiometabolic outcomes in female offspring and produces sex-specific effects on fetal myocardium. Since lipids are essential to cardiometabolic function, we hypothesized that prenatal T excess leads to sex-specific disruptions in lipid metabolism at birth. Shotgun lipidomics was performed on the plasma samples collected 48 hours after birth from female (F) and male (M) lambs of control (C) and (T) sheep (CF = 4, TF = 7, CM = 5, TM = 10) and data were analyzed by univariate analysis, multivariate dimensionality reduction modeling followed by functional enrichment, and pathway analyses. Biosynthesis of phosphatidylserine was the major pathway responsible for sex differences in controls. Unsupervised and supervised models showed separation between C and T in both sexes with glycerophospholipids and glycerolipids classes being responsible for the sex differences between C and T. T excess increased cholesterol in females while decreasing phosphatidylcholine levels in male lambs. Specifically, T excess: 1) suppressed the phosphatidylethanolamine N-methyltransferase (PEMT) phosphatidylcholine synthesis pathway overall and in TM lambs as opposed to suppression of carnitine levels overall and TF lambs; and 2) activated biosynthesis of ether-linked (O-)phosphatidylethanolamine and O-phosphatidylcholine from O-diacylglycerol overall and in TF lambs. Higher cholesterol levels could underlie adverse cardiometabolic outcomes in TF lambs, whereas suppressed PEMT pathway in TM lambs could lead to endoplasmic reticulum stress and defective lipid transport. These novel findings point to sex-specific effects of prenatal T excess on lipid metabolism in newborn lambs, a precocial ovine model of translational relevance.

Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power. We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as "prior information" into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels. Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients. Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Desorption electrospray ionization and matrix-assisted laser desorption/ionization as imaging approaches for biological samples analysis

The imaging of biological tissues can offer valuable information about the sample composition, which improves the understanding of analyte distribution in such complex samples. Different approaches using mass spectrometry imaging (MSI), also known as imaging mass spectrometry (IMS), enabled the visualization of the distribution of numerous metabolites, drugs, lipids, and glycans in biological samples. The high sensitivity and multiple analyte evaluation/visualization in a single sample provided by MSI methods lead to various advantages and overcome drawbacks of classical microscopy techniques. In this context, the application of MSI methods, such as desorption electrospray ionization-MSI (DESI-MSI) and matrix-assisted laser desorption/ionization-MSI (MALDI-MSI), has significantly contributed to this field. This review discusses the evaluation of exogenous and endogenous molecules in biological samples using DESI and MALDI imaging. It offers rare technical insights not commonly found in the literature (scanning speed and geometric parameters), making it a comprehensive guide for applying these techniques step-by-step. Furthermore, we provide an in-depth discussion of recent research findings on using these methods to study biological tissues.

A metabolomics study: Could plasma metabolites be a guide for the prevention of tamsulosin side effects?

BACKGROUND

The understanding of precision medicine, which aims for high efficacy and low toxicity in treatments, has gained more importance with omics technologies. In this study, it was aimed to reach new suggestions for low-toxicity treatment by clarifying the relationship between tamsulosin side effects and metabolome profiles.

MATERIALS AND METHODS

Plasma samples of control and tamsulosin-treated rats were analyzed by LC-Q-TOF/MS/MS. MS/MS data was processed in XCMS software for the identification of metabolite and metabolic pathway analysis. Data were classified with MATLAB 2019b for multivariate data analysis. 34m/z values were found to be significantly different between the drug and control groups (P≤0.01 and fold analysis≥1.5) and identified by comparing METLIN and HMDB databases.

RESULTS

According to multivariate data analysis, α-Linolenic Acid, Thiamine, Retinoic acid, 1.25-Dihydroxyvitamin D3-26.23-Lactone, L-Glutamine, L-Serine, Retinaldehyde, Sphingosine 1-phosphate, L-Lysine, 23S.25-Dihydroxyvitamin D3, Sphinganine, L-Cysteine, Uridine 5'-diphosphate, Calcidiol, L-Tryptophan, L-Alanine levels changed significantly compared to the control group. Differences in the metabolisms of Retinol, Sphingolipid, Alanine-Aspartate-Glutamate, Glutathione, Fatty Acid, Tryptophan, and biosynthesis of Aminoacyl-tRNA, and Unsaturated Fatty Acid have been successfully demonstrated by metabolic pathway analysis. According to our study, vitamin A and D supplements can be recommended to prevent side effects such as asthenia, rhinitis, nasal congestion, dizziness and IFIS in the treatment of tamsulosin. Alteration of aminoacyl-tRNA biosynthesis and sphingolipid metabolism pathways during tamsulosin treatment is effective in the occurrence of nasal congestion.

CONCLUSIONS

Our study provides important information for tamsulosin therapy with high efficacy and low side effects in precision medicine.

Understanding ayahuasca effects in major depressive disorder treatment through in vitro metabolomics and bioinformatics

Emerging insights from metabolomic-based studies of major depression disorder (MDD) are mainly related to biochemical processes such as energy or oxidative stress, in addition to neurotransmission linked to specific metabolite intermediates. Hub metabolites represent nodes in the biochemical network playing a critical role in integrating the information flow in cells between metabolism and signaling pathways. Limited technical-scientific studies have been conducted to understand the effects of ayahuasca (Aya) administration in the metabolism considering MDD molecular context. Therefore, this work aims to investigate an in vitro primary astrocyte model by untargeted metabolomics of two cellular subfractions: secretome and intracellular content after pre-defined Aya treatments, based on DMT concentration. Mass spectrometry (MS)-based metabolomics data revealed significant hub metabolites, which were used to predict biochemical pathway alterations. Branched-chain amino acid (BCAA) metabolism, and vitamin B6 and B3 metabolism were associated to Aya treatment, as "housekeeping" pathways. Dopamine synthesis was overrepresented in the network results when considering the lowest tested DMT concentration (1 µmol L^-1). Building reaction networks containing significant and differential metabolites, such as nicotinamide, L-DOPA, and L-leucine, is a useful approach to guide on dose decision and pathway selection in further analytical and molecular studies.

Metabolization and sequestration of plant specialized metabolites in insect herbivores: Current and emerging approaches

Herbivorous insects encounter diverse plant specialized metabolites (PSMs) in their diet, that have deterrent, anti-nutritional, or toxic properties. Understanding how they cope with PSMs is crucial to understand their biology, population dynamics, and evolution. This review summarizes current and emerging cutting-edge methods that can be used to characterize the metabolic fate of PSMs, from ingestion to excretion or sequestration. It further emphasizes a workflow that enables not only to study PSM metabolism at different scales, but also to tackle and validate the genetic and biochemical mechanisms involved in PSM resistance by herbivores. This review thus aims at facilitating research on PSM-mediated plant-herbivore interactions.

Developmental programming: Impact of prenatal bisphenol-A exposure on liver and muscle transcriptome of female sheep

Gestational Bisphenol A (BPA) exposure leads to peripheral insulin resistance, and hepatic and skeletal muscle oxidative stress and lipotoxicity during adulthood in the female sheep offspring. To investigate transcriptional changes underlying the metabolic outcomes, coding and non-coding (nc) RNA in liver and muscle from 21-month-old control and prenatal BPA-treated (0.5 mg/kg/day from days 30 to 90 of gestation; Term: 147 days) female sheep were sequenced. Prenatal BPA-treatment dysregulated: expression of 194 genes (138 down, 56 up) in liver and 112 genes (32 down, 80 up) in muscle (FDR < 0.05 and abs log2FC > 0.5); 155 common gene pathways including mitochondrial-related genes in both tissues; 1415 gene pathways including oxidative stress and lipid biosynthetic process specifically in the liver (FDR < 0.01); 192 gene pathways including RNA biosynthetic processes in muscle (FDR < 0.01); 77 lncRNA (49 down, 28 up), 14 microRNAs (6 down, 8 up), 127 snoRNAs (63 down, 64 up) and 55 snRNAs (15 down, 40 up) in the liver while upregulating 6 lncRNA and dysregulating 65 snoRNAs (47 down, 18 up) in muscle (FDR < 0.1, abs log2FC > 0.5). Multiple ncRNA correlated with LCORL, MED17 and ZNF41 mRNA in liver but none of them in the muscle. Discriminant analysis identified (p < 0.05) PECAM, RDH11, ABCA6, MIR200B, and MIR30B in liver and CAST, NOS1, FASN, MIR26B, and MIR29A in muscle as gene signatures of gestational BPA exposure. These findings provide mechanistic clues into the development and/or maintenance of the oxidative stress and lipid accumulation and potential for development of mitochondrial and fibrotic defects contributing to the prenatal BPA-induced metabolic dysfunctions.

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

Metabolomics of Arterial Stiffness

Arterial stiffness (AS) is one of the earliest detectable signs of structural and functional alterations of the vessel wall and an independent predictor of cardiovascular events and death. The emerging field of metabolomics can be utilized to detect a wide spectrum of intermediates and products of metabolism in body fluids that can be involved in the pathogenesis of AS. Research over the past decade has reinforced this idea by linking AS to circulating acylcarnitines, glycerophospholipids, sphingolipids, and amino acids, among other metabolite species. Some of these metabolites influence AS through traditional cardiovascular risk factors (e.g., high blood pressure, high blood cholesterol, diabetes, smoking), while others seem to act independently through both known and unknown pathophysiological mechanisms. We propose the term 'arteriometabolomics' to indicate the research that applies metabolomics methods to study AS. The 'arteriometabolomics' approach has the potential to allow more personalized cardiovascular risk stratification, disease monitoring, and treatment selection. One of its major goals is to uncover the causal metabolic pathways of AS. Such pathways could represent valuable treatment targets in vascular ageing.

Exploring Chemical Markers Related to the Acceptance and Sensory Profiles of Concentrated Liquid Coffees: An Untargeted Metabolomics Approach

In this study, we aimed to apply an untargeted LC/QTOF-MS analysis for the identification of compounds that positively and negatively affect the acceptance of coffee beverages from liquid coffee concentrates (CLCs) before and after storage. The metabolomic results were integrated with physicochemical and sensory parameters, such as color, pH, titratable acidity, and oxygen contents, by a bootstrapped version of partial least squares discriminant analysis (PLS-DA) to select and classify the most relevant variables regarding the rejection or acceptance of CLC beverages. The OPLS-DA models for metabolite selection discriminated between the percent sensory acceptance (the Accepted group) and rejection (the Rejected group). Eighty-two molecular features were considered statistically significant. Our data suggest that coffee sample rejection is associated with chlorogenic acid hydrolysis to produce ferulic and quinic acids, consequently generating methoxybenzaldehydes that impact the perceived acidity and aroma. Furthermore, acceptance was correlated with higher global scores and sweetness, as with lactones such as feruloyl-quinolactone, caffeoyl quinolactone, and 4-caffeoyl-1,5-quinolactone, and significant oxygen levels in the headspace.

Comparison of chemometric strategies for potential exposure marker discovery and false-positive reduction in untargeted metabolomics: application to the serum analysis by LC-HRMS after intake of Vaccinium fruit supplements

Untargeted liquid chromatographic-high-resolution mass spectrometric (LC-HRMS) metabolomics for potential exposure marker (PEM) discovery in nutrikinetic studies generates complex outputs. The correct selection of statistically significant PEMs is a crucial analytical step for understanding nutrition-health interactions. Hence, in this paper, different chemometric selection workflows for PEM discovery, using multivariate or univariate parametric or non-parametric data analyses, were comparatively tested and evaluated. The PEM selection protocols were applied to a small-sample-size untargeted LC-HRMS study of a longitudinal set of serum samples from 20 volunteers after a single intake of (poly)phenolic-rich Vaccinium myrtillus and Vaccinium corymbosum supplements. The non-parametric Games-Howell test identified a restricted group of significant features, thus minimizing the risk of false-positive retention. Among the forty-seven PEMs exhibiting a statistically significant postprandial kinetics, twelve were successfully annotated as purine pathway metabolites, benzoic and benzodiol metabolites, indole alkaloids, and organic and fatty acids, and five (i.e. octahydro-methyl-β-carboline-dicarboxylic acid, tetrahydro-methyl-β-carboline-dicarboxylic acid, citric acid, caprylic acid, and azelaic acid) were associated to Vaccinium berry consumption for the first time. The analysis of the area under the curve of the longitudinal dataset highlighted thirteen statistically significant PEMs discriminating the two interventions, including four intra-intervention relevant metabolites (i.e. abscisic acid glucuronide, catechol sulphate, methyl-catechol sulphate, and α-hydroxy-hippuric acid). Principal component analysis and sample classification through linear discriminant analysis performed on PEM maximum intensity confirmed the discriminating role of these PEMs.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.

Solid-Phase Extraction Embedded Dialysis (SPEED), an Innovative Procedure for the Investigation of Microbial Specialized Metabolites

Solid-phase extraction embedded dialysis (SPEED technology) is an innovative procedure developed to physically separate in-situ, during the cultivation, the mycelium of filament forming microorganisms, such as actinomycetes and fungi, and the XAD-16 resin used to trap the secreted specialized metabolites. SPEED consists of an external nylon cloth and an internal dialysis tube containing the XAD resin. The dialysis barrier selects the molecular weight of the trapped compounds, and prevents the aggregation of biomass or macromolecules on the XAD beads. The external nylon promotes the formation of a microbial biofilm, making SPEED a biofilm supported cultivation process. SPEED technology was applied to the marine Streptomyces albidoflavus 19-S21, isolated from a core of a submerged Kopara sampled at 20 m from the border of a saltwater pond. The chemical space of this strain was investigated effectively using a dereplication strategy based on molecular networking and in-depth chemical analysis. The results highlight the impact of culture support on the molecular profile of Streptomyces albidoflavus 19-S21 secondary metabolites.

Xconnector: Retrieving and visualizing metabolites and pathways information from various database resources

Metabolomics databases contain crucial information collected from various biological systems and experiments. Developers and scientists performed massive efforts to make the database public and accessible. The diversity of the metabolomics databases arises from the different data types included within the database originating from various sources and experiments can be confusing for biologists and researchers who need further manual investigation for the retrieved data. Xconnector is a software package designed to easily retrieve and visualize metabolomics data from different databases. Xconnector can parse information from Human Metabolome Database (HMDB), Livestock Metabolome Database (LMDB), Yeast Metabolome Database (YMDB), Toxin and Toxin Target Database (T3DB), ReSpect Phytochemicals Database (ReSpectDB), The Blood Exposome Database, Phenol-Explorer Database, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Small Molecule Pathway Database (SMPDB). Using Python language, Xconnector connects the targeted databases, recover requested metabolites from single or different database sources, reformat and repack the data to generate a single Excel CSV file containing all information from the databases, in an application programming interface (API)/ Python dependent manner seamlessly. In addition, Xconnector automatically generates graphical outputs in a time-saving approach ready for publication. SIGNIFICANCE: The powerful ability of Xconnector to summarize metabolomics information from different sources would enable researchers to get a closer glimpse on the nature of potential molecules of interest toward medical diagnostics, better biomarker discovery, and personalized medicine. The software is available as an executable application and as a python package compatible for different operating systems.

Efficacy of Analytical Technologies in Metabolomics Studies of the Gastrointestinal Cancers

According to the reports of the World Health Organization and the International Agency for Research on Cancer, cancer is the second leading cause of human death worldwide. However, early-stage detection of cancers can efficiently enhance the chance of therapy and saving lives. Metabolomics strategies apply a variety of approaches to discover new potential diagnoses, prognoses, and/or therapeutic biomarkers of various diseases. Metabolomics aims to identify and measure different low-molecular-weight biomolecules in physiological environments. In these studies, special metabolites are extracted from biological samples and identified using analytical techniques. Afterward, using data processing programs discovering significantly associated biomarkers is pursued. In the present review, we aimed to discuss recently reported analytical approaches on the metabolomics studies of gastrointestinal cancers including gastric, colorectal, and esophageal cancers. The gas- and liquid-chromatography with different detectors have been shown that are the main analytical techniques and for metabolites quantification, nuclear magnetic resonance has been utilized as a master method.

Linking Gut Microbiome and Lipid Metabolism: Moving beyond Associations

Various studies aiming to elucidate the role of the gut microbiome-metabolome co-axis in health and disease have primarily focused on water-soluble polar metabolites, whilst non-polar microbial lipids have received less attention. The concept of microbiota-dependent lipid biotransformation is over a century old. However, only recently, several studies have shown how microbial lipids alter intestinal and circulating lipid concentrations in the host, thus impacting human lipid homeostasis. There is emerging evidence that gut microbial communities play a particularly significant role in the regulation of host cholesterol and sphingolipid homeostasis. Here, we review and discuss recent research focusing on microbe-host-lipid co-metabolism. We also discuss the interplay of human gut microbiota and molecular lipids entering host systemic circulation, and its role in health and disease.

Investigating metabolism by mass spectrometry: From steady state to dynamic view

Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

Untargeted Metabolomics Studies on Drug-Incubated Phragmites australis Profiles

Plants produce a huge number of functionally and chemically different natural products that play an important role in linking the plant with the adjacent environment. Plants can also absorb and transform external organic compounds (xenobiotics). Currently there are only a few studies concerning the effects of xenobiotics and their transformation products on plant metabolites using a mass spectrometric untargeted screening strategy. This study was designed to investigate the changes of the Phragmites australis metabolome following/after diclofenac or carbamazepine incubation, using a serial coupling of reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) combined with accurate high-resolution time-of-flight mass spectrometer (TOF-MS). An untargeted screening strategy of metabolic fingerprints was developed to purposefully compare samples from differently treated P. australis plants, revealing that P. australis responded to each drug differently. When solvents with significantly different polarities were used, the metabolic profiles of P. australis were found to change significantly. For instance, the production of polyphenols (such as quercetin) in the plant increased after diclofenac incubation. Moreover, the pathway of unsaturated organic acids became more prominent, eventually as a reaction to protect the cells against reactive oxygen species (ROS). Hence, P. australis exhibited an adaptive mechanism to cope with each drug. Consequently, the untargeted screening approach is essential for understanding the complex response of plants to xenobiotics.

Metabolomics in Radiation Biodosimetry: Current Approaches and Advances

Triage and medical intervention strategies for unanticipated exposure during a radiation incident benefit from the early, rapid and accurate assessment of dose level. Radiation exposure results in complex and persistent molecular and cellular responses that ultimately alter the levels of many biological markers, including the metabolomic phenotype. Metabolomics is an emerging field that promises the determination of radiation exposure by the qualitative and quantitative measurements of small molecules in a biological sample. This review highlights the current role of metabolomics in assessing radiation injury, as well as considerations for the diverse range of bioanalytical and sampling technologies that are being used to detect these changes. The authors also address the influence of the physiological status of an individual, the animal models studied, the technology and analysis employed in interrogating response to the radiation insult, and variables that factor into discovery and development of robust biomarker signatures. Furthermore, available databases for these studies have been reviewed, and existing regulatory guidance for metabolomics are discussed, with the ultimate goal of providing both context for this area of radiation research and the consideration of pathways for continued development.

Identifying Locations with Possible Undetected Imported Severe Acute Respiratory Syndrome Coronavirus 2 Cases by Using Importation Predictions

Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection exported from mainland China could lead to self-sustained outbreaks in other countries. By February 2020, several countries were reporting imported SARS-CoV-2 cases. To contain the virus, early detection of imported SARS-CoV-2 cases is critical. We used air travel volume estimates from Wuhan, China, to international destinations and a generalized linear regression model to identify locations that could have undetected imported cases. Our model can be adjusted to account for exportation of cases from other locations as the virus spreads and more information on importations and transmission becomes available. Early detection and appropriate control measures can reduce the risk for transmission in all locations.

Metabolomics: A Tool for Cultivar Phenotyping and Investigation of Grain Crops

The quality of plants is often enhanced for diverse purposes such as improved resistance to environmental pressures, better taste, and higher yields. Considering the world’s dependence on plants (nutrition, medicine, or biofuel), developing new cultivars with superior characteristics is of great importance. As part of the ‘omics’ approaches, metabolomics has been employed to investigate the large number of metabolites present in plant systems under well-defined environmental conditions. Recent advances in the metabolomics field have greatly expanded our understanding of plant metabolism, largely driven by potential application to agricultural systems. The current review presents the workflow for plant metabolome analyses, current knowledge, and future directions of such research as determinants of cultivar phenotypes. Furthermore, the value of metabolome analyses in contemporary crop science is illustrated. Here, metabolomics has provided valuable information in research on grain crops and identified significant biomarkers under different conditions and/or stressors. Moreover, the value of metabolomics has been redefined from simple biomarker identification to a tool for discovering active drivers involved in biological processes. We illustrate and conclude that the rapid advances in metabolomics are driving an explosion of information that will advance modern breeding approaches for grain crops and address problems associated with crop productivity and sustainable agriculture.

Understanding the Biostimulant Action of Vegetal-Derived Protein Hydrolysates by High-Throughput Plant Phenotyping and Metabolomics: A Case Study on Tomato

Designing and developing new biostimulants is a crucial process which requires an accurate testing of the product effects on the morpho-physiological traits of plants and a deep understanding of the mechanism of action of selected products. Product screening approaches using omics technologies have been found to be more efficient and cost effective in finding new biostimulant substances. A screening protocol based on the use of high-throughput phenotyping platform for screening new vegetal-derived protein hydrolysates (PHs) for biostimulant activity followed by a metabolomic analysis to elucidate the mechanism of the most active PHs has been applied on tomato crop. Eight PHs (A-G, I) derived from enzymatic hydrolysis of seed proteins of Leguminosae and Brassicaceae species were foliarly sprayed twice during the trial. A non-ionic surfactant Triton X-100 at 0.1% was also added to the solutions before spraying. A control treatment foliarly sprayed with distilled water containing 0.1% Triton X-100 was also included. Untreated and PH-treated tomato plants were monitored regularly using high-throughput non-invasive imaging technologies. The phenotyping approach we used is based on automated integrative analysis of photosynthetic performance, growth analysis, and color index analysis. The digital biomass of the plants sprayed with PH was generally increased. In particular, the relative growth rate and the growth performance were significantly improved by PHs A and I, respectively, compared to the untreated control plants. Kinetic chlorophyll fluorescence imaging did not allow to differentiate the photosynthetic performance of treated and untreated plants. Finally, MS-based untargeted metabolomics analysis was performed in order to characterize the functional mechanisms of selected PHs. The treatment modulated the multi-layer regulation process that involved the ethylene precursor and polyamines and affected the ROS-mediated signaling pathways. Although further investigation is needed to strengthen our findings, metabolomic data suggest that treated plants experienced a metabolic reprogramming following the application of the tested biostimulants. Nonetheless, our experimental data highlight the potential for combined use of high-throughput phenotyping and metabolomics to facilitate the screening of new substances with biostimulant properties and to provide a morpho-physiological and metabolomic gateway to the mechanisms underlying PHs action on plants.