Processing and analysis of GC/LC-MS-based metabolomics data

Chemical Profiling of Polar Lipids and the Polyphenolic Fraction of Commercial Italian Phaseolus Seeds by UHPLC-HRMS and Biological Evaluation

The common bean (Phaseolus vulgaris L.) is one of the oldest food crops in the world. In this study, the ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-MS/MS) technique was used to characterize the polar lipid composition and polyphenolic fraction of five bean varieties commonly consumed in Italy: Cannellino (PVCA), Controne (PVCO), Borlotti (PVBO), Stregoni (PVST), and Vellutina (PVVE). Lipid content represents a minor fraction of the whole metabolome in dry beans, and little is known about their polar lipids, which could be potentially bioactive components. Thirty-three compounds were detected through UHPLC-MS/MS, including oxylipins, phospholipids, N-acyl glycerolipids, and several fatty acids. The dichloromethane extracts were subjected to principal component analysis (PCA), with the results showing greater differentiation for the Borlotti variety. Moreover, 27 components belonging to different polyphenol classes, such as phenolic acids, flavonoids, catechins, anthocyanins and their glycosides, and some saponins, were identified in the hydroalcoholic seed extracts. In addition, the mineral content of the beans was determined. Considering the high number of compounds in the five apolar seed extracts, all samples were examined to determine their in vitro inhibitory activity against the enzyme cyclooxygenase-2 (COX-2), which is inducible in inflammatory cells and mediates inflammatory responses. Only PVCO showed the best inhibition of the COX-2 enzyme with an IC50 = 31.15 ± 2.16 µg/mL. In light of these results, the potential anti-inflammatory properties of PVCO were evaluated in the LPS-stimulated murine macrophage cell line J774A.1. Herein, we demonstrate, for the first time, that PVCO at 30 µg/mL can significantly reduce the release of TNF-α, with a less significant anti-inflammatory effect being observed in terms of IL-6 release.

Oxylipin concentration shift in exhaled breath condensate (EBC) of SARS-CoV-2 infected patients

Infection of airway epithelial cells with severe acute respiratory coronavirus 2 (SARS-CoV-2) can lead to severe respiratory tract damage and lung injury with hypoxia. It is challenging to sample the lower airways non-invasively and the capability to identify a highly representative specimen that can be collected in a non-invasive way would provide opportunities to investigate metabolomic consequences of COVID-19 disease. In the present study, we performed a targeted metabolomic approach using liquid chromatography coupled with high resolution chromatography (LC-MS) on exhaled breath condensate (EBC) collected from hospitalized COVID-19 patients (COVID+) and negative controls, both non-hospitalized and hospitalized for other reasons (COVID-). We were able to noninvasively identify and quantify inflammatory oxylipin shifts and dysregulation that may ultimately be used to monitor COVID-19 disease progression or severity and response to therapy. We also expected EBC-based biochemical oxylipin changes associated with COVID-19 host response to infection. The results indicated ten targeted oxylipins showing significative differences between SAR-CoV-2 infected EBC samples and negative control subjects. These compounds were prostaglandins A2 and D2, LXA4, 5-HETE, 12-HETE, 15-HETE, 5-HEPE, 9-HODE, 13-oxoODE and 19(20)-EpDPA, which are associated with specific pathways (i.e. P450, COX, 15-LOX) related to inflammatory and oxidative stress processes. Moreover, all these compounds were up-regulated by COVID+, meaning their concentrations were higher in subjects with SAR-CoV-2 infection. Given that many COVID-19 symptoms are inflammatory in nature, this is interesting insight into the pathophysiology of the disease. Breath monitoring of these and other EBC metabolites presents an interesting opportunity to monitor key indicators of disease progression and severity.

Exhaled breath condensate profiles of U.S. Navy divers following prolonged hyperbaric oxygen (HBO) and nitrogen-oxygen (Nitrox) chamber exposures

Prolonged exposure to hyperbaric hyperoxia can lead to pulmonary oxygen toxicity (PO2tox). PO2tox is a mission limiting factor for special operations forces divers using closed-circuit rebreathing apparatus and a potential side effect for patients undergoing hyperbaric oxygen (HBO) treatment. In this study, we aim to determine if there is a specific breath profile of compounds in exhaled breath condensate (EBC) that is indicative of the early stages of pulmonary hyperoxic stress/PO2tox. Using a double-blind, randomized 'sham' controlled, cross-over design 14 U.S. Navy trained diver volunteers breathed two different gas mixtures at an ambient pressure of 2 ATA (33 fsw, 10 msw) for 6.5 h. One test gas consisted of 100% O2(HBO) and the other was a gas mixture containing 30.6% O2with the balance N2(Nitrox). The high O2stress dive (HBO) and low O2stress dive (Nitrox) were separated by at least seven days and were conducted dry and at rest inside a hyperbaric chamber. EBC samples were taken immediately before and after each dive and subsequently underwent a targeted and untargeted metabolomics analysis using liquid chromatography coupled to mass spectrometry (LC-MS). Following the HBO dive, 10 out of 14 subjects reported symptoms of the early stages of PO2tox and one subject terminated the dive early due to severe symptoms of PO2tox. No symptoms of PO2tox were reported following the nitrox dive. A partial least-squares discriminant analysis of the normalized (relative to pre-dive) untargeted data gave good classification abilities between the HBO and nitrox EBC with an AUC of 0.99 (±2%) and sensitivity and specificity of 0.93 (±10%) and 0.94 (±10%), respectively. The resulting classifications identified specific biomarkers that included human metabolites and lipids and their derivatives from different metabolic pathways that may explain metabolomic changes resulting from prolonged HBO exposure.

1H NMR metabolomics analysis of oil palm stem tissue infected by Ganoderma boninense based on field severity Indices

Basal stem rot disease (BSR) caused by G. boninense affects most oil palm plants in Southeast Asia. This disease can be fatal to palm oil production. BSR shows no signs on the tree in the early stages of infection. Therefore, it is essential to find an approach that can detect BSR disease in oil palm, especially at any level of disease severity in the field. This study aims to identify biomarkers of BSR disease in oil palm stem tissue based on various disease severity indices in the field using ¹H NMR-based metabolomics analysis. The crude extract of oil palm stem tissue with four disease severity indices was analyzed by ¹H NMR metabolomics. Approximately 90 metabolites from oil palm stem tissue were identified.Twenty of these were identified as metabolites that significantly differentiated the four disease severity indices. These metabolites include the organic acid group, the carbohydrate group, the organoheterocyclic compound group, and the benzoid group. In addition, different tentative biomarkers for different disease severity indices were also identified. These tentative biomarkers consist of groups of organic acids, carbohydrates, organoheterocyclic compounds, nitrogenous organic compounds, and benzene. There are five pathways in oil palm that are potentially affected by BSR disease.

Role of lipidomics in assessing the functional lipid composition in breast milk

Breast milk is the ideal source of nutrients for infants in early life. Lipids represent 2–5% of the total breast milk composition and are a major energy source providing 50% of an infant’s energy intake. Functional lipids are an emerging class of lipids in breast milk mediating several different biological functions, health, and developmental outcome. Lipidomics is an emerging field that studies the structure and function of lipidome. It provides the ability to identify new signaling molecules, mechanisms underlying physiological activities, and possible biomarkers for early diagnosis and prognosis of diseases, thus laying the foundation for individualized, targeted, and precise nutritional management strategies. This emerging technique can be useful to study the major role of functional lipids in breast milk in several dimensions. Functional lipids are consumed with daily food intake; however, they have physiological benefits reported to reduce the risk of disease. Functional lipids are a new area of interest in lipidomics, but very little is known of the functional lipidome in human breast milk. In this review, we focus on the role of lipidomics in assessing functional lipid composition in breast milk and how lipid bioinformatics, a newly emerging branch in this field, can help to determine the mechanisms by which breast milk affects newborn health.

Metabolomics Analysis of Bacterial Pathogen Burkholderia thailandensis and Mammalian Host Cells in Co-culture

The Tier 1 HHS/USDA Select Agent Burkholderia pseudomallei is a bacterial pathogen that is highly virulent when introduced into the respiratory tract and intrinsically resistant to many antibiotics. Transcriptomic- and proteomic-based methodologies have been used to investigate mechanisms of virulence employed by B. pseudomallei and Burkholderia thailandensis, a convenient surrogate; however, analysis of the pathogen and host metabolomes during infection is lacking. Changes in the metabolites produced can be a result of altered gene expression and/or post-transcriptional processes. Thus, metabolomics complements transcriptomics and proteomics by providing a chemical readout of a biological phenotype, which serves as a snapshot of an organism's physiological state. However, the poor signal from bacterial metabolites in the context of infection poses a challenge in their detection and robust annotation. In this study, we coupled mammalian cell culture-based metabolomics with feature-based molecular networking of mono- and co-cultures to annotate the pathogen's secondary metabolome during infection of mammalian cells. These methods enabled us to identify several key secondary metabolites produced by B. thailandensis during infection of airway epithelial and macrophage cell lines. Additionally, the use of in silico approaches provided insights into shifts in host biochemical pathways relevant to defense against infection. Using chemical class enrichment analysis, for example, we identified changes in a number of host-derived compounds including immune lipids such as prostaglandins, which were detected exclusively upon pathogen challenge. Taken together, our findings indicate that co-culture of B. thailandensis with mammalian cells alters the metabolome of both pathogen and host and provides a new dimension of information for in-depth analysis of the host-pathogen interactions underlying Burkholderia infection.

Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry

Alterations in cell metabolism, including changes in lipid composition occurring during malignancy, are well characterized for various tumor types. However, a significant part of studies that deal with brain tumors have been performed using cell cultures and animal models. Here, we present a dataset of 124 high-resolution negative ionization mode lipid profiles of human brain tumors resected during neurosurgery. The dataset is supplemented with 38 non-tumor pathological brain tissue samples resected during elective surgery. The change in lipid composition alterations of brain tumors enables the possibility of discriminating between malignant and healthy tissues with the implementation of ambient mass spectrometry. On the other hand, the collection of clinical samples allows the comparison of the metabolism alteration patterns in animal models or in vitro models with natural tumor samples ex vivo. The presented dataset is intended to be a data sample for bioinformaticians to test various data analysis techniques with ambient mass spectrometry profiles, or to be a source of clinically relevant data for lipidomic research in oncology.

Metabolomics Data Treatment: Basic Directions of the Full Process

The present chapter describes basic aspects of the main steps for data processing on mass spectrometry-based metabolomics platforms, focusing on the main objectives and important considerations of each step. Initially, an overview of metabolomics and the pivotal techniques applied in the field are presented. Important features of data acquisition and preprocessing such as data compression, noise filtering, and baseline correction are revised focusing on practical aspects. Peak detection, deconvolution, and alignment as well as missing values are also discussed. Special attention is given to chemical and mathematical normalization approaches and the role of the quality control (QC) samples. Methods for uni- and multivariate statistical analysis and data pretreatment that could impact them are reviewed, emphasizing the most widely used multivariate methods, i.e., principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least square-discriminant analysis (OPLS-DA), and hierarchical cluster analysis (HCA). Criteria for model validation and softwares used in data processing were also approached. The chapter ends with some concerns about the minimal requirements to report metadata in metabolomics.

Mining plant metabolomes: Methods, applications, and perspectives

Plants produce a variety of metabolites that are essential for plant growth and human health. To fully understand the diversity of metabolites in certain plants, lots of methods have been developed for metabolites detection and data processing. In the data-processing procedure, how to effectively reduce false-positive peaks, analyze large-scale metabolic data, and annotate plant metabolites remains challenging. In this review, we introduce and discuss some prominent methods that could be exploited to solve these problems, including a five-step filtering method for reducing false-positive signals in LC-MS analysis, QPMASS for analyzing ultra-large GC-MS data, and MetDNA for annotating metabolites. The main applications of plant metabolomics in species discrimination, metabolic pathway dissection, population genetic studies, and some other aspects are also highlighted. To further promote the development of plant metabolomics, more effective and integrated methods/platforms for metabolite detection and comprehensive databases for metabolite identification are highly needed. With the improvement of these technologies and the development of genomics and transcriptomics, plant metabolomics will be widely used in many fields.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

Exhaled breath biomarkers of influenza infection and influenza vaccination

Respiratory viral infections are considered a major public health threat, and breath metabolomics can provide new ways to detect and understand how specific viruses affect the human pulmonary system. In this pilot study, we characterized the metabolic composition of human breath for an early diagnosis and differentiation of influenza viral infection, as well as other types of upper respiratory viral infections. We first studied the non-specific effects of planned seasonal influenza vaccines on breath metabolites in healthy subjects after receiving the immunization. We then investigated changes in breath content from hospitalized patients with flu-like symptoms and confirmed upper respiratory viral infection. The exhaled breath was sampled using a custom-made breath condenser, and exhaled breath condensate (EBC) samples were analysed using liquid chromatography coupled to quadruplole-time-of-flight mass spectrometer (LC-qTOF). All metabolomic data was analysed using both targeted and untargeted approaches to detect specific known biomarkers from inflammatory and oxidative stress biomarkers, as well as new molecules associated with specific infections. We were able to find clear differences between breath samples collected before and after flu vaccine administration, together with potential biomarkers that are related to inflammatory processes and oxidative stress. Moreover, we were also able to discriminate samples from patients with flu-related symptoms that were diagnosed with confirmatory respiratory viral panels (RVP). RVP positive and negative differences were identified, as well as differences between specific viruses defined. These results provide very promising information for the further study of the effect of influenza A and other viruses in human systems by using a simple and non-invasive specimen like breath.

MStractor: R Workflow Package for Enhancing Metabolomics Data Pre-Processing and Visualization

Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository.

Metabolomic analysis reveals dose-dependent alteration of maize (Zea mays L.) metabolites and mineral nutrient profiles upon exposure to zerovalent iron nanoparticles

Nanoscale zero-valent iron (nZVI) has been widely applied in the environmental field to degrade organic pollutants. The potential risk posed from nZVI on crop species is not well understood and is critical for sustainable application in the future. In this study, maize (Zea mays L.) plants were cultivated in field soils mixed with nZVI at 0, 50, and 500 mg/kg soil for four weeks. Upon exposure to 500 mg/kg nZVI, ICP-MS results showed that Fe accumulated by roots and translocated to leaves was increased by 36% relative to untreated controls. At 50 mg/kg, root elongation was enhanced by 150-200%; at 500 mg/kg, pigments, lipid peroxidation, and polyphenolic levels in leaves were increased by 12, 87 and 23%, respectively, whereas the accumulation of Al, Ca, and P were decreased by 62.2%, 19.7%, and 13.3%, respectively. A gas chromatography-mass spectrometry (GC-MS) based metabolomics analysis of maize roots revealed that antioxidants and stress signaling-associated metabolites were downregulated at 50 mg/kg, but were upregulated at 500 mg/kg. At 50 mg/kg, the content of glutamate was increased by 11-fold, whereas glutamine was decreased by 99% with respect to controls. Interestingly, eight metabolic pathways were disturbed at 50 mg/kg, but none at 500 mg/kg. This metabolic reprogramming at the lower dose represented potential risks to the health of exposed plants, which could be particularly important although no phenotypic impacts were noted. Overall, metabolites analysis provides a deeper understanding at the molecular level of plant response to nZVI and is a powerful tool for full characterization of risk posed to crop species as part of food safety assessment.

Intraspecific variation in polar and nonpolar metabolite profiles of a threatened Caribbean coral

INTRODUCTION

Research aimed at understanding intraspecific variation among corals could substantially increase understanding of coral biology and improve outcomes of active restoration efforts. Metabolomics is useful for identifying physiological drivers leading to variation among genotypes and has the capacity to improve our selection of candidate corals that express phenotypes beneficial to restoration.

OBJECTIVES

Our study aims to compare metabolomic profiles among known, unique genotypes of the threatened coral Acropora cervicornis. In doing so, we seek information related to the physiological characteristics driving variation among genotypes, which could aid in identifying genets with desirable traits for restoration.

METHODS

We applied proton nuclear magnetic resonance (¹H-NMR) and liquid chromatography-mass spectrometry (LC-MS) to identify and compare metabolomic profiles for seven unique genotypes of A. cervicornis that previously exhibited phenotypic variation in a common garden coral nursery.

RESULTS

Significant variation in polar and nonpolar metabolite profiles was found among A. cervicornis genotypes. Despite difficulties identifying all significant metabolites driving separation among genotypes, our data support previous findings and further suggest metabolomic profiles differ among various genotypes of the threatened species A. cervicornis.

CONCLUSION

The implementation of metabolomic analyses allowed identification of several key metabolites driving separation among genotypes and expanded our understanding of the A. cervicornis metabolome. Although our research is specific to A. cervicornis, these findings have broad relevance for coral biology and active restoration. Furthermore, this study provides specific information on the understudied A. cervicornis metabolome and further confirmation that differences in metabolome structure could drive phenotypic variation among genotypes.

A Practical Guide to Metabolomics Software Development

A growing number of software tools have been developed for metabolomics data processing and analysis. Many new tools are contributed by metabolomics practitioners who have limited prior experience with software development, and the tools are subsequently implemented by users with expertise that ranges from basic point-and-click data analysis to advanced coding. This Perspective is intended to introduce metabolomics software users and developers to important considerations that determine the overall impact of a publicly available tool within the scientific community. The recommendations reflect the collective experience of an NIH-sponsored Metabolomics Consortium working group that was formed with the goal of researching guidelines and best practices for metabolomics tool development. The recommendations are aimed at metabolomics researchers with little formal background in programming and are organized into three stages: (i) preparation, (ii) tool development, and (iii) distribution and maintenance.

Comparison of the Metabolic Profiles in the Plasma and Urine Samples Between Autistic and Typically Developing Boys: A Preliminary Study

Background: Autism spectrum disorder (ASD) is defined as a pervasive developmental disorder which is caused by genetic and environmental risk factors. Besides the core behavioral symptoms, accumulated results indicate children with ASD also share some metabolic abnormalities. Objectives: To analyze the comprehensive metabolic profiles in both of the first-morning urine and plasma samples collected from the same cohort of autistic boys. Methods: In this study, 30 autistic boys and 30 tightly matched healthy control (HC) boys (age range: 2.4~6.7 years) were recruited. First-morning urine and plasma samples were collected and the liquid chromatography-mass spectrometry (LC-MS) was applied to obtain the untargeted metabolic profiles. The acquired data were processed by multivariate analysis and the screened metabolites were grouped by metabolic pathway. Results: Different discriminating metabolites were found in plasma and urine samples. Notably, taurine and catechol levels were decreased in urine but increased in plasma in the same cohort of ASD children. Enriched pathway analysis revealed that perturbations in taurine and hypotaurine metabolism, phenylalanine metabolism, and arginine and proline metabolism could be found in both of the plasma and urine samples. Conclusion: These preliminary results suggest that a series of common metabolic perturbations exist in children with ASD, and confirmed the importance to have a comprehensive analysis of the metabolites in different biological samples to reveal the full picture of the complex metabolic patterns associated with ASD. Further targeted analyses are needed to validate these results in a larger cohort.

Integrative Analysis of Gene-Specific DNA Methylation and Untargeted Metabolomics Data from the ELEMENT Cohort

Epigenetic modifications, such as DNA methylation, influence gene expression and cardiometabolic phenotypes that are manifest in developmental periods in later life, including adolescence. Untargeted metabolomics analysis provide a comprehensive snapshot of physiological processes and metabolism and have been related to DNA methylation in adults, offering insights into the regulatory networks that influence cellular processes. We analyzed the cross-sectional correlation of blood leukocyte DNA methylation with 3758 serum metabolite features (574 of which are identifiable) in 238 children (ages 8-14 years) from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) study. Associations between these features and percent DNA methylation in adolescent blood leukocytes at LINE-1 repetitive elements and genes that regulate early life growth (IGF2, H19, HSD11B2) were assessed by mixed effects models, adjusting for sex, age, and puberty status. After false discovery rate correction (FDR q < 0.05), 76 metabolites were significantly associated with LINE-1 DNA methylation, 27 with HSD11B2, 103 with H19, and 4 with IGF2. The ten identifiable metabolites included dicarboxylic fatty acids (five associated with LINE-1 or H19 methylation at q < 0.05) and 1-octadecanoyl-rac-glycerol (q < 0.0001 for association with H19 and q = 0.04 for association with LINE-1). We then assessed the association between these ten known metabolites and adiposity 3 years later. Two metabolites, dicarboxylic fatty acid 17:3 and 5-oxo-7-octenoic acid, were inversely associated with measures of adiposity (P < .05) assessed approximately 3 years later in adolescence. In stratified analyses, sex-specific and puberty-stage specific (Tanner stage = 2 to 5 vs Tanner stage = 1) associations were observed. Most notably, hundreds of statistically significant associations were observed between H19 and LINE-1 DNA methylation and metabolites among children who had initiated puberty. Understanding relationships between subclinical molecular biomarkers (DNA methylation and metabolites) may increase our understanding of genes and biological pathways contributing to metabolic changes that underlie the development of adiposity during adolescence.

Modeling CO2 exchange and meteorological factors of an apple orchard using partial least square regression

The eddy covariance (EC) technique was used to measure variations of orchard-atmosphere CO₂ exchange, as a function of meteorological variables in an apple orchard in 2016-2017. The annual average CO₂ exchange rate was 2.295 kg m^-2. Excavations and biomass assessments demonstrated that the orchard stored close to 20.6 tC ha^-1 as plant C over a 15-year period. Seasonally, high rates of CO₂ uptake and low CO₂ emissions occurred between May and August and December and March, respectively. The maximum rates of monthly CO₂ exchange were 144.44 and 153.61 gC m^-2 month^-1 in August 2016 and June 2017, respectively. Partial least squares (PLS) regressions were used to analyze the influence of meteorological factors to on CO₂ exchange rates. Temperature and photosynthetic active radiation (PAR) were observed to exert the largest influence on driving variation in CO₂ exchange. The main meteorological factors affecting CO₂ exchange on daily and monthly time scales were soil temperature (T_soil), air temperature (T_a), PAR, below canopy CO₂ concentration (BCC), vapor pressure deficit (VPD), and soil water content at 50 cm (SWC_50cm). The regression model equation describing CO₂ exchange included T_a, VPD, precipitation (PPT), and sunshine duration (SD), as significant variables. This model curve fitting explains over 80% of the variation in CO₂ exchange. This study provides CO₂ exchange characteristics and a model equation capable of predicting CO₂ exchange of an apple orchard. Graphical Abstract.

Mass spectrometry-based lipidomics in food science and nutritional health: A comprehensive review

With the advance in science and technology as well as the improvement of living standards, the function of food is no longer just to meet the needs of survival. Food science and its associated nutritional health issues have been increasingly debated. Lipids, as complex metabolites, play a key role both in food and human health. Taking advantages of mass spectrometry (MS) by combining its high sensitivity and accuracy with extensive selective determination of all lipid classes, MS-based lipidomics has been employed to resolve the conundrum of addressing both qualitative and quantitative aspects of high-abundance and low-abundance lipids in complex food matrices. In this review, we systematically summarize current applications of MS-based lipidomics in food field. First, common MS-based lipidomics procedures are described. Second, the applications of MS-based lipidomics in food science, including lipid composition characterization, adulteration, traceability, and other issues, are discussed. Third, the application of MS-based lipidomics for nutritional health covering the influence of food on health and disease is introduced. Finally, future research trends and challenges are proposed. MS-based lipidomics plays an important role in the field of food science, promoting continuous development of food science and integration of food knowledge with other disciplines. New methods of MS-based lipidomics have been developed to improve accuracy and sensitivity of lipid analysis in food samples. These developments offer the possibility to fully characterize lipids in food samples, identify novel functional lipids, and better understand the role of food in promoting healt.

Adipogenic effects of prenatal exposure to bisphenol S (BPS) in adult F1 male mice

Bisphenol S (BPS) has been increasingly used as a substitute for bisphenol A (BPA), a known endocrine disruptor. Early-life exposure to BPA affects fetal development and the risk of obesity in adolescence and adulthood. However, the effects of fetal exposure BPS in later life are unknown. This study aimed to investigate the effects of prenatal BPS exposure on adiposity in adult F1 mice. Pregnant C57BL/6 N mice were exposed to BPS (0, 0.05, 0.5, 5, and 50 mg/kg/d) via drinking water from gestation day 9 until delivery. Thereafter, two groups of offspring (6 weeks old) were either administered a standard diet (STD) or a high-fat diet (HFD) for 4 weeks until euthanasia. The body weight and gonadal white adipose tissue (gWAT) mass were determined, and the energy expenditure for the adiposity phenotype was computed especially for male mice, followed by histological analysis of the gWAT. Thereafter, the expression levels of adipogenic marker genes (Pparg, Cebpa, Fabp4, Lpl, and Adipoq) were analyzed in the gWAT via reverse-transcription PCR analysis. BPS-exposed male mice displayed apparent gWAT hypertrophy, consistent with the significant increase in adipocyte size in the gWAT and upregulation of Pparg and its direct target genes among HFD mice in comparison with the control mice. These results suggest that prenatal BPS exposure potentially increases the susceptibility to HFD-induced adipogenesis in male adult mice.

Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions

PURPOSE OF REVIEW

Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer.

RECENT FINDINGS

Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.

Integrated LC-MS and GC-MS-based untargeted metabolomics studies of the effect of azadirachtin on Bactrocera dorsalis larvae

Azadirachtin exhibits excellent bioactivities against several hundred arthropods. However, current knowlege of its biochemical effect on B. dorsalis larvae is not deep enough. In this study, integrated LC-MS and GC-MS-based untargeted metabolomics were used to analyze the changes of endogenous metabolites and the biochemical effects of azadirachtin on B. dorsalis larvae. Azadirachtin has excellent bioactivities against B. dorsalis larvae in this study, leading to a longer developmental duration, lower survival rate, and low pupa weight. The effect of azadirachtin was investigated on a total of 22 and 13 differentially abundant metabolites in the LC-MS and GC-MS-based metabolomics results, are selected respectively. Pathway analysis indicated that 14 differentially enriched metabolic pathways, including seven influential pathways, are worthy of attention. Further integrated key metabolic pathway analysis showed that histidine metabolism, D-glutamine and D-glutamate metabolism, biotin metabolism, ascorbate and aldarate metabolism, pentose and glucuronate interconversions, and alanine, aspartate and glutamate metabolism in B. dorsalis larvae are significantly relevant pathways affected by azadirachtin. Although extrapolating the bioactivity results in this study to the practical project of B. dorsalis pest management in the field has limitations, it was found that azadirachtin has a significant effect on the primary metabolism of B. dorsalis larvae.

Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study

BACKGROUND

LC-MS technology makes it possible to measure the relative abundance of numerous molecular features of a sample in single analysis. However, especially non-targeted metabolite profiling approaches generate vast arrays of data that are prone to aberrations such as missing values. No matter the reason for the missing values in the data, coherent and complete data matrix is always a pre-requisite for accurate and reliable statistical analysis. Therefore, there is a need for proper imputation strategies that account for the missingness and reduce the bias in the statistical analysis.

RESULTS

Here we present our results after evaluating nine imputation methods in four different percentages of missing values of different origin. The performance of each imputation method was analyzed by Normalized Root Mean Squared Error (NRMSE). We demonstrated that random forest (RF) had the lowest NRMSE in the estimation of missing values for Missing at Random (MAR) and Missing Completely at Random (MCAR). In case of absent values due to Missing Not at Random (MNAR), the left truncated data was best imputed with minimum value imputation. We also tested the different imputation methods for datasets containing missing data of various origin, and RF was the most accurate method in all cases. The results were obtained by repeating the evaluation process 100 times with the use of metabolomics datasets where the missing values were introduced to represent absent data of different origin.

CONCLUSION

Type and rate of missingness affects the performance and suitability of imputation methods. RF-based imputation method performs best in most of the tested scenarios, including combinations of different types and rates of missingness. Therefore, we recommend using random forest-based imputation for imputing missing metabolomics data, and especially in situations where the types of missingness are not known in advance.

Potential biomonitoring of atmospheric carbon dioxide in Coffea arabica leaves using near-infrared spectroscopy and partial least squares discriminant analysis

The potencial of Coffea arabica leaves as bioindicators of atmospheric carbon dioxide (CO₂) was evaluated in a free-air carbon dioxide enrichment (FACE) experiment by using near-infrared reflectance (NIR) spectroscopy for direct analysis and partial least squares discriminant analysis (PLS-DA). A supervised classification model was built and validated from the spectra of coffee leaves grown under elevated and current CO₂ levels. PLS-DA allowed correct test set classification of 92% of the elevated-CO₂ level leaves and 100% of the current-CO₂ level leaves. The spectral bands accounting for the discrimination of the elevated-CO₂ leaves were at 1657 and 1698 nm, as indicated by the variable importance in the projection (VIP) score together with the regression coefficients. Seven months after suspension of enriched CO₂, returning to current-CO₂ levels, new spectral measurements were made and subjected to PLS-DA analysis. The predictive model correctly classified all leaves as grown under current-CO₂ levels. The fingerprints suggest that after suspension of elevated-CO₂, the spectral changes observed previously disappeared. The recovery could be triggered by two reasons: the relief of the stress stimulus or the perception of a return of favorable conditions. In addition, the results demonstrate that NIR spectroscopy can provide a rapid, nondestructive, and environmentally friendly method for biomonitoring leaves suffering environmental modification. Finally, C. arabica leaves associated with NIR and mathematical models have the potential to become a good biomonitoring system.

Mass spectrometry-based urinary metabolomics for the investigation on the mechanism of action of Eleutherococcus senticosus (Rupr. & Maxim.) Maxim. leaves against ischemic stroke in rats

ETHNOPHARMACOLOGICAL RELEVANCE

As a traditional Chinese medicine, Eleutherococcus senticosus (Rupr. & Maxim.) Maxim. leaves (ESL) can treat ischemic, neurasthenia, and hypertension diseases. However, only few studies have been conducted on the mechanism of action of ESL for ischemic disease treatment.

AIM OF THE STUDY

This study aimed to discover the potential biomarkers in the rats caused by ischemic stroke and build a gene-enzyme-biomarker network to explore the mechanism of ESL treatment on ischemic stroke further.

MATERIALS AND METHODS

The urinary metabolomics strategy was developed by combining UPLC-Q-TOF/MS with multivariate data analysis. The gene-enzyme-biomarker network was built by Cytoscape 3.6.0 on the basis of the potential biomarkers filtered out via urinary metabolomic analysis. Then, the potential target enzymes of ESL in the treatment of ischemic stroke were selected for further validation analysis via the ELISA kits.

RESULTS

A total of 42 biomarkers associated with ischemic stroke have been identified, among which 38 species can be adjusted by ESL, including 5'-methylthioadenosine, prostaglandin A2, l-methionine, aldosterone, 11b-hydroxyprogesterone, prostaglandin E3, dehydroepiandrosterone, taurine, 5-methoxyindoleacetate, and p-cresol glucuronide. These biomarkers were involved in several metabolic pathways, including taurine and hypotaurine, arachidonic acid, cysteine and methionine, steroid hormone biosynthesis, tryptophan, and tyrosine metabolism pathways. The gene-enzyme-biomarker network was built, and three predicted target proteins, including cyclooxygenase-2 (COX-2), monoamine oxidase (MAO), and nitric oxide synthase (NOS), were selected as the potential target enzymes for ESL in ischemic stroke treatment.

CONCLUSIONS

All results showed that ESL can play a therapeutic role in treating ischemic stroke through different pathways. This study will provide an overall view of the mechanism underlying the action of ESL against ischemic stroke.

Targeted neurotransmitter metabolomics profiling of oleanolic acid in the treatment of spontaneously hypertensive rats

Essential hypertension (EH) is a prevalent chronic medical condition and a major risk factor for cardiovascular morbidity and mortality. Neurotransmitters are involved in the physiological process of blood pressure regulation in the body. Studies have shown that oleanolic acid (OA) can effectively regulate neurotransmitter-related metabolic disorders caused by EH, but the mechanism is still unclear. Here, we studied the neurotransmitter metabolic profiles in five brain regions by targeted metabolomics approaches in spontaneously hypertensive rats (SHRs) treated with OA and vehicle. Samples from five brain regions (hippocampus, striatum, hypothalamus, temporal lobe, and frontal lobe) were collected from the control group, the spontaneously hypertensive rat (SHR) group, and the OA group. Targeted metabolomics based on UPLC-Q-Exactive-MS was employed to characterize the dramatically changed neurotransmitters in the brain regions of SHRs treated with OA and vehicle. The expressions of the key enzymes involved in the neurotransmitter metabolism were detected by the reverse transcription-polymerase chain reaction (RT-PCR). The metabolomic profiles of SHRs pre-protected by OA were significantly different from those of unprotected SHRs. A total of 18 neurotransmitters could be confirmed as significantly altered metabolites, which were involved in tyrosine and glutamate metabolism as well as other pathways. The results showing seven key enzymes in neurotransmitter metabolism further validated the changes in the metabolic pathways. OA could effectively restore tyrosine metabolism in the striatum and hypothalamus, glutamate metabolism in the hippocampus, striatum and temporal lobe, cholinergic metabolism in the striatum, and histidine metabolism in the hypothalamus due to its inhibition of inflammatory reactions, structural damage of the neuronal cells, and increase in sedative activity. This study indicated that brain region-targeted metabolomics can provide a powerful tool to further investigate the possible mechanism of OA in EH.

Saliva and urine metabolic profiling reveals altered amino acid and energy metabolism in male farmers exposed to pesticides in Madhya Pradesh State, India

Globally, the human population is exposed to low doses of pesticides due to its extensive use in agriculture. The chronic exposure to pesticides can lead to cancer, depression, anxiety, Parkinson's and Alzheimer's diseases etc. Here, we have made an attempt to use mass spectrometry based metabolomics to investigate the metabolic perturbations induced by the pesticides in the urine and saliva samples of farmers from the Madhya Pradesh State of India. The study was aimed to establish non-invasive matrices like urine and saliva as alternative diagnostic matrices to the occupational exposure studies. Saliva and urine samples were collected from 51 pesticides applicators and acquired metabolic profiles of urine and saliva samples using gas chromatography-mass spectrometry (GC-MS). Multivariate pattern recognition and pathway analysis were used to analyze and interpret the data. Investigation of endogenous metabolic profiles revealed remarkable discrimination in both saliva and urine samples of the exposed population strongly suggesting the changes in metabolic composition within the identified metabolites (for urine samples: accuracy 0.9766, R² = 0.9130, Q² = 0.8703; for saliva samples, an accuracy of 0.9961, R² = 0.9698, Q² = 0.9637). Thirteen metabolites of urine samples and sixteen metabolites of saliva samples were identified as differential metabolites specific to pesticide exposure. Pathway analysis of differential metabolites revealed that amino acid metabolism, energy metabolism (glycolysis and TCA cycle) and glutathione metabolism (oxidative stress) were found to affect in pesticide exposed population. The present study suggested that GC-MS based metabolomics can help to reveal the metabolic perturbations in human population after pesticides exposure.

Metabolomic profiles differ among unique genotypes of a threatened Caribbean coral

Global threats to reefs require urgent efforts to resolve coral attributes that affect survival in a changing environment. Genetically different individuals of the same coral species are known to exhibit different responses to the same environmental conditions. New information on coral physiology, particularly as it relates to genotype, could aid in unraveling mechanisms that facilitate coral survival in the face of stressors. Metabolomic profiling detects a large subset of metabolites in an organism, and, when linked to metabolic pathways, can provide a snapshot of an organism’s physiological state. Identifying metabolites associated with desirable, genotype-specific traits could improve coral selection for restoration and other interventions. A key step toward this goal is determining whether intraspecific variation in coral metabolite profiles can be detected for species of interest, however little information exists to illustrate such differences. To address this gap, we applied untargeted ¹H-NMR and LC-MS metabolomic profiling to three genotypes of the threatened coral Acropora cervicornis. Both methods revealed distinct metabolite “fingerprints” for each genotype examined. A number of metabolites driving separation among genotypes were identified or putatively annotated. Pathway analysis suggested differences in protein synthesis among genotypes. For the first time, these data illustrate intraspecific variation in metabolomic profiles for corals in a common garden. Our results contribute to the growing body of work on coral metabolomics and suggest future work could identify specific links between phenotype and metabolite profile in corals.

Potato native and wound periderms are differently affected by down-regulation of FHT, a suberin feruloyl transferase

Potato native and wound healing periderms contain an external multilayered phellem tissue (potato skin) consisting of dead cells whose cell walls are impregnated with suberin polymers. The phellem provides physical and chemical barriers to tuber dehydration, heat transfer, and pathogenic infection. Previous RNAi-mediated gene silencing studies in native periderm have demonstrated a role for a feruloyl transferase (FHT) in suberin biosynthesis and revealed how its down-regulation affects both chemical composition and physiology. To complement these prior analyses and to investigate the impact of FHT deficiency in wound periderms, a bottom-up methodology has been used to analyze soluble tissue extracts and solid polymers concurrently. Multivariate statistical analysis of LC-MS and GC-MS data, augmented by solid-state NMR and thioacidolysis, yields two types of new insights: the chemical compounds responsible for contrasting metabolic profiles of native and wound periderms, and the impact of FHT deficiency in each of these plant tissues. In the current report, we confirm a role for FHT in developing wound periderm and highlight its distinctive features as compared to the corresponding native potato periderm.

Targeted and Untargeted Metabolic Profiling of Wild Grassland Plants identifies Antibiotic and Anthelmintic Compounds Targeting Pathogen Physiology, Metabolism and Reproduction

Plants traditionally used by farmers to manage livestock ailments could reduce reliance on synthetic antibiotics and anthelmintics but in many cases their chemical composition is unknown. As a case study, we analyzed the metabolite profiles of 17 plant species and 45 biomass samples from agricultural grasslands in England using targeted and untargeted metabolite profiling by liquid-chromatography mass spectrometry. We identified a range of plant secondary metabolites, including 32 compounds with known antimicrobial/anthelmintic properties which varied considerably across the different plant samples. These compounds have been shown previously to target multiple aspects of pathogen physiology and metabolism in vitro and in vivo, including inhibition of quorum sensing in bacteria and egg viability in nematodes. The most abundant bioactive compounds were benzoic acid, myricetin, p-coumaric acid, rhamnetin, and rosmarinic acid. Four wild plants (Filipendula ulmaria (L.) Maxim., Prunella vulgaris L., Centuarea nigra L., and Rhinanthus minor L.) and two forage legumes (Medicago sativa L., Trifolium hybridium L.) contained high levels of these compounds. Forage samples from native high-diversity grasslands had a greater abundance of medicinal compounds than samples from agriculturally improved grasslands. Incorporating plants with antibiotic/anthelmintic compounds into livestock feeds may reduce global drug-resistance and preserve the efficacy of last-resort drugs.

Network Marker Selection for Untargeted LC-MS Metabolomics Data

Untargeted metabolomics using high-resolution liquid chromatography-mass spectrometry (LC-MS) is becoming one of the major areas of high-throughput biology. Functional analysis, that is, analyzing the data based on metabolic pathways or the genome-scale metabolic network, is critical in feature selection and interpretation of metabolomics data. One of the main challenges in the functional analyses is the lack of the feature identity in the LC-MS data itself. By matching mass-to-charge ratio (m/z) values of the features to theoretical values derived from known metabolites, some features can be matched to one or more known metabolites. When multiple matchings occur, in most cases only one of the matchings can be true. At the same time, some known metabolites are missing in the measurements. Current network/pathway analysis methods ignore the uncertainty in metabolite identification and the missing observations, which could lead to errors in the selection of significant subnetworks/pathways. In this paper, we propose a flexible network feature selection framework that combines metabolomics data with the genome-scale metabolic network. The method adopts a sequential feature screening procedure and machine learning-based criteria to select important subnetworks and identify the optimal feature matching simultaneously. Simulation studies show that the proposed method has a much higher sensitivity than the commonly used maximal matching approach. For demonstration, we apply the method on a cohort of healthy subjects to detect subnetworks associated with the body mass index (BMI). The method identifies several subnetworks that are supported by the current literature, as well as detects some subnetworks with plausible new functional implications. The R code is available at http://web1.sph.emory.edu/users/tyu8/MSS.

Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies

BACKGROUND

High throughput metabolomics makes it possible to measure the relative abundances of numerous metabolites in biological samples, which is useful to many areas of biomedical research. However, missing values (MVs) in metabolomics datasets are common and can arise due to both technical and biological reasons. Typically, such MVs are substituted by a minimum value, which may lead to different results in downstream analyses.

RESULTS

Here we present a modified version of the K-nearest neighbor (KNN) approach which accounts for truncation at the minimum value, i.e., KNN truncation (KNN-TN). We compare imputation results based on KNN-TN with results from other KNN approaches such as KNN based on correlation (KNN-CR) and KNN based on Euclidean distance (KNN-EU). Our approach assumes that the data follow a truncated normal distribution with the truncation point at the detection limit (LOD). The effectiveness of each approach was analyzed by the root mean square error (RMSE) measure as well as the metabolite list concordance index (MLCI) for influence on downstream statistical testing. Through extensive simulation studies and application to three real data sets, we show that KNN-TN has lower RMSE values compared to the other two KNN procedures as well as simpler imputation methods based on substituting missing values with the metabolite mean, zero values, or the LOD. MLCI values between KNN-TN and KNN-EU were roughly equivalent, and superior to the other four methods in most cases.

CONCLUSION

Our findings demonstrate that KNN-TN generally has improved performance in imputing the missing values of the different datasets compared to KNN-CR and KNN-EU when there is missingness due to missing at random combined with an LOD. The results shown in this study are in the field of metabolomics but this method could be applicable with any high throughput technology which has missing due to LOD.

Navigating freely-available software tools for metabolomics analysis

INTRODUCTION

The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools.

OBJECTIVES

To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics.

METHODS

The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC-MS, GC-MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for.

RESULTS

A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary.

CONCLUSION

This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools' abilities to perform specific data analysis tasks e.g. peak picking.

Metabolic phenotyping for discovery of urinary biomarkers of diet, xenobiotics and blood pressure in the INTERMAP Study: an overview

The etiopathogenesis of cardiovascular diseases (CVDs) is multifactorial. Adverse blood pressure (BP) is a major independent risk factor for epidemic CVD affecting ~40% of the adult population worldwide and resulting in significant morbidity and mortality. Metabolic phenotyping of biological fluids has proven its application in characterizing low-molecular-weight metabolites providing novel insights into gene-environmental-gut microbiome interaction in relation to a disease state. In this review, we synthesize key results from the INTERnational study of MAcro/micronutrients and blood Pressure (INTERMAP) Study, a cross-sectional epidemiologic study of 4680 men and women aged 40-59 years from Japan, the People's Republic of China, the United Kingdom and the United States. We describe the advancements we have made regarding the following: (1) analytical techniques for high-throughput metabolic phenotyping; (2) statistical analyses for biomarker identification; (3) discovery of unique food-specific biomarkers; and (4) application of metabolome-wide association studies to gain a better understanding into the molecular mechanisms of cross-cultural and regional BP differences.

Metabolomics of aging assessed in individual parasitoid wasps

Metabolomics studies of low-biomass organisms, such as small insects, have previously relied on the pooling of biological samples to overcome detection limits, particularly using NMR. We show that the differentiation of metabolite profiles of individual 1 mg parasitoid wasps of different ages is possible when using a modified sample preparation and a combination of untargeted NMR and LC-MS based metabolomics. Changes were observed between newly emerged and older wasps in glycerolipids, amino acids and circulatory sugars. This advance in chemical profiling has important implications for the study of the behaviour and ecology of parasitoids and many other species of small organisms because predictions and observations are typically made at the level of the individual. Thus, the metabolomic state of low-biomass individuals can now be related to their behaviour and ecological performance. We discuss specifically the utility of age-related metabolomic profiling but our new approach can be applied to a wide range of biological research.

Evaluation of the anti-inflammatory effects of volatile oils from processed products of Angelica sinensis radix by GC-MS-based metabolomics

ETHNOPHARMACOLOGICAL RELEVANCE

Roots of Angelica sinensis (Oliv.) Diels (AS) a commonly used herbal, always act as an anti-inflammatory drug in Chinese traditional therapy. In clinical use, AS is always processed before being used for the reason that processing can increase its therapeutic effect. Recent studies have shown that volatile oil of AS (VOAS), an important component in AS, has evident anti-inflammatory activities.

AIM OF THE STUDY

In this study, our aim is to evaluate the anti-inflammatory effects of volatile oils from processed products of AS.

MATERIALS AND METHODS

In this paper, volatile oils from stir-fried AS (C-VOAS), parched AS with alcohol (J-VOAS), parched AS with soil (T-VOAS), and parched AS with sesame oil (Y-VOAS) were applied to intervene the carrageenan-induced acute inflammation model rats. GC-MS based metabolomics was utilized to determine different metabolites in the inflammatory exudate and plasma samples.

RESULTS

The results showed that VOASs could significantly inhibit the release of PGE2, HIS, 5-HT and TNF-α, among which C-VOAS and J-VOAS expressed better effect. Otherwise, 14 potential biomarkers were identified respectively in inflammatory exudate and plasma, which changed highly significantly (P<0.01) in C-VOAS and J-VOAS groups.

CONCLUSIONS

We inferred that the anti-inflammatory effect of C-VOAS and J-VOAS were superior to other VOASs.

Citrate Accumulation-Related Gene Expression and/or Enzyme Activity Analysis Combined With Metabolomics Provide a Novel Insight for an Orange Mutant

'Hong Anliu' (HAL, Citrus sinensis cv. Hong Anliu) is a bud mutant of 'Anliu' (AL), characterized by a comprehensive metabolite alteration, such as lower accumulation of citrate, high accumulation of lycopene and soluble sugars in fruit juice sacs. Due to carboxylic acid metabolism connects other metabolite biosynthesis and/or catabolism networks, we therefore focused analyzing citrate accumulation-related gene expression profiles and/or enzyme activities, along with metabolic fingerprinting between 'HAL' and 'AL'. Compared with 'AL', the transcript levels of citrate biosynthesis- and utilization-related genes and/or the activities of their respective enzymes such as citrate synthase, cytosol aconitase and ATP-citrate lyase were significantly higher in 'HAL'. Nevertheless, the mitochondrial aconitase activity, the gene transcript levels of proton pumps, including vacuolar H(+)-ATPase, vacuolar H(+)-PPase, and the juice sac-predominant p-type proton pump gene (CsPH8) were significantly lower in 'HAL'. These results implied that 'HAL' has higher abilities for citrate biosynthesis and utilization, but lower ability for the citrate uptake into vacuole compared with 'AL'. Combined with the metabolites-analyzing results, a model was then established and suggested that the reduction in proton pump activity is the key factor for the low citrate accumulation and the comprehensive metabolite alterations as well in 'HAL'.

Identifying the metabolic perturbations in earthworm induced by cypermethrin using gas chromatography-mass spectrometry based metabolomics

Globally, cypermethrin is one of the most widely used synthetic pyrethroid for agricultural and domestic purposes. Most part of the pesticides used in the agriculture ends up as residues in the soil, making soil dwelling organisms, especially earthworms more susceptible to pesticide intoxication. Cypermethrin is known to be a neurotoxicant to many model organisms, including mammals and insects, but such type of toxicity evidence is not available for invertebrate systems like earthworms. In the present work, metabolomics based approach was utilized to identify the toxic mechanism of action of cypermethrin on earthworm (Metaphire posthuma) and these were exposed to sub-lethal concentrations of cypermethrin such as 2.5 mg/kg, 5 mg/kg, 10 mg/kg and 20 mg/kg (1/40^th, 1/20^th, 1/10^th and 1/5^th of LC₅₀, respectively) for fourteen days. The results revealed that 22 metabolites (mainly fatty acids, sugars and amino acids) were shown significant responses in the exposed earthworms and these responses are dose dependent. It is proposed that mainly carbohydrate and fatty acids in neural system metabolism was disturbed. Overall, the results provided that metabolomics can be an effective tool to understand the effects of cypermethrin on the metabolic responses of earthworm Metaphire posthuma.

A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis

Highlights

Developed a data preprocessing strategy to cope with missing values and mask effects in data analysis from high variation of abundant metabolites.

A new method- ‘x-VAST’ was developed to amend the measurement deviation enlargement.

Applying the above strategy, several low abundant masked differential metabolites were rescued.

Metabolomics is a booming research field. Its success highly relies on the discovery of differential metabolites by comparing different data sets (for example, patients vs. controls). One of the challenges is that differences of the low abundant metabolites between groups are often masked by the high variation of abundant metabolites. In order to solve this challenge, a novel data preprocessing strategy consisting of three steps was proposed in this study. In step 1, a ‘modified 80%’ rule was used to reduce effect of missing values; in step 2, unit-variance and Pareto scaling methods were used to reduce the mask effect from the abundant metabolites. In step 3, in order to fix the adverse effect of scaling, stability information of the variables deduced from intensity information and the class information, was used to assign suitable weights to the variables. When applying to an LC/MS based metabolomics dataset from chronic hepatitis B patients study and two simulated datasets, the mask effect was found to be partially eliminated and several new low abundant differential metabolites were rescued.

Principles and practice of lipidomics

The technical advances in mass spectrometry, particularly the development of (ultra)-high-resolution/mass accuracy measurement capabilities in combination with refinement of soft ionization techniques, have increased the application and success of lipidomics to answer biological questions in relation to lipid metabolism. Together with other omics technologies, lipidomics has become an important tool to practice systems biology as lipids comprise a very significant part of the metabolome and play pleiotropic roles in cellular functions. As an increasing number of disorders are linked to lipid metabolism, lipidomics is used to search for biomarkers, understand disease mechanism and follow the efficacy of therapeutic options. This review provides a first introduction to the major methodological strategies currently used for mass spectrometry-based lipidomics and associated data pre-processing and analysis.

Accounting for undetected compounds in statistical analyses of mass spectrometry 'omic studies

Mass spectrometry is an important high-throughput technique for profiling small molecular compounds in biological samples and is widely used to identify potential diagnostic and prognostic compounds associated with disease. Commonly, this data generated by mass spectrometry has many missing values resulting when a compound is absent from a sample or is present but at a concentration below the detection limit. Several strategies are available for statistically analyzing data with missing values. The accelerated failure time (AFT) model assumes all missing values result from censoring below a detection limit. Under a mixture model, missing values can result from a combination of censoring and the absence of a compound. We compare power and estimation of a mixture model to an AFT model. Based on simulated data, we found the AFT model to have greater power to detect differences in means and point mass proportions between groups. However, the AFT model yielded biased estimates with the bias increasing as the proportion of observations in the point mass increased while estimates were unbiased with the mixture model except if all missing observations came from censoring. These findings suggest using the AFT model for hypothesis testing and mixture model for estimation. We demonstrated this approach through application to glycomics data of serum samples from women with ovarian cancer and matched controls.

Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach

MOTIVATION

Peak detection is a key step in the preprocessing of untargeted metabolomics data generated from high-resolution liquid chromatography-mass spectrometry (LC/MS). The common practice is to use filters with predetermined parameters to select peaks in the LC/MS profile. This rigid approach can cause suboptimal performance when the choice of peak model and parameters do not suit the data characteristics.

RESULTS

Here we present a method that learns directly from various data features of the extracted ion chromatograms (EICs) to differentiate between true peak regions from noise regions in the LC/MS profile. It utilizes the knowledge of known metabolites, as well as robust machine learning approaches. Unlike currently available methods, this new approach does not assume a parametric peak shape model and allows maximum flexibility. We demonstrate the superiority of the new approach using real data. Because matching to known metabolites entails uncertainties and cannot be considered a gold standard, we also developed a probabilistic receiver-operating characteristic (pROC) approach that can incorporate uncertainties.

AVAILABILITY AND IMPLEMENTATION

The new peak detection approach is implemented as part of the apLCMS package available at http://web1.sph.emory.edu/apLCMS/ CONTACT: tyu8@emory.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MS-based metabolomics facilitates the discovery of in vivo functional small molecules with a diversity of biological contexts

In vivo small molecules as necessary intermediates are involved in numerous critical metabolic pathways and biological processes associated with many essential biological functions and events. There is growing evidence that MS-based metabolomics is emerging as a powerful tool to facilitate the discovery of functional small molecules that can better our understanding of development, infection, nutrition, disease, toxicity, drug therapeutics, gene modifications and host-pathogen interaction from metabolic perspectives. However, further progress must still be made in MS-based metabolomics because of the shortcomings in the current technologies and knowledge. This technique-driven review aims to explore the discovery of in vivo functional small molecules facilitated by MS-based metabolomics and to highlight the analytic capabilities and promising applications of this discovery strategy. Moreover, the biological significance of the discovery of in vivo functional small molecules with different biological contexts is also interrogated at a metabolic perspective.

Metabolomics network characterization of resuscitation after normocapnic hypoxia in a newborn piglet model supports the hypothesis that room air is better

Perinatal asphyxia is attributed to hypoxia and/or ischemia around the time of birth and may lead to multiorgan dysfunction. Aim of this research article is to investigate whether different metabolomic profiles occurred according to oxygen concentration administered at resuscitation. In order to perform the experiment, forty newborn piglets were subjected to normocapnic hypoxia and reoxygenation and were randomly allocated in 4 groups resuscitated with different oxygen concentrations, 18%, 21%, 40%, and 100%, respectively. Urine metabolic profiles at baseline and at hypoxia were analysed by ¹H-NMR spectroscopy and metabolites were also identified by multivariate statistical analysis. Metabolic pathways associations were also built up by ingenuity pathway analysis (IPA). Bioinformatics analysis of metabolites characterized the effect of metabolism in the 4 groups; it showed that the 21% of oxygen is the most “physiological” and appropriate concentration to be used for resuscitation. Our data indicate that resuscitation with 21% of oxygen seems to be optimal in terms of survival, rapidity of resuscitation, and metabolic profile in the present animal model. These findings need to be confirmed with metabolomics in human and, if so, the knowledge of the perinatal asphyxia condition may significantly improve.

Assessing unintended effects of a mammary-specific transgene at the whole animal level in host and non-target animals

Risk assessment in transgenic plants is intrinsically different than that for transgenic animals; however both require the verification of proper transgene function and in conjunction, an estimate of any unintended effects caused by expression of the transgene. This work was designed to gather data regarding methodologies to detect pleiotropic effects at the whole animal level using a line of transgenic goats that produce the antimicrobial protein human lysozyme (hLZ) in their milk with the goal of using the milk to treat childhood diarrhea. Metabolomics was used to determine the serum metabolite profile of both the host (lactating does) and non-target organism (kid goats raised on control or hLZ milk) prior to weaning (60 days), at weaning (90 days) and 1 month post-weaning (120 days). In addition, intestinal histology of the kid goats was also carried out. Histological analysis of intestinal segments of the pre-weaning group revealed significantly wider duodenal villi (p = 0.014) and significantly longer villi (p = 0.028) and deeper crypts (p = 0.030) in the ileum of kid goats consuming hLZ milk. Serum metabolomics was capable of detecting differences over time but revealed no significant differences in metabolites between control and hLZ fed kids after correction for false discovery rate. Serum metabolomics of control or hLZ lactating does showed only one significant difference in an unknown metabolite (q = 0.0422). The results as a whole indicate that consumption of hLZ milk results in positive or insignificant intestinal morphology and metabolic changes. This work contributes to the establishment of the safety and durability of the hLZ mammary-specific transgene.