Meta-analysis of untargeted metabolomic data from multiple profiling experiments

Metabolic changes induced by heavy metal copper exposure in human ovarian granulosa cells

Copper (Cu) is a common heavy metal and a hazardous environmental pollutant. Emerging epidemiological evidence suggests that Cu exposure is associated with female infertility, especially ovarian dysfunction. However, the mechanisms underlying ovarian toxicity remain poorly understood. Granulosa cells play crucial roles in follicle development and are the main target cells of environmental pollutants for ovarian toxicity. In this study, we investigated the effects of Cu exposure on human granulosa (KGN) cells by using cell biology and metabolomics methods, and explored the molecular mechanisms of Cu-induced cytotoxicity. We found that Cu reduced cell viability in a dose- and time-dependent manner. Then, metabolomic analyses led to the identification of 279, 368 and 466 differentially expressed metabolites (DEMs) in KGN cells exposed to 10, 60 and 240 μM Cu, respectively. Pathway enrichment analysis revealed that high Cu led to disturbances of glutathione metabolism, nucleotide metabolism, glycerophospholipid and ether lipid metabolism. Using cell biological assays, we found that exposure to high Cu significantly decreased the GSH/GSSG ratio and altered the activities of the antioxidant enzymes SOD and CAT. Exposure to high Cu significantly increased the level of mitochondrial ROS. These findings further supported the results revealed by metabolomic analysis and provided clues for elucidating the mechanism by which Cu interferes with the development of ovarian follicles.

Development of metabolomic biomarkers to discriminate the geographical origin of Korean and Russian snow crabs using CE-TOF/MS

The quantity of snow crabs (Chionoecetes opilio) harvested in Korea is subject to seasonal restrictions; therefore, snow crabs are imported from Russia. Metabolites in snow crabs from two geographic origins were compared. The metabolites were subjected to metabolomic analysis to prevent fraudulent sales of marine products from a particular country. Capillary electrophoresis time-of-flight mass spectrometry was used. Seventy-seven target metabolites were identified using a mass spectral library. Through orthogonal partial least squares discriminant analysis, the top 25 biomarker candidates were evaluated based on p-values and fold changes. A total of 246 peaks (187 and 59 in the cation and anion modes, respectively) were identified. Among the biomarker candidates, 2-oxovaleric acid, asymmetric dimethylarginine, hypotaurine, and allo-threonine were selected as final biomarkers to unequivocally determine the geographic origin. Overall, metabolic analyses allowed us to differentiate snow crabs from different geographic origins. This method could also be extended of other marine products.

Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.

Metabolomic profiling of upper GI malignancies in blood and tissue: a systematic review and meta-analysis

OBJECTIVE

To conduct a systematic review and meta-analysis of case-control and cohort human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on esophageal cancer (EC), cancer of the gastroesophageal junction (GEJ), and gastric cancer (GC) in blood and tissue.

BACKGROUND

Upper gastrointestinal cancers (UGC), predominantly EC, GEJ, and GC, are malignant tumour types with high morbidity and mortality rates. Numerous studies have focused on metabolomic profiling of UGC in recent years. In this systematic review and meta-analysis, we have provided a collective summary of previous findings on metabolites and metabolomic profiling associated with EC, GEJ and GC.

METHODS

Following the PRISMA procedure, a systematic search of four databases (Embase, PubMed, MEDLINE, and Web of Science) for molecular epidemiologic studies on the metabolomic profiles of EC, GEJ and GC was conducted and registered at PROSPERO (CRD42023486631). The Newcastle-Ottawa Scale (NOS) was used to benchmark the risk of bias for case-controlled and cohort studies. QUADOMICS, an adaptation of the QUADAS-2 (Quality Assessment of Diagnostic Accuracy) tool, was used to rate diagnostic accuracy studies. Original articles comparing metabolite patterns between patients with and without UGC were included. Two investigators independently completed title and abstract screening, data extraction, and quality evaluation. Meta-analysis was conducted whenever possible. We used a random effects model to investigate the association between metabolite levels and UGC.

RESULTS

A total of 66 original studies involving 7267 patients that met the required criteria were included for review. 169 metabolites were differentially distributed in patients with UGC compared to healthy patients among 44 GC, 9 GEJ, and 25 EC studies including metabolites involved in glycolysis, anaerobic respiration, tricarboxylic acid cycle, and lipid metabolism. Phosphatidylcholines, eicosanoids, and adenosine triphosphate were among the most frequently reported lipids and metabolites of cellular respiration, while BCAA, lysine, and asparagine were among the most commonly reported amino acids. Previously identified lipid metabolites included saturated and unsaturated free fatty acids and ketones. However, the key findings across studies have been inconsistent, possibly due to limited sample sizes and the majority being hospital-based case-control analyses lacking an independent replication group.

CONCLUSION

Thus far, metabolomic studies have provided new opportunities for screening, etiological factors, and biomarkers for UGC, supporting the potential of applying metabolomic profiling in early cancer diagnosis. According to the results of our meta-analysis especially BCAA and TMAO as well as certain phosphatidylcholines should be implicated into the diagnostic procedure of patients with UGC. We envision that metabolomics will significantly enhance our understanding of the carcinogenesis and progression process of UGC and may eventually facilitate precise oncological and patient-tailored management of UGC.

Metabolome panels as potential noninvasive biomarkers for primary glomerulonephritis sub-types: meta-analysis of profiling metabolomics studies

Primary glomerulonephritis diseases (PGDs) are known as the top causes of chronic kidney disease worldwide. Renal biopsy, an invasive method, is the main approach to diagnose PGDs. Studying the metabolome profiles of kidney diseases is an inclusive approach to identify the disease's underlying pathways and discover novel non-invasive biomarkers. So far, different experiments have explored the metabolome profiles in different PGDs, but the inconsistencies might hinder their clinical translations. The main goal of this meta-analysis study was to achieve consensus panels of dysregulated metabolites in PGD sub-types. The PGDs-related metabolome profiles from urine samples in humans were selected in a comprehensive search. Amanida package in R software was utilized for performing the meta-analysis. Through sub-type analyses, the consensus list of metabolites in each category was obtained. To identify the most affected pathways, functional enrichment analysis was performed. Also, a gene-metabolite network was constructed to identify the key metabolites and their connected proteins. After a vigorous search, among the 11 selected studies (15 metabolite profiles), 270 dysregulated metabolites were recognized in urine of 1154 PGDs and control samples. Through sub-type analyses by Amanida package, the consensus list of metabolites in each category was obtained. Top dysregulated metabolites (vote score of ≥ 4 or ≤ - 4) in PGDs urines were selected as main panel of meta-metabolites including glucose, leucine, choline, betaine, dimethylamine, fumaric acid, citric acid, 3-hydroxyisovaleric acid, pyruvic acid, isobutyric acid, and hippuric acid. The enrichment analyses results revealed the involvement of different biological pathways such as the TCA cycle and amino acid metabolisms in the pathogenesis of PGDs. The constructed metabolite-gene interaction network revealed the high centralities of several metabolites, including pyruvic acid, leucine, and choline. The identified metabolite panels could shed a light on the underlying pathological pathways and be considered as non-invasive biomarkers for the diagnosis of PGD sub-types.

Update on metabolomic findings in COPD patients

COPD is a heterogeneous disorder that shows diverse clinical presentations (phenotypes and "treatable traits") and biological mechanisms (endotypes). This heterogeneity implies that to carry out a more personalised clinical management, it is necessary to classify each patient accurately. With this objective, and in addition to clinical features, it would be very useful to have well-defined biological markers. The search for these markers may either be done through more conventional laboratory and hypothesis-driven techniques or relatively blind high-throughput methods, with the omics approaches being suitable for the latter. Metabolomics is the science that studies biological processes through their metabolites, using various techniques such as gas and liquid chromatography, mass spectrometry and nuclear magnetic resonance. The most relevant metabolomics studies carried out in COPD highlight the importance of metabolites involved in pathways directly related to proteins (peptides and amino acids), nucleic acids (nitrogenous bases and nucleosides), and lipids and their derivatives (especially fatty acids, phospholipids, ceramides and eicosanoids). These findings indicate the relevance of inflammatory-immune processes, oxidative stress, increased catabolism and alterations in the energy production. However, some specific findings have also been reported for different COPD phenotypes, demographic characteristics of the patients, disease progression profiles, exacerbations, systemic manifestations and even diverse treatments. Unfortunately, the studies carried out to date have some limitations and shortcomings and there is still a need to define clear metabolomic profiles with clinical utility for the management of COPD and its implicit heterogeneity.

Metabolomic Profiling in Children with Celiac Disease: Beyond the Gluten-Free Diet

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD's progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. By analyzing metabolites, we can gain insights into the reasons behind CD's occurrence, as well as better comprehend the impact of treatment initiation on patients. In this review, we present a collection of articles that showcase the latest breakthroughs in the field of metabolomics in pediatric CD, with the aim of trying to identify CD biomarkers for both early diagnosis and treatment monitoring. These advancements shed light on the potential of metabolomic analysis in enhancing our understanding of the disease and improving diagnostic and therapeutic strategies. More studies need to be designed to cover metabolic profiles in subjects at risk of developing the disease, as well as those analyzing biomarkers for follow-up treatment with a GFD.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Rice peroxygenase catalyzes lipoxygenase-dependent regiospecific epoxidation of lipid peroxides in the response to abiotic stressors

The peroxygenase pathway plays pivotal roles in plant responses to oxidative stress and other environmental stressors. Analysis of a network of co-expressed stress-regulated rice genes demonstrated that expression of OsPXG9 is negatively correlated with expression of genes involved in jasmonic acid biosynthesis. DNA sequence analysis and structure/function studies reveal that OsPXG9 is a caleosin-like peroxygenase with amphipathic α-helices that localizes to lipid droplets in rice cells. Enzymatic studies demonstrate that 12-epoxidation is slightly more favorable with 9(S)-hydroperoxyoctadecatrienoic acid than with 9(S)-hydroperoxyoctadecadienoic acid as substrate. The products of 12-epoxidation are labile, and the epoxide ring is hydrolytically cleaved into corresponding trihydroxy compounds. On the other hand, OsPXG9 catalyzed 15-epoxidation of 13(S)-hydroperoxyoctadecatrienoic acid generates a relatively stable epoxide product. Therefore, the regiospecific 12- or 15-epoxidation catalyzed by OsPXG9 strongly depends on activation of the 9- or 13- peroxygenase reaction pathways, with their respective preferred substrates. The relative abundance of products in the 9-PXG and 13-PXG pathways suggest that the 12-epoxidation involves intramolecular oxygen transfer while the 15-epoxidation can proceed via intramolecular or intermolecular oxygen transfer. Expression of OsPXG9 is up-regulated by abiotic stimuli such as drought and salt stress, but it is down-regulated by biotic stimuli such as flagellin 22 and salicylic acid. The results suggest that the primary function of OsPXG9 is to modulate the level of lipid peroxides to facilitate effective defense responses to abiotic and biotic stressors.

Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data

Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space.

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.

Rootstock-scion exchanging mRNAs participate in the pathways of amino acids and fatty acid metabolism in cucumber under early chilling stress

Cucumber (Cucumis sativus L.) often experiences chilling stress that limits their growth and productivity. Grafting is widely used to improve abiotic stress resistance by alternating a vigorous root system, suggesting there exists systemic signals communication between distant organs. mRNAs are reported to be evolving in fortification strategies by long-distance signaling when plants suffering from chilling stress. However, the potential function of mobile mRNAs alleviating chilling stress in grafted cucumber is still unknown. Here, the physiological changes, mobile mRNAs profiling, transcriptomic and metabolomic changes in above- and underground tissues of all graft combinations of cucumber and pumpkin responding to chilling stress were established and analyzed comprehensively. The co-relationship between the cluster of chilling-induced pumpkin mobile mRNAs with Differentially Expressed Genes (DEGs) and Differentially Intensive Metabolites (DIMs) revealed that four key chilling-induced pumpkin mobile mRNAs were highly related to glycine, serine and threonine synthesis and fatty acid β-oxidative degradation metabolism in cucumber tissues of heterografts. The verification of mobile mRNAs, potential transport of metabolites and exogenous application of key metabolites of glycerophospholipid metabolism pathway in cucumber seedlings confirmed that the role of mobile mRNAs in regulating chilling responses in grafted cucumber. Our results build a link between the long-distance mRNAs of chilling-tolerant pumpkin and the fatty acid β-oxidative degradation metabolism of chilling-sensitive cucumber. It helps to uncover the mechanism of signaling interaction between scion and stock responding to chilling tolerant in grafted cucumber.

Metabolomics Work Flow and Analytics in Systems Biology

Metabolomics is an omics approach of systems biology that involves the development and assessment of large-scale, comprehensive biochemical analysis tools for metabolites in biological systems. This review describes the metabolomics workflow and provides an overview of current analytic tools used for the quantification of metabolic profiles. We explain analytic tools such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, ionization techniques, and approaches for data extraction and analysis.

Differences in amino acid and lipid metabolism distinguish Crohn's from idiopathic/cryptoglandular perianal fistulas by tissue metabonomic profiling and may offer clues to underlying pathogenesis

INTRODUCTION

Few studies have investigated perianal fistula etiopathogenesis, and although the cryptoglandular theory is widely accepted in idiopathic cases, in Crohn's disease, it is thought to involve the interplay between microbiological, immunological and genetic factors. A pilot study was conducted to assess for metabolic variations in Crohn's perianal fistula tissue that might differ from that of idiopathic (cryptoglandular) perianal fistula tissue as a comparator. The goal was to identify any potential biomarkers of disease, which may improve the understanding of pathogenesis.

AIMS AND METHODS

Fistula tract biopsies were obtained from 30 patients with idiopathic perianal fistula and 20 patients with Crohn's anal fistula. Two different assays were used in an ultra-high-performance liquid chromatography system coupled with a mass spectrometric detector to achieve broad metabolome coverage. Univariate and multivariate statistical data analyses were used to identify differentiating metabolic features corresponding to the perianal fistula phenotype (i.e. Crohn's disease vs. idiopathic).

RESULTS

Significant orthogonal partial least squares discriminant analysis predictive models (validated with cross-validated-analysis of variance P value <0.05) differentiated metabolites from tissue samples from Crohn's vs. idiopathic anal fistula patients using both metabolic profiling platforms. A total of 41 metabolites were identified, suggesting alterations in pathways, including amino acid, carnitine and lipid metabolism.

CONCLUSION

Metabonomics may reveal biomarkers of Crohn's perianal fistula. Further work in larger numbers is required to validate the findings of these studies as well as cross-correlation with microbiome work to better understand the impact of host-gut/environment interactions in the pathophysiology of Crohn's and idiopathic perianal fistulas and identify novel therapeutic targets.

Targeted volatolomics of human monocytes: Comparison of 2D-GC/TOF-MS and 1D-GC/Orbitrap-MS methods

Blood is a complex biological matrix providing valuable information on nutritional, metabolic, and immune status. The detection of blood biomarkers requires sensitive analytical methods because analytes are at very low concentrations. Peripheral blood monocytes play a crucial role in inflammatory processes, and the metabolites released by monocytes during these processes might serve as important signalling molecules and biomarkers of particular physiological states. Headspace solid-phase microextraction (HS-SPME) combined with two different mass spectrometric platforms, two-dimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (2D-GC/TOF-MS) and one-dimensional gas chromatography coupled to Orbitrap mass spectrometry (GC/Orbitrap-MS), were applied for the investigation of volatile organic compounds (VOCs) produced by human peripheral blood monocytes. An optimized method was subsequently applied for the characterization of changes in VOCs induced by lipopolysaccharides (LPS) and zymosan (ZYM) stimulation. Overall, the 2D-GC/TOF-MS and the 1D-GC/Orbitrap-MS analyses each yielded about 4000 and 400 peaks per sample, respectively. In total, 91 VOCs belonging to eight different chemical classes were identified. The samples were collected in two fractions, conditioned media for monitoring extracellularly secreted molecules and cell pellet samples to determine the intracellular composition of VOCs. Alcohols, ketones, and hydrocarbons were the main chemical classes of the metabolic profile identified in cell fractions. Aldehydes, acids and cyclic compounds were characteristic of the conditioned media fraction. Here we demonstrate that HS-SPME-2D-GC/TOF-MS is more suitable for the identification of specific VOC profiles produced by human monocytes than 1D-GC/Orbitrap-MS. We define the signature of VOCs occurring early after monocyte activation and characterise the signalling compounds released by immune cells into media.

The Hitchhiker's Guide to Untargeted Lipidomics Analysis: Practical Guidelines

Lipidomics is a newly emerged discipline involving the identification and quantification of thousands of lipids. As a part of the omics field, lipidomics has shown rapid growth both in the number of studies and in the size of lipidome datasets, thus, requiring specific and efficient data analysis approaches. This paper aims to provide guidelines for analyzing and interpreting lipidome data obtained using untargeted methods that rely on liquid chromatography coupled with mass spectrometry (LC-MS) to detect and measure the intensities of lipid compounds. We present a state-of-the-art untargeted LC-MS workflow for lipidomics, from study design to annotation of lipid features, focusing on practical, rather than theoretical, approaches for data analysis, and we outline possible applications of untargeted lipidomics for biological studies. We provide a detailed R notebook designed specifically for untargeted lipidome LC-MS data analysis, which is based on xcms software.

Physiological and metabolomics responses of two wheat (Triticum aestivum L.) genotypes differing in grain cadmium accumulation

To reduce cadmium (Cd) pollution of food chains, screening and breeding of low-Cd-accumulating genotypes have received increasing attention. However, the mechanisms involving Cd tolerance and accumulation are not fully understood. Here, we investigated the physiological responses and metabolomics profiling on two wheat (Triticum aestivum L.) genotypes, a low-Cd-accumulating genotype in grains (Aikang58, AK58) and a high-Cd-accumulating genotype in grains (Zhenmai10, ZM10), in hydroponic culture treated without/with Cd for 7 days. The results showed that AK58 was a Cd tolerant genotype with higher capacity of antioxidant systems in root. In addition, the concentrations of Cd bound to root cell walls were higher in AK58 than ZM10, of which pectin and hemicellulose played important roles in Cd binding. Moreover, subcellular distribution manifested that Cd sequestrated in the vacuoles was another tolerance mechanism in AK58. Simultaneously, metabolomics profiling showed that, in AK58, phenylalanine metabolism, alanine, aspartate and glutamate metabolism, isoquinoline alkaloid biosynthesis, arginine and proline metabolism, arginine biosynthesis and glyoxylate and dicarboxylate metabolism are highly related to antioxidant defense system, cell wall biosynthesis and metabolisms of phytochelatins together with other organic ligands, playing crucial roles in Cd tolerance and Cd fixation mechanisms in roots. These novel findings should be useful for molecular assisted screening and breeding of low Cd-accumulating genotypes for wheat crop.

Analysis of Differentiated Chemical Components between Zijuan Purple Tea and Yunkang Green Tea by UHPLC-Orbitrap-MS/MS Combined with Chemometrics

Zijuan tea (Camellia sinensis var. assamica cv. Zijuan) is a unique purple tea. Recently, purple tea has drawn much attention for its special flavor and health benefits. However, the characteristic compounds of purple tea compared with green tea have not been reported yet. The present study employed a non-targeted metabolomics approach based on ultra-high performance liquid chromatography (UHPLC)-Orbitrap-tandem mass spectrometry (MS/MS) for comprehensive analysis of characteristic metabolites between Zijuan purple tea (ZJT) and Yunkang green tea (YKT). Partial least squares-discriminant analysis (PLS-DA) indicated that there are significant differences in chemical profiles between ZJT and YKT. A total of 66 major differential metabolites included catechins, proanthocyanins, flavonol and flavone glycosides, phenolic acids, amino acids and alkaloids were identified in ZJT. Among them, anthocyanins are the most characteristic metabolites. Nine glycosides of anthocyanins and six glycosides of proanthocyanins were found to be significantly higher in ZJT than that in YKT. Subsequently, pathway analysis revealed that ZJT might generate anthocyanins and proanthocyanins through the flavonol and flavone glycosides. Furthermore, quantitative analysis showed absolutely higher concentrations of total anthocyanins in ZJT, which correlated with the metabolomics results. This study presented the comprehensive chemical profiling and the characterized metabolites of ZJT. These results also provided chemical evidence for potential health functions of ZJT.

Metabolic Profile Discriminates and Predicts Arabidopsis Susceptibility to Virus under Field Conditions

As obligatory parasites, plant viruses alter host cellular metabolism. There is a lack of information on the variability of virus-induced metabolic responses among genetically diverse plants in a natural context with daily changing conditions. To decipher the metabolic landscape of plant-virus interactions in a natural setting, twenty-six and ten accessions of Arabidopsis thaliana were inoculated with Turnip mosaic virus (TuMV), in two field experiments over 2 years. The accessions were measured for viral accumulation, above-ground biomass, targeted and untargeted metabolic profiles. The phenotypes of the accessions ranged from susceptibility to resistance. Susceptible and resistant accessions were shown to have different metabolic routes after inoculation. Susceptible genotypes accumulate primary and secondary metabolites upon infection, at the cost of hindered growth. Twenty-one metabolic signatures significantly accumulated in resistant accessions whereas they maintained their growth as mock-inoculated plants without biomass penalty. Metabolic content was demonstrated to discriminate and be highly predictive of the susceptibility of inoculated Arabidopsis. This study is the first to describe the metabolic landscape of plant-virus interactions in a natural setting and its predictive link to susceptibility. It provides new insights on plant-virus interactions. In this undomesticated species and in ecologically realistic conditions, growth and resistance are in a permanent conversation.

Metabolomics of gingival crevicular fluid to identify biomarkers for periodontitis: A systematic review with meta-analysis

The present systematic review aimed to examine periodontitis-specific biomarkers in the gingival crevicular fluid (GCF) that could have a diagnostic relevance, and to provide a qualitative assessment of the current literature. Metabolites are reliable indicators of pathophysiological statuses, and their quantification in the GCF can provide an outlook of the changes associated with periodontitis and have diagnostic value. Relevant studies identified from PubMed, Embase, Cochrane Library, and Scopus databases were examined to answer the following PECO question: "In systemically healthy individuals, can concentration of specific metabolites in the GCF be used to discriminate subjects with healthy periodontium (H) or gingivitis from patients with periodontitis (P) and which is the diagnostic accuracy?" Quality of included studies was rated using a modified version of the QUADOMICS tool. Meta-analysis was conducted whenever possible. After the screening of 1,554 titles, 15 studies were selected, with sample size ranging from 30 to 93 subjects. Eleven studies performed targeted metabolomics analysis and provided data for 10 metabolites. Among the most consistent markers, malondialdehyde levels were found higher in the P group compared with H group (SMD = 2.86; 95% CI: 1.64, 4.08). Also, a significant increase of 8-hydroxy-deoxyguanosine, 4-hydroxynonenal, and neopterin was detected in periodontally diseased sites, while glutathione showed an inverse trend. When considering data from untargeted metabolomic analysis in four studies, more than 40 metabolites were found significantly discriminant, mainly related to amino acids and lipids degradation pathways. Notably, only one study reported measures of diagnostic accuracy. Several metabolites were differentially expressed in GCF of subjects across different periodontal conditions, having a major potential for investigating periodontal pathophysiology and for site-specific diagnosis. Oxidative stress-related molecules, such as malondialdehyde and 8-hydroxy-deoxyguanosine, were the most consistently associated to periodontitis (PROSPERO CRD42020188482).

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.

Salivary metabolomics for the diagnosis of periodontal diseases: a systematic review with methodological quality assessment

INTRODUCTION

Early diagnosis of periodontitis by means of a rapid, accurate and non-invasive method is highly desirable to reduce the individual and epidemiological burden of this largely prevalent disease.

OBJECTIVES

The aims of the present systematic review were to examine potential salivary metabolic biomarkers and pathways associated to periodontitis, and to assess the accuracy of salivary untargeted metabolomics for the diagnosis of periodontal diseases.

METHODS

Relevant studies identified from MEDLINE (PubMed), Embase and Scopus databases were systematically examined for analytical protocols, metabolic biomarkers and results from the multivariate analysis (MVA). Pathway analysis was performed using the MetaboAnalyst online software and quality assessment by means of a modified version of the QUADOMICS tool.

RESULTS

Twelve studies met the inclusion criteria, with sample sizes ranging from 19 to 130 subjects. Compared to periodontally healthy individuals, valine, phenylalanine, isoleucine, tyrosine and butyrate were found upregulated in periodontitis patients in most studies; while lactate, pyruvate and N-acetyl groups were the most significantly expressed in healthy individuals. Metabolic pathways that resulted dysregulated are mainly implicated in inflammation, oxidative stress, immune activation and bacterial energetic metabolism. The findings from MVA revealed that periodontitis is characterized by a specific metabolic signature in saliva, with coefficients of determination ranging from 0.52 to 0.99.

CONCLUSIONS

This systematic review summarizes candidate metabolic biomarkers and pathways related to periodontitis, which may provide opportunities for the validation of diagnostic or predictive models and the discovery of novel targets for monitoring and treating such a disease (PROSPERO CRD42020188482).

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

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

Integration of LC/MS-based molecular networking and classical phytochemical approach allows in-depth annotation of the metabolome of non-model organisms - The case study of the brown seaweed Taonia atomaria

Untargeted LC-MS based metabolomics is a useful approach in many research areas such as medicine, systems biology, environmental sciences or even ecology. In such an approach, annotation of metabolomes of non-model organisms remains a significant challenge. In this study, an analytical workflow combining a classical phytochemical approach, using the isolation and the full characterization of the chemical structure of natural products, together with the use of MS/MS-based molecular networking with various levels of restrictiveness was developed. This protocol was applied to the marine brown seaweed Taonia atomaria, a cosmopolitan algal species, and allowed to annotate more than 200 metabolites. First, the algal organic crude extracts were fractionated by flash-chromatography and the chemical structure of eight of the main chemical constituents of this alga were fully characterized by means of spectroscopic methods (1D and 2D NMR, HRMS). These compounds were further used as chemical standards. In a second step, the main fractions of the algal extracts were analyzed by UHPLC-MS/MS and the resulting data were uploaded to the Global Natural Products Social Molecular Networking platform (GNPS) to create several molecular networks (MNs). A first MN (MN-1) was built with restrictive parameters and allowed the creation of clusters composed by nodes with highly similar MS/MS spectra. Then, using database hits and chemical standards as "seed" nodes and/or similarity between MS/MS fragmentation pattern, the main clusters were easily annotated as common glycerolipids and phospholipids, much rare lipids -such as acylglycerylhydroxymethyl-N,N,N-trimethyl-ß-alanines or fulvellic acid derivatives- but also new glycerolipids bearing a terpene moiety. Lastly, the use of less and less constrained MNs allowed to further increase the number of annotated metabolites.

Targeted and non-targeted unexpected food contaminants analysis by LC/HRMS: Feasibility study on rice

A widely applicable analytical LC/HRMS method based on ion source optimization, data treatment optimization on rice matrix was developed. The effects of key parameters of ion source, and their interactions on ESI response were studied on HPLC-QTOF. Compared with center points, 40% and 20% increase of response factors in the positive and negative mode can be achieved by ion source optimization, respectively. Data processing strategies inspired from metabolomics and multi-targeted analysis were compared and developed using case and control rice samples. Highly automated workflow using XCMS achieved highest mass accuracy, highest detection rate of 96% for 5 μg/kg in a non-targeted way. A clear distinction between the control and contaminated samples by PCA and PLS-DA was also achieved by this workflow using XCMS, even for the concentration of 5 μg/kg.

Addition of insoluble fiber to isolation media allows for increased metabolite diversity of lab-cultivable microbes derived from zebrafish gut samples

There is a gap in measured microbial diversity when comparing genomic sequencing techniques versus cultivation from environmental samples in a laboratory setting. Standardized methods in artificial environments may not recapitulate the environmental conditions that native microbes require for optimal growth. For example, the intestinal tract houses microbes at various pH values as well as minimal oxygen and light environments. These microbes are also exposed to an atypical source of carbon: dietary fiber compacted in fecal matter. To investigate how the addition of insoluble fiber to isolation media could affect the cultivation of microbes from zebrafish intestines, an isolate library was built and analyzed using the bioinformatics pipeline IDBac. While all isolation media encouraged the growth of species from several phyla, the extent of growth was greater with the addition of fiber allowing for easier isolation. Furthermore, fiber addition altered the metabolism of the cultivated gut-derived microbes and induced the production of unique metabolites that were not produced when microbes were otherwise grown on standard isolation media. Addition of this inexpensive carbon source to the media supported the cultivation of a diverse community whose secondary metabolite production may more closely replicate their metabolite production in vivo.

Comparative metabolomic responses of low- and high-cadmium accumulating genotypes reveal the cadmium adaptive mechanism in Brassica napus

Recently, oilseed rape has gathered interest for its ability to withstand elevated metal contents in plant, a key feature for remediation of contaminated soils. In this study, comparative and functional metabolomic analyses using liquid chromatography/mass spectrometry were undertaken to explore the metabolic basis of this attribute under cadmium (Cd) stress. Results revealed both conserved and differential metabolomic responses between genotype CB671 (tolerant Cd-accumulating) and its sensitive counterpart ZD622. CB671 responded to Cd stress by rearranging carbon flux towards production of compatible solutes, sugar storage forms and ascorbate, as well as jasmonates, ethylene and vitamin B6. Intriguingly, IAA abundance was reduced by 1.91-fold, which was in connection with tryptophan funnelling into serotonin (3.48-fold rise). In ZD622 by contrast, Cd provoked drastic depletion of carbohydrates and vitamins, but subtle hormones alteration. A striking accumulation of unsaturated fatty acids and oxylipins in CB671, paralleled by glycerophospholipids build-up and induction of inositol-derived signalling metabolites (up to 5.41-fold) suggested ability for prompt triggering of detoxifying mechanisms. Concomitantly, phytosteroids, monoterpenes and carotenoids were induced, denoting fine-tuned mechanisms for membrane maintenance, which was not evident in ZD622. Further, ZD622 markedly accumulated phenolics from upstream sub-classes of flavonoids; in CB671 however, a distinct phenolic wiring was activated, prioritizing anthocyanins and lignans instead. Along with cell wall (CW) saccharides, the activation of lignans evoked CW priming in CB671. Current results have demonstrated existence of notable metabolomic-based strategies for Cd tolerance in metal-accumulating oilseed rapes, and provided a holistic view of metabolites potentially contributing to Cd tolerance in this species.

Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome

BACKGROUND

Obesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analysis (Mendelian randomization) to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals.

METHODS

We expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirectional IV analysis to classify metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS.

RESULTS

We identified ten known metabolites that are more likely to be causes (e.g., alpha-hydroxybutyrate) or effects (e.g., valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g., glycine). Importantly, we also identified about five times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched (p < 0.05) metabolite sets for the cause versus effect groups, providing further support that these two metabolite groups are linked to obesity via distinct biological mechanisms.

CONCLUSIONS

These findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.

Monitoring peppermint washout in the breath metabolome by secondary electrospray ionization-high resolution mass spectrometry

In this study, a secondary electrospray ionization-high resolution mass spectrometer (SESI-HRMS) system was employed to profile the real-time exhaled metabolome of ten subjects who had ingested a peppermint oil capsule. In total, six time points were sampled during the experiment. Using an untargeted way of profiling breath metabolome, 2333 m/z unique metabolite features were determined in positive mode, and 1322 in negative mode. To benchmark the performance of the SESI-HRMS setup, several additional checks were done, including determination of the technical variation, the biological variation of one subject within three days, the variation within a time point, and the variation across all samples, taking all m/z features into account. Reproducibility was good, with the median technical variation being 18% and the median variation within biological replicates being 34%. Both variations were lower than the variation across individuals. Washout profiles of compounds from the peppermint oil, including menthone, limonene, pulegone, menthol and menthofuran were determined in all subjects. Metabolites of the peppermint oil were also determined in breath, for example, cis/trans-carveol, perillic acid and menthol glucuronide. Butyric acid was found to be the major metabolite that reduce the uptake rate of limonene. Pathways related to limonene metabolism were examined, and meaningful pathways were identified from breath metabolomics data acquired by SESI using an untargeted analysis.

Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems biology

Archived metabolomics data represent a broad resource for the scientific community. However, the absence of tools for the meta-analysis of heterogeneous data types makes it challenging to perform direct comparisons in a single and cohesive workflow. Here we present a framework for the meta-analysis of metabolic pathways and interpretation with proteomic and transcriptomic data. This framework facilitates the comparison of heterogeneous types of metabolomics data from online repositories (e.g., XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights) representing tens of thousands of studies, as well as locally acquired data. As a proof of concept, we apply the workflow for the meta-analysis of i) independent colon cancer studies, further interpreted with proteomics and transcriptomics data, ii) multimodal data from Alzheimer's disease and mild cognitive impairment studies, demonstrating its high-throughput capability for the systems level interpretation of metabolic pathways. Moreover, the platform has been modified for improved knowledge dissemination through a collaboration with Metabolomics Workbench and LIPID MAPS. We envision that this meta-analysis tool will help overcome the primary bottleneck in analyzing diverse datasets and facilitate the full exploitation of archival metabolomics data for addressing a broad array of questions in metabolism research and systems biology.

Untargeted Proteomics-Based Profiling for the Identification of Novel Processing-Induced Protein Modifications in Milk

Nonenzymatic post-translational protein modifications (nePTMs) affect the nutritional, physiological, and technological properties of proteins in food and in vivo. In contrast to the usual targeted analyses, the present study determined nePTMs in processed milk in a truly untargeted proteomic approach. Thus, it was possible to determine to which extent known nePTM structures explain protein modifications in processed milk and to detect and identify novel products. The method combined ultrahigh-performance liquid chromatography coupled to electrospray ionization tandem mass spectrometry with bioinformatic data analysis by the software XCMS. The nePTMs detected by untargeted profiling of a β-lactoglobulin-lactose model were incorporated in a sensitive scheduled multiple reaction monitoring method to analyze these modifications in milk samples and to monitor their reaction kinetics during thermal treatment. Additionally, we identified the structures of unknown modifications. Lactosylation, carboxymethylation, formylation of lysine and N-terminus, glycation of arginine, oxidation of methionine, tryptophan, and cysteine, oxidative deamination of N-terminus, and deamidation of asparagine and glutamine were the most important reactions of β-lactoglobulin during milk processing. The isomerization of aspartic acid was observed for the first time in milk products, and N-terminal 4-imidazolidinone was identified as a novel nePTM.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Discovering Temporal Patterns in Longitudinal Nontargeted Metabolomics Data via Group and Nuclear Norm Regularized Multivariate Regression

Temporal associations in longitudinal nontargeted metabolomics data are generally ignored by common pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). To discover temporal patterns in longitudinal metabolomics, a multitask learning (MTL) method employing structural regularization was proposed. The group regularization term of the proposed MTL method enables the selection of a small number of tentative biomarkers while maintaining high prediction accuracy. Meanwhile, the nuclear norm imposed into the regression coefficient accounts for the interrelationship of the metabolomics data obtained on consecutive time points. The effectiveness of the proposed method was demonstrated by comparison study performed on a metabolomics dataset and a simulating dataset. The results showed that a compact set of tentative biomarkers charactering the whole antipyretic process of Qingkailing injection were selected with the proposed method. In addition, the nuclear norm introduced in the new method could help the group norm to improve the method’s recovery ability.

Combining Physiological and Metabolomic Analysis to Unravel the Regulations of Coronatine Alleviating Water Stress in Tobacco (Nicotiana tabacum L.)

Drought is a major abiotic stress that restricts plants growth, development, and yield. Coronatine (COR), a mimic of JA-Ile, functions in plant tolerance to multiple stresses. In our study, we examined the effects of COR in tobacco under polyethylene glycol (PEG) stress. COR treatment improved plant growth under stress as measured by fresh weight (FW) and dry weight (DW). The enzyme activity assay indicated that, under osmotic stress conditions, the activities of superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), and glutathione reductase (GR) were enhanced by COR treatment. Histochemical analyses via nitrotetrazolium blue chloride (NBT) and 3,3'-diaminobenzidine (DAB) staining showed that COR reduced reactive oxygen species (ROS) accumulation during osmotic stress. Metabolite profiles revealed that COR triggered significant metabolic changes in tobacco leaves under osmotic stress, and many essential metabolites, such as sugar and sugar derivatives, organic acids, and nitrogen-containing compounds, which might play active roles in osmotic-stressed tobacco plants, were markedly accumulated in the COR-treated tobacco. The work presented here provides a comprehensive understanding of the COR-mediated physiological, biochemical, and metabolic adjustments that minimize the adverse impact of osmotic stress on tobacco.

Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software

Small-molecule drugs and toxicants commonly interact with more than a single protein target, each of which may have unique effects on cellular phenotype. Although untargeted metabolomics is often applied to understand the mode of action of these chemicals, simple pairwise comparisons of treated and untreated samples are insufficient to resolve the effects of disrupting two or more independent protein targets. Here, we introduce a workflow for dose-response metabolomics to evaluate chemicals that potentially affect multiple proteins with different potencies. Our approach relies on treating samples with various concentrations of compound prior to analysis with mass spectrometry-based metabolomics. Data are then processed with software we developed called TOXcms, which statistically evaluates dose-response trends for each metabolomic signal according to user-defined tolerances and subsequently groups those that follow the same pattern. Although TOXcms was built upon the XCMS framework, it is compatible with any metabolomic data-processing software. Additionally, to enable correlation of dose responses beyond those that can be measured by metabolomics, TOXcms also accepts data from respirometry, cell death assays, other omic platforms, etc. In this work, we primarily focus on applying dose-response metabolomics to find off-target effects of drugs. Using metformin and etomoxir as examples, we demonstrate that each group of dose-response patterns identified by TOXcms signifies a metabolic response to a different protein target with a unique drug binding affinity. TOXcms is freely available on our laboratory website at http://pattilab.wustl.edu/software/toxcms .

Annotation of Specialized Metabolites from High-Throughput and High-Resolution Mass Spectrometry Metabolomics

High-throughput mass spectrometry (MS) metabolomics profiling of highly complex samples allows the comprehensive detection of hundreds to thousands of metabolites under a given condition and point in time and produces information-rich data sets on known and unknown metabolites. One of the main challenges is the identification and annotation of metabolites from these complex data sets since the number of authentic standards available for specialized metabolites is far lower than an account for the number of mass spectral features. Previously, we reported two novel tools, MetNet and MetCirc, for putative annotation and structural prediction on unknown metabolites using known metabolites as baits. MetNet employs differences between m/z values of MS1 features, which correspond to metabolic transformations, and statistical associations, while MetCirc uses MS/MS features as input and calculates similarity scores of aligned spectra between features to guide the annotation of metabolites. Here, we showcase the use of MetNet and MetCirc to putatively annotate metabolites and provide detailed instructions as to how those can be used. While our case studies are from plants, the tools find equal utility in studies on bacterial, fungal, or mammalian xenobiotic samples.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Metabolomics responses of Bambusa pervariabilis × Dendrocalamopsis grandis varieties to Biotic (pathogenic fungus) stress

Bambusa pervariabilis × Dendrocalamopsis grandis blight, caused by Arthrinium phaeospermum, is one of the most common and serious diseases in bamboo and occurs in the newly born twigs. Bamboo has suffered large dead areas, including more than 3000 hm², which greatly threatens the process of returning farmlands to forests and the construction of ecological barriers. To identify differential metabolites and metabolic pathways associated with B. pervariabilis × D. grandis to A. phaeospermum, ultra-performance liquid chromatography (UPLC) and quadrupole-time of flight (Q-TOF) Mass Spectrometry (MS) combined with a data-dependent acquisition method was used to analyse the entire sample spectrum. In total, 13223 positive ion peaks and 10616 negative ion peaks were extracted. OPLS-DA and several other analyses were performed using the original data. The OPLS-DA models showed good quality and had strong predictive power, indicating clear trends in the analyses of the treatment and control groups. Clustering and KEGG pathway analyses were used to screen the differential metabolites in the treatment and control groups from the three B. pervariabilis × D. grandis varieties and reflected their metabolic responses induced by A. phaeospermum infection. The results showed that the three B. pervariabilis × D. grandis varieties mode showed significant changes in the following six resistance-related metabolites after A. phaeospermum invasion in positive and negative ion modes: proline, glutamine, dictamnine, apigenin 7-O-neohesperidoside, glutamate, and cis-Aconitate. The following four main metabolic pathways are involved: Arginine and proline metabolism, Glyoxylate and dicarboxylate metabolism, Biosynthesis of alkaloids derived from shikimate pathway, and Flavone and flavonol biosynthesis. This study lays a foundation for the later detection of differential metabolites and metabolic pathways for targeting, and provides a theoretical basis for disease-resistant breeding and the control of B. pervariabilis × D. grandis blight.

Systems-level analysis of isotopic labeling in untargeted metabolomic data by X13CMS

Identification of previously unreported metabolites (so-called 'unknowns') in untargeted metabolomic data has become an increasingly active area of research. Considerably less attention, however, has been dedicated to identifying unknown metabolic pathways. Yet, for each unknown metabolite structure, there is potentially a yet-to-be-discovered chemical transformation. Elucidating these biochemical connections is essential to advancing our knowledge of cellular metabolism and can be achieved by tracking an isotopically labeled precursor to an unexpected product. In addition to their role in mapping metabolic fates, isotopic labels also provide critical insight into pathway dynamics (i.e., metabolic fluxes) that cannot be obtained from conventional label-free metabolomic analyses. When labeling is compared quantitatively between conditions, for example, isotopic tracers can enable relative pathway activities to be inferred. To discover unexpected chemical transformations or unanticipated differences in metabolic pathway activities, we have developed X13CMS, a platform for analyzing liquid chromatography/mass spectrometry (LC/MS) data at the systems level. After providing cells, animals, or patients with an isotopically enriched metabolite (e.g., 13C, 15N, or 2H), X13CMS identifies compounds that have incorporated the isotopic tracer and reports the extent of labeling for each. The analysis can be performed with a single condition, or isotopic fates can be compared between multiple conditions. The choice of which metabolite to enrich and which isotopic label to use is highly context dependent, but 13C-glucose and 13C-glutamine are often applied because they feed a large number of metabolic pathways. X13CMS is freely available.

Ontology-based metabolomics data integration with quality control

Aim: The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. Results & methodology: This paper presents an integrated system of quality control (QC) methods to assess metabolomics results by evaluating the data acquisition strategies and metabolite identification process when integrating datasets for meta-analysis. An ontology knowledge base and a rule-based system representing the experiment and chemical background information direct the processes involved in data integration and QC verification. A diabetes meta-analysis study using these QC methods finds putative biomarkers that differ between cohorts. Conclusion: The methods presented here ensure the validity of meta-analysis when integrating data from different metabolic profiling studies.

PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data

Metabolomics is a powerful approach for discovering biomarkers and for characterizing the biochemical consequences of genetic variation. While untargeted metabolite profiling can measure thousands of signals in a single experiment, many biologically meaningful signals cannot be readily identified as known metabolites nor compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To overcome these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets and to generate metabolic pathway annotations for these signals. To pair up signals measured in different datasets, where retention times (RT) are often not comparable or even available, we implemented an imputation-based approach that only requires mass-to-charge ratios (m/z). As validation, we treated each shared known metabolite as an unmatched signal and showed that PAIRUP-MS correctly matched 70–88% of these metabolites from among thousands of signals, equaling or outperforming a standard m/z- and RT-based approach. We performed further validation using genetic data: the most stringent set of matched signals and shared knowns showed comparable consistency of genetic associations across datasets. Next, we developed a pathway reconstitution method to annotate unknown signals using curated metabolic pathways containing known metabolites. We performed genetic validation for the generated annotations, showing that annotated signals associated with gene variants were more likely to be enriched for pathways functionally related to the genes compared to random expectation. Finally, we applied PAIRUP-MS to study associations between metabolites and genetic variants or body mass index (BMI) across multiple datasets, identifying up to ~6 times more significant signals and many more BMI-associated pathways compared to the standard practice of only analyzing known metabolites. These results demonstrate that PAIRUP-MS enables analysis of unknown signals in a robust, biologically meaningful manner and provides a path to more comprehensive, well-powered studies of untargeted metabolomics data.

Untargeted metabolomics can systematically profile thousands of metabolite signals in biological samples and is an increasingly popular approach for discovering biomarkers and predictors for human traits and diseases. However, currently, a significant portion of the measured signals cannot be identified as known metabolites or easily compared across datasets, and thus are usually excluded from downstream analyses. Here, we present PAIRUP-MS, a suite of computational methods designed to analyze unknown, unidentified signals across multiple mass spectrometry-based profiling datasets. Specifically, PAIRUP-MS contains a flexible imputation-based approach for pairing up unknown signals across datasets, allowing for meta-analysis of matched signals across studies that would otherwise be incompatible. PAIRUP-MS also offers a pathway annotation and enrichment analysis framework that links metabolite signals to plausible biological functions without using their chemical identities. Importantly, we validated both components of PAIRUP-MS using genetic data and applied them to study an example trait, body mass index. Overall, our results demonstrate that PAIRUP-MS enables previously infeasible analyses of unknown, unidentified signals across multiple datasets, thereby greatly improving power for discovery and biological inference.

Cluster Analysis of Untargeted Metabolomic Experiments

Untargeted metabolite profiling based upon LC-MS methodology can be used to identify unique metabolic phenotypes associated with stress, disease or environmental exposure of cells using mathematical clustering. Here, we show how unsupervised data analysis is a powerful tool for both quality control and answering simple biological questions. We will demonstrate how to format untargeted mass spectrometry data for import into R, a programming language and software environment for statistical computing (R Development Core Team. R: A language and environment for statistical computing, reference index version 2.15. R Foundation for Statistical Computing, Vienna, 2012). Using R, we transform untargeted metabolite data using hierarchical clustering and principal component analysis (PCA) to create visual representations of change between biological samples and explore how these can be used predictively, in determining environmental stress, health and metabolic insight.

A Protocol to Compare Methods for Untargeted Metabolomics

There are thousands of published methods for profiling metabolites with liquid chromatography/mass spectrometry (LC/MS). While many have been evaluated and optimized for a small number of select metabolites, very few have been assessed on the basis of global metabolite coverage. Thus, when performing untargeted metabolomics, researchers often question which combination of extraction techniques, chromatographic separations, and mass spectrometers is best for global profiling. Method comparisons are complicated because thousands of LC/MS signals (so-called features) in a typical untargeted metabolomic experiment cannot be readily identified with current resources. It is therefore challenging to distinguish methods that increase signal number due to improved metabolite coverage from methods that increase signal number due to contamination and artifacts. Here, we present the credentialing protocol to remove the latter from untargeted metabolomic datasets without having to identify metabolite structures. This protocol can be used to compare or optimize methods pertaining to any step of the untargeted metabolomic workflow (e.g., extraction, chromatography, mass spectrometer, informatic software, etc.).