Metabolomics by numbers: acquiring and understanding global metabolite data

Current Challenges in Plant Eco-Metabolomics

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant⁻organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.

Method selectivity evaluation using the co-feature ratio in LC/MS metabolomics: Comparison of HILIC stationary phase performance for the analysis of plasma, urine and cell extracts

Evaluation of the chromatographic separation in metabolomics studies has primarily been done using preselected sets of standards or by counting the number of detected features. An alternative approach is to calculate each feature's co-feature ratio, which is a combined selectivity measurement for the separation (i.e. extent of co-elution) and the MS-signal (i.e. adduct formation and in-source fragmentation). The aim of this study was to demonstrate how the selectivity of different HILIC stationary phases can be evaluated using the co-feature ratio approach. The study was based on three sample types; plasma, urine and cell extracts. Samples were analyzed on an UHPLC-ESI-Q-ToF system using an amide, a bare silica and a sulfobetaine stationary phase. For each feature, a co-feature ratio was calculated and used for multivariate analysis of the selectivity differences between the three stationary phases. Unsupervised PCA models indicated that the co-feature ratios were highly dependent on type of stationary phase. For several metabolites a 15-30 fold difference in the co-feature ratio were observed between the stationary phases. Observed selectivity differences related primarily to the retention patterns of unwanted matrix components such as inorganic salts (detected as salt clusters), glycerophospholipids, and polyethylene glycols. These matrix components affected the signal intensity of co-eluting metabolites by interfering with the ionization efficiency and/or their adduct formation. Furthermore, the retention pattern of these matrix components had huge influence on the number of detected features. The co-feature ratio approach has successfully been applied for evaluation of the selectivity performance of three HILIC stationary phases. The co-feature ratio could therefore be used in metabolomics for developing selective methods fit for their purpose, thereby avoiding generic analytical approaches, which are often biased, as type and amount of interfering matrix components are metabolome dependent.

Mass spectral fragmentation of trimethylsilylated small molecules

Mass spectrometry-based untargeted metabolomics detects many peaks that cannot be identified. While advances have been made for automatic structure annotations in LC-electrospray-MS/MS, no open source solutions are available for hard electron ionization used in GC-MS. In metabolomics, most compounds bear moieties with acidic protons, for example, amino, hydroxyl, or carboxyl groups. Such functional groups increase the boiling points of metabolites too much for use in GC-MS. Hence, in GC-MS-focused metabolomics, derivatization of these groups is essential and has been employed since the 1960s. Specifically, trimethylsilylation is known as mild and universal method for GC-MS analysis. Here, we comprehensively compile accurate mass fragmentation rules and pathways of trimethylsilylated small molecules from 80 research articles over the past 5 decades, including diagnostic fragment ions, neutral losses, and typical ion ratios, for alcohols, carboxylic acids, amines, amino acids, sugars, steroids, thiols, and phosphates. These fragmentation rules were subsequently validated by specificity and sensitivity assessments using the NIST 14 nominal mass library and a new in-house GC-QTOF MS library containing 589 accurate mass spectra. From 556 tested fragmentation patterns, 228 rules yielded true positive hits within 4 mDa mass accuracy. These rules can be applied to assign substructures for mass spectra computation and unknown identification. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:245-257, 2018.

Gut metabolome meets microbiome: A methodological perspective to understand the relationship between host and microbe

It is well established that gut microbes and their metabolic products regulate host metabolism. The interactions between the host and its gut microbiota are highly dynamic and complex. In this review we present and discuss the metabolomic strategies to study the gut microbial ecosystem. We highlight the metabolic profiling approaches to study faecal samples aimed at deciphering the metabolic product derived from gut microbiota. We also discuss how metabolomics data can be integrated with metagenomics data derived from gut microbiota and how such approaches may lead to better understanding of the microbial functions. Finally, the emerging approaches of genome-scale metabolic modelling to study microbial co-metabolism and host-microbe interactions are highlighted.

Molecular Signatures of High-Grade Cervical Lesions

Cervical cancer is the fourth most common neoplasia in women and the infection with human papilloma virus (HPV) is its necessary cause. Screening methods, currently based on cytology and HPV DNA tests, display low specificity/sensitivity, reducing the efficacy of cervical cancer screening programs. Herein, molecular signatures of cervical cytologic specimens revealed by liquid chromatography-mass spectrometry (LC-MS), were tested in their ability to provide a metabolomic screening for cervical cancer. These molecules were tested whether they could clinically differentiate insignificant HPV infections from precancerous lesions. For that, high-grade squamous intraepithelial lesions (HSIL)-related metabolites were compared to those of no cervical lesions in women with and without HPV infection. Samples were collected from women diagnosed with normal cervix (N = 40) and from those detected with HSIL from cytology and colposcopy (N = 40). Liquid-based cytology diagnosis, DNA HPV-detection test, and LC-MS analysis were carried out for all the samples. The same sample, in a customized collection medium, could be used for all the diagnostic techniques employed here. The metabolomic profile of cervical cancer provided by LC-MS was found to indicate unique molecular signatures for HSIL, being two ceramides and a sphingosine metabolite. These molecules occurred independently of women’s HPV status and could be related to the pre-neoplastic phenotype. Statistical models based on such findings could correctly discriminate and classify HSIL and no cervical lesion women. The results showcase the potential of LC-MS as an emerging technology for clinical use in cervical cancer screening, although further validation with a larger sample set is still necessary.

Deciphering hepatocellular carcinoma through metabolomics: from biomarker discovery to therapy evaluation

Hepatocellular carcinoma (HCC) is the third most common cause of death from cancer, with increasing prevalence worldwide. The mortality rate of HCC is similar to its incidence rate, which reflects its poor prognosis. At present, the diagnosis of HCC is still mostly dependent on invasive biopsy, imaging methods, and serum α-fetoprotein (AFP) testing. Because of the asymptomatic nature of early HCC, biopsy and imaging methods usually detect HCC at the middle–late stages. AFP has limited sensitivity and specificity, as many other nonmalignant liver diseases can also result in a very high serum level of AFP. Therefore, better biomarkers with higher sensitivity and specificity at earlier stages are greatly needed. Since metabolic reprogramming is an essential hallmark of cancer and the liver is the metabolic hub of living systems, it is useful to investigate HCC from a metabolic perspective. As a noninvasive and nondestructive approach, metabolomics provides holistic information on dynamically metabolic responses of living systems to both endogenous and exogenous factors. Therefore, it would be conducive to apply metabolomics in investigating HCC. In this review, we summarize recent metabolomic studies on HCC cellular, animal, and clinicopathologic models with attention to metabolomics as a biomarker in cancer diagnosis. Recent applications of metabolomics with respect to therapeutic and prognostic evaluation of HCC are also covered, with emphasis on the potential of treatment by drugs from natural products. In the last section, the current challenges and trends of future development of metabolomics on HCC are discussed. Overall, metabolomics provides us with novel insight into the diagnosis, prognosis, and therapeutic evaluation of HCC.

Metabolomic markers of fertility in bull seminal plasma

Metabolites play essential roles in biological systems, but detailed identities and significance of the seminal plasma metabolome related to bull fertility are still unknown. The objectives of this study were to determine the comprehensive metabolome of seminal plasma from Holstein bulls and to ascertain the potential of metabolites as biomarkers of bull fertility. The seminal plasma metabolome from 16 Holstein bulls with two fertility rates were determined by gas chromatography-mass spectrometry (GC-MS). Multivariate and univariate analyses of the data were performed, and the pathways associated with the seminal plasma metabolome were identified using bioinformatics approaches. Sixty-three metabolites were identified in the seminal plasma of all bulls. Fructose was the most abundant metabolite in the seminal fluid, followed for citric acid, lactic acid, urea and phosphoric acid. Androstenedione, 4-ketoglucose, D-xylofuranose, 2-oxoglutaric acid and erythronic acid represented the least predominant metabolites. Partial-Least Squares Discriminant Analysis (PLSDA) revealed a distinct separation between high and low fertility bulls. The metabolites with the greatest Variable Importance in Projection score (VIP > 2) were 2-oxoglutaric acid and fructose. Heat-map analysis, based on VIP score, and univariate analysis indicated that 2-oxoglutaric acid was less (P = 0.02); whereas fructose was greater (P = 0.02) in high fertility than in low fertility bulls. The current study is the first to describe the metabolome of bull seminal plasma using GC-MS and presented metabolites such as 2-oxoglutaric acid and fructose as potential biomarkers of bull fertility.

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)-mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5-10 min, depending on user experience; data processing typically takes 1-3 h, and data analysis takes ∼30 min.

Newborn Screening: History, Current Status, and Future Directions

Newborn screening programs aim to achieve presymptomatic diagnosis of treatable disorders allowing for early initiation of medical care to prevent or reduce significant morbidity and mortality. Many of the conditions included in the newborn screening panels are inborn errors of metabolism; however, screening for endocrine, hematologic, immunologic, and cardiovascular diseases, and hearing loss is also included in many panels. Newborn screening tests are not diagnostic and therefore diagnostic testing is needed to confirm or exclude the suspected diagnosis. Further advancement in technology is expected to allow continuous expansion of newborn screening.

Investigation of the urinary metabolic variations and the application in bladder cancer biomarker discovery

Urine metabolomics have been used to identify biomarkers for clinical diseases. However, inter-individual variations and effect factors need to be further evaluated. In our study, we explored the urine metabolome in a cohort of 203 health adults, 6 patients with benign bladder lesions, and 53 patients with bladder cancer (BCa) using liquid chromatography coupled with high resolution mass spectrometry. Inter-individual analysis of both healthy controls and BCa patients showed that the urine metabolome was relatively stable. Further analysis indicated that sex and age affect inter-individual variations in urine metabolome. Metabolic pathways such as tryptophan metabolism, the citrate cycle, and pantothenate and CoA biosynthesis were found to be related to sex and age. To eliminate age and sex interference, additional BCa urine metabolomic biomarkers were explored using age and sex-matched urine samples (Test group: 44 health adults vs. 33 patients with BCa). Metabolic profiling of urine could significantly differentiate the cases with cancer from the controls and high-grade from low-grade BCa. A metabolite panel consisting of trans-2-dodecenoylcarnitine, serinyl-valine, feruloyl-2-hydroxyputrescine, and 3-hydroxynonanoyl carnitine were discovered to have good predictive ability for BCa with an area under the curve (AUC) of 0.956 (cross validation: AUC = 0.924). A panel of indolylacryloylglycine, N₂ -galacturonyl-L-lysine, and aspartyl-glutamate was used to establish a robust model for high- and low-grade BCa distinction with AUC of 0.937 (cross validation: AUC = 0.891). External sample (26 control vs. 20 BCa) validation verified the acceptable accuracy of these models for BCa detection. Our study showed that urinary metabolomics is a useful strategy for differential analysis and biomarker discovery.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.

Biomarkers for equine joint injury and osteoarthritis

We report the results of a symposium aimed at identifying validated biomarkers that can be used to complement clinical observations for diagnosis and prognosis of joint injury leading to equine osteoarthritis (OA). Biomarkers might also predict pre-fracture change that could lead to catastrophic bone failure in equine athletes. The workshop was attended by leading scientists in the fields of equine and human musculoskeletal biomarkers to enable cross-disciplinary exchange and improve knowledge in both. Detailed proceedings with strategic planning was written, added to, edited and referenced to develop this manuscript. The most recent information from work in equine and human osteoarthritic biomarkers was accumulated, including the use of personalized healthcare to stratify OA phenotypes, transcriptome analysis of anterior cruciate ligament (ACL) and meniscal injuries in the human knee. The spectrum of "wet" biomarker assays that are antibody based that have achieved usefulness in both humans and horses, imaging biomarkers and the role they can play in equine and human OA was discussed. Prediction of musculoskeletal injury in the horse remains a challenge, and the potential usefulness of spectroscopy, metabolomics, proteomics, and development of biobanks to classify biomarkers in different stages of equine and human OA were reviewed. The participants concluded that new information and studies in equine musculoskeletal biomarkers have potential translational value for humans and vice versa. OA is equally important in humans and horses, and the welfare issues associated with catastrophic musculoskeletal injury in horses add further emphasis to the need for good validated biomarkers in the horse. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:823-831, 2018.

Prediction of platinum-based chemotherapy efficacy in lung cancer based on LC-MS metabolomics approach

Lung cancer is the common cause of cancer-related death worldwide. Platinum-based chemotherapy is the cornerstone of treatment for lung cancer. Platinum sensitivity is a major possibility for effective cancer treatment. In this study, several potential biomarkers were identified for evaluating and predicting the response to platinum-based chemotherapy. LC-MS-based metabolomics was performed on plasma samples from 43 lung cancer patients with different chemotherapy efficacy. By combing multivariate statistical analysis, pathway analysis with correlation analysis, 8 potential biomarkers were significantly associated with platinum chemotherapy response. Moreover, a prediction model with these biomarkers involved in citric acid cycle, glutamate metabolism and amino acid metabolism, showed 100% sensitivity and 100% specificity for predicting chemotherapy response in a validation set. Interestingly, 2-hydroxyglutaric acid (2-HG) as an oncometabolite accumulated in lung cancer was remarkably elevated in the partial response (PR) patients. Collectively, our findings implicated that metabolomics can serve as a potential tool to select lung cancer patients that are more likely to benefit from the platinum-based treatment.

Prediction of Chemotherapeutic Efficacy in Non-Small Cell Lung Cancer by Serum Metabolomic Profiling

Purpose: No validated biomarkers that could identify the subset of patients with lung adenocarcinoma who might benefit from chemotherapy have yet been well established. This study aimed to explore potential biomarker model predictive of efficacy and survival outcomes after first-line pemetrexed plus platinum doublet based on metabolomics profiling.Experimental Design: In total, 354 consecutive eligible patients were assigned to receive first-line chemotherapy of pemetrexed in combination with either cisplatin or carboplatin. Prospectively collected serum samples before initial treatment were utilized to perform metabolomics profiling analyses under the application of LC/MS-MS. Binary logistic regression analysis was carried out to establish discrimination models.Results: There were 251 cases randomly sorted into discovery set, the rest of 103 cases into validation set. Seven metabolites including hypotaurine, uridine, dodecanoylcarnitine, choline, dimethylglycine, niacinamide, and l-palmitoylcarnitine were identified associated with chemo response. On the basis of the seven-metabolite panel, a discriminant model according to logistic regression values g(z) was established with the receiver operating characteristic curve (AUC) of 0.912 (Discovery set) and 0.909 (Validation set) in differentiating progressive disease (PD) groups from disease control (DC) groups. The median progression-free survival (PFS) after chemotherapy in patients with g(z) ≤0.155 was significantly longer than that in those with g(z) > 0.155 (10.3 vs.4.5 months, P < 0.001).Conclusions: This study developed an effective and convenient discriminant model that can accurately predict the efficacy and survival outcomes of pemetrexed plus platinum doublet chemotherapy prior to treatment delivery. Clin Cancer Res; 24(9); 2100-9. ©2018 AACR.

Urinary metabolic profiling of asymptomatic acute intermittent porphyria using a rule-mining-based algorithm

INTRODUCTION

Metabolomic profiling combines Nuclear Magnetic Resonance spectroscopy with supervised statistical analysis that might allow to better understanding the mechanisms of a disease.

OBJECTIVES

In this study, the urinary metabolic profiling of individuals with porphyrias was performed to predict different types of disease, and to propose new pathophysiological hypotheses.

METHODS

Urine ¹H-NMR spectra of 73 patients with asymptomatic acute intermittent porphyria (aAIP) and familial or sporadic porphyria cutanea tarda (f/sPCT) were compared using a supervised rule-mining algorithm. NMR spectrum buckets bins, corresponding to rules, were extracted and a logistic regression was trained.

RESULTS

Our rule-mining algorithm generated results were consistent with those obtained using partial least square discriminant analysis (PLS-DA) and the predictive performance of the model was significant. Buckets that were identified by the algorithm corresponded to metabolites involved in glycolysis and energy-conversion pathways, notably acetate, citrate, and pyruvate, which were found in higher concentrations in the urines of aAIP compared with PCT patients. Metabolic profiling did not discriminate sPCT from fPCT patients.

CONCLUSION

These results suggest that metabolic reprogramming occurs in aAIP individuals, even in the absence of overt symptoms, and supports the relationship that occur between heme synthesis and mitochondrial energetic metabolism.

A phylogenomic analysis of lichen-feeding tiger moths uncovers evolutionary origins of host chemical sequestration

Host species utilize a variety of defenses to deter feeding, including secondary chemicals. Some phytophagous insects have evolved tolerance to these chemical defenses, and can sequester secondary defense compounds for use against their own predators and parasitoids. While numerous studies have examined plant-insect interactions, little is known about lichen-insect interactions. Our study focused on reconstructing the evolution of lichen phenolic sequestration in the tiger moth tribe Lithosiini (Lepidoptera: Erebidae: Arctiinae), the most diverse lineage of lichen-feeding moths, with 3000 described species. We built an RNA-Seq dataset and examined the adult metabolome for the presence of lichen-derived phenolics. Using the transcriptomic dataset, we recover a well-resolved phylogeny of the Lithosiini, and determine that the metabolomes within species are more similar than those among species. Results from an initial ancestral state reconstruction suggest that the ability to sequester phenolics produced by a single chemical pathway preceded generalist sequestration of phenolics produced by multiple chemical pathways. We conclude that phenolics are consistently and selectively sequestered within Lithosiini. Furthermore, sequestration of compounds from a single chemical pathway may represent a synapomorphy of the tribe, and the ability to sequester phenolics produced by multiple pathways arose later. These findings expand on our understanding of the interactions between Lepidoptera and their lichen hosts.

Mass spectrometry for the evaluation of cardiovascular diseases based on proteomics and lipidomics

The identification and quantification of proteins and lipids is of major importance for the diagnosis, prognosis and understanding of the molecular mechanisms involved in disease development. Owing to its selectivity and sensitivity, mass spectrometry has become a key technique in analytical platforms for proteomic and lipidomic investigations. Using this technique, many strategies have been developed based on unbiased or targeted approaches to highlight or monitor molecules of interest from biomatrices. Although these approaches have largely been employed in cancer research, this type of investigation has been met by a growing interest in the field of cardiovascular disorders, potentially leading to the discovery of novel biomarkers and the development of new therapies. In this paper, we will review the different mass spectrometry- based proteomic and lipidomic strategies applied in cardiovascular diseases, especially atherosclerosis. Particular attention will be given to recent developments and the role of bioinformatics in data treatment. This review will be of broad interest to the medical community by providing a tutorial of how mass spectrometric strategies can support clinical trials.

TRIENNIAL GROWTH AND DEVELOPMENT SYMPOSIUM: Molecular mechanisms related to bovine intramuscular fat deposition in the longissimus muscle

The intramuscular fat (IMF) content of the LM, also known as marbling, is particularly important in determining the price of beef in Korea, Japan, and the United States. Deposition of IMF is influenced by both genetic (e.g., breed, gender, and genotype) and nongenetic factors (e.g., castration, nutrition, stressors, animal weight, and age). Castration of bulls markedly increases deposition of IMF, resulting in improved beef quality. Here, we present a comparative gene expression approach between bulls and steers. Transcriptomic and proteomic studies have demonstrated that the combined effects of increases in lipogenesis, fatty acid uptake, and fatty acid esterification and decreased lipolysis are associated with increased IMF deposition in the LM. Several peripheral tissues (LM, adipose tissues, and the liver) are involved in lipid metabolism. Therefore, understanding the significance of the tissue network in lipid metabolism is important. Here, we demonstrate that lipid metabolism in LM tissues is crucial for IMF deposition, whereas lipid metabolism in the liver plays only a minor role. Metabolism of body fat and IMF deposition in bovine species has similarities with these processes in metabolic diseases, such as obesity in humans and rodents. Extensive studies on metabolic diseases using epigenome modification (DNA methylation, histone modification, and microRNA), microbial metagenomics, and metabolomics have been performed in humans and rodents, and new findings have been reported using these technologies. The importance of applying "omics" fields (epigenomics, metagenomics, and metabolomics) to the study of IMF deposition in cattle is described. New information on the molecular mechanisms of IMF deposition may be used to design nutritional or genetic methods to manipulate IMF deposition and to modify fatty acid composition in beef cattle. Applying nutrigenomics could maximize the expression of genetic potential of economically important traits (e.g., marbling) in animals.

Spent culture medium analysis from individually cultured bovine embryos demonstrates metabolomic differences

Spent culture medium can provide valuable information regarding the physiological state of a bovine preimplantation embryos through non-invasive analysis of the sum/depleted metabolite constituents. Metabolomics has become of great interest as an adjunct technique to morphological and cleavage-rate assessment, but more importantly, in improving our understanding of metabolism. In this study, in vitro produced bovine embryos developing at different rates were evaluated using proton nuclear magnetic resonance (1H NMR). Spent culture medium from individually cultured embryos (2-cell to blastocyst stage) were divided into two groups based on their cleavage rate fast growing (FG) and slow growing (SG; developmentally delayed by 12-24 h), then analyzed by a 600 MHz NMR spectrometer. Sixteen metabolites were detected and investigated for sum/depletion throughout development. Data indicate distinct differences between the 4-cell SG and FG embryos for pyruvate (P < 0.05, n = 9) and at the 16-cell stage for acetate, tryptophan, leucine/isoleucine, valine and histidine. Overall sum/depletion levels of metabolites demonstrated that embryos produced glutamate, but consumed histidine, tyrosine, glycine, methionine, tryptophan, phenylalanine, lysine, arginine, acetate, threonine, alanine, pyruvate, valine, isoleucine/leucine, and lactate with an overall trend of higher consumption of these metabolites by FG groups. Principal component analysis revealed distinct clustering of the plain medium, SG, and FG group, signifying the uniqueness of the metabolomic signatures of each of these groups. This study is the first of its kind to characterize the metabolomic profiles of SG and FG bovine embryos produced in vitro using 1H NMR. Elucidating differences between embryos of varying developmental rates could contribute to a better understanding of embryonic health and physiology.

Metabolic Signatures in Response to Abscisic Acid (ABA) Treatment in Brassica napus Guard Cells Revealed by Metabolomics

Drought can severely damage crops, resulting in major yield losses. During drought, vascular land plants conserve water via stomatal closure. Each stomate is bordered by a pair of guard cells that shrink in response to drought and the associated hormone abscisic acid (ABA). The activation of complex intracellular signaling networks underlies these responses. Therefore, analysis of guard cell metabolites is fundamental for elucidation of guard cell signaling pathways. Brassica napus is an important oilseed crop for human consumption and biodiesel production. Here, non-targeted metabolomics utilizing gas chromatography mass spectrometry (GC-MS/MS) and liquid chromatography mass spectrometry (LC-MS/MS) were employed for the first time to identify metabolic signatures in response to ABA in B. napus guard cell protoplasts. Metabolome profiling identified 390 distinct metabolites in B. napus guard cells, falling into diverse classes. Of these, 77 metabolites, comprising both primary and secondary metabolites were found to be significantly ABA responsive, including carbohydrates, fatty acids, glucosinolates, and flavonoids. Selected secondary metabolites, sinigrin, quercetin, campesterol, and sitosterol, were confirmed to regulate stomatal closure in Arabidopsis thaliana, B. napus or both species. Information derived from metabolite datasets can provide a blueprint for improvement of water use efficiency and drought tolerance in crops.

Enhanced antitumor efficacy and reduced toxicity of Abnormal Savda Munziq on tumor bearing mice treated with chemotherapy

Previous research has demonstrated the anti-tumor properties of Abnormal Savda Munziq (ASMq), a traditional Uyghur compound herbal medicine. The effects of ASMq on cervical carcinomas in U27 tumor-bearing mice is investigated, the effect of adding Fluorouracil (5-FU) is also assessed in this paper. The results demonstrate that ASMq and 5-FU significantly inhibited the proliferation of U27 cells in a time-dependent and dose-dependent manner. Evaluating the interactions between ASMq and 5-FU on U27 cell growth yields a combination index (CI) < 1 in different time periods, suggesting a synergistic effect between the two drugs in vitro. Nuclear magnetic resonance (NMR) analysis demonstrates that ASMq can inhibit enhanced lipid metabolism in tumor mice, enhance the glutamine content, promote lymphocyte and macrophage proliferation, and increase tumor necrosis factor(TNF-α) and interleukin(IL) production, which can enhance the effect of 5-FU on the inhibition of tumors. Also ASMq can reduce the content of ALT and AST in serum. Increased SOD, GSH-Px, and decreased the content of MDA in liver tissue. ASMq has a synergistic effect on liver and tumor pathology, as well as tumor inhibition rate. In addition, ASMq can also enhance the body's antioxidant capacity and improve the body's metabolism, and reduce 5-FU's toxic side effects.

Gas Chromatography-Mass Spectrometry for Metabolite Profiling of Japanese Black Cattle Naturally Contaminated with Zearalenone and Sterigmatocystin

The objective of this study was to evaluate the metabolic profile of cattle fed with or without zearalenone (ZEN) and sterigmatocystin (STC)-contaminated diets using a gas chromatography-mass spectrometry metabolomics approach. Urinary samples were collected from individual animals (n = 6 per herd) from fattening female Japanese Black (JB) cattle herds (23 months old, 550–600 kg). Herd 1 had persistently high urinary ZEN and STC concentrations due to the presence of contaminated rice straw. Herd 2, the second female JB fattening herd (23 months old, 550–600 kg), received the same dietary feed as Herd 1, with non-contaminated rice straw. Urine samples were collected from Herd 1, two weeks after the contaminated rice straw was replaced with uncontaminated rice straw (Herd 1N). Identified metabolites were subjected to principal component analysis (PCA) and ANOVA. The PCA revealed that the effects on cattle metabolites depended on ZEN and STC concentrations. The contamination of cattle feed with multiple mycotoxins may alter systemic metabolic processes, including metabolites associated with ATP generation, amino acids, glycine-conjugates, organic acids, and purine bases. The results obtained from Herd 1N indicate that a two-week remedy period was not sufficient to improve the levels of urinary metabolites, suggesting that chronic contamination with mycotoxins may have long-term harmful effects on the systemic metabolism of cattle.

Early metabolic markers identify potential targets for the prevention of type 2 diabetes

AIMS/HYPOTHESIS

The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers.

METHODS

We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study.

RESULTS

Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors.

CONCLUSIONS/INTERPRETATION

This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.

Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice

Geographical origin determination of white rice has become the major issue of food industry. However, there is still lack of a high-throughput method for rapidly and reproducibly differentiating the geographical origins of commercial white rice. In this study, we developed a method that employed lipidomics and deep learning to discriminate white rice from Korea to China. A total of 126 white rice of 30 cultivars from different regions were utilized for the method development and validation. By using direct infusion-mass spectrometry-based targeted lipidomics, 17 lysoglycerophospholipids were simultaneously characterized within minutes per sample. Unsupervised data exploration showed a noticeable overlap of white rice between two countries. In addition, lysophosphatidylcholines (lysoPCs) were prominent in white rice from Korea while lysophosphatidylethanolamines (lysoPEs) were enriched in white rice from China. A deep learning prediction model was built using 2014 white rice and validated using two different batches of 2015 white rice. The model accurately discriminated white rice from two countries. Among 10 selected predictors, lysoPC(18:2), lysoPC(14:0), and lysoPE(16:0) were the three most important features. Random forest and gradient boosting machine models also worked well in this circumstance. In conclusion, this study provides an architecture for high-throughput classification of white rice from different geographical origins.

Protein Secretion in Gram-Positive Bacteria: From Multiple Pathways to Biotechnology

A number of Gram-positive bacteria are important players in industry as producers of a diverse array of economically interesting metabolites and proteins. As discussed in this overview, several Gram-positive bacteria are valuable hosts for the production of heterologous proteins. In contrast to Gram-negative bacteria, proteins secreted by Gram-positive bacteria are released into the culture medium where conditions for correct folding are more appropriate, thus facilitating the isolation and purification of active proteins. Although seven different protein secretion pathways have been identified in Gram-positive bacteria, the majority of heterologous proteins are produced via the general secretion or Sec pathway. Not all proteins are equally well secreted, because heterologous protein production often faces bottlenecks including hampered secretion, susceptibility to proteases, secretion stress, and metabolic burden. These bottlenecks are associated with reduced yields leading to non-marketable products. In this chapter, besides a general overview of the different protein secretion pathways, possible hurdles that may hinder efficient protein secretion are described and attempts to improve yield are discussed including modification of components of the Sec pathway. Attention is also paid to omics-based approaches that may offer a more rational approach to optimize production of heterologous proteins.

The use of stable isotopes in the study of human pathophysiology

The growing prevalence of metabolic diseases including fatty liver disease and Type 2 diabetes has increased the emphasis on understanding metabolism at the mechanistic level and how it is perturbed in disease. Metabolomics is a continually expanding field that seeks to measure metabolites in biological systems during a physiological stimulus or a genetic alteration. Typically, metabolomics studies provide total pool sizes of metabolites rather than dynamic flux measurements. More recently there has been a resurgence in approaches that use stable isotopes (e.g. ²H and ¹³C) for the unambiguous tracking of individual atoms through compartmentalised metabolic networks in humans to determine underlying mechanisms. This is known as metabolic flux analysis and enables the capture of a dynamic picture of the metabolome and its interactions with the genome and proteome. In this review, we describe current approaches using stable isotope labelling in the field of metabolomics and provide examples of studies that led to an improved understanding of glucose, fatty acid and amino acid metabolism in humans, particularly in relation to metabolic disease. Examples include the use of stable isotopes of glucose to study tumour bioenergetics as well as brain metabolism during traumatic brain injury. Lipid tracers have also been used to measure non-esterified fatty acid production whilst amino acid tracers have been used to study the rate of protein digestion on whole body postprandial protein metabolism. In addition, we illustrate the use of stable isotopes for measuring flux in human physiology by providing examples of breath tests to measure insulin resistance and gastric emptying rates.

Metabolomic signature of brain cancer

Despite advances in surgery and adjuvant therapy, brain tumors represent one of the leading causes of cancer-related mortality and morbidity in both adults and children. Gliomas constitute about 60% of all cerebral tumors, showing varying degrees of malignancy. They are difficult to treat due to dismal prognosis and limited therapeutics. Metabolomics is the untargeted and targeted analyses of endogenous and exogenous small molecules, which charact erizes the phenotype of an individual. This emerging "omics" science provides functional readouts of cellular activity that contribute greatly to the understanding of cancer biology including brain tumor biology. Metabolites are highly informative as a direct signature of biochemical activity; therefore, metabolite profiling has become a promising approach for clinical diagnostics and prognostics. The metabolic alterations are well-recognized as one of the key hallmarks in monitoring disease progression, therapy, and revealing new molecular targets for effective therapeutic intervention. Taking advantage of the latest high-throughput analytical technologies, that is, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), metabolomics is now a promising field for precision medicine and drug discovery. In the present report, we review the application of metabolomics and in vivo metabolic profiling in the context of adult gliomas and paediatric brain tumors. Analytical platforms such as high-resolution (HR) NMR, in vivo magnetic resonance spectroscopic imaging and high- and low-resolution MS are discussed. Moreover, the relevance of metabolic studies in the development of new therapeutic strategies for treatment of gliomas are reviewed.

Performance of a High-Pressure Liquid Chromatography-Ion Mobility-Mass Spectrometry System for Metabolic Profiling

A commercial liquid chromatography/drift tube ion mobility-mass spectrometer (LC/IM-MS) was evaluated for its utility in global metabolomics analysis. Performance was assessed using 12 targeted metabolite standards where the limit of detection (LOD), linear dynamic range, resolving power, and collision cross section (Ω) are reported for each standard. Data were collected in three different instrument operation modes: flow injection analysis with IM-MS (FIA/IM-MS), LC/MS, and LC/IM-MS. Metabolomics analyses of human plasma and HaCaT cells were used to compare the above three operation modes. LC/MS provides linearity in response, data processing automation, improved limits of detection, and ease of use. Advantages of LC/IM-MS and FIA/IM-MS include the ability to develop mobility-mass trend lines for structurally similar biomolecules, increased peak capacity, reduction of chemical/matrix noise, improvement in signal-to-noise, and separations of isobar/isomer compounds that are not resolved by LC. We further tested the feasibility of incorporating IM-MS into conventional LC/MS metabolomics workflows. In general, the addition of ion mobility dimension has increased the separation of compounds in complex biological matrixes and has the potential to largely improve the throughput of metabolomics analysis.

Metabolomic and proteomic investigations of impacts of titanium dioxide nanoparticles on Escherichia coli

In a previous study, it was demonstrated that the toxic impact of titanium dioxide nanoparticles on Escherichia coli starts at 10 ppm and is closely related to the presence of little aggregates. It was also assumed that only a part of the bacterial population is able to adapt to this stress and attempts to survive. Proteomic analyses, supported by results from metabolomics, reveal that exposure of E. coli to nano-TiO2 induces two main effects on bacterial metabolism: firstly, the up-regulation of proteins and the increase of metabolites related to energy and growth metabolism; secondly, the down-regulation of other proteins resulting in an increase of metabolites, particularly amino acids. Some proteins, e.g. chaperonin 1 or isocitrate dehydrogenase, and some metabolites, e.g. phenylalanine or valine, might be used as biomarkers of nanoparticles stress. Astonishingly, the ATP content gradually rises in relation with the nano-TiO2 concentration in the medium, indicating a dramatic release of ATP by the damaged cells. These apparently contradictory results accredit the thesis of a heterogeneity of the bacterial population. This heterogeneity is also confirmed by SEM images which show that while some bacteria are fully covered by nano-TiO2, the major part of the bacterial population remains free from nanoparticles, resulting in a difference of proteome and metabolome. The use of combined-omics has allowed to better understand the heterogeneous bacterial response to nano-TiO2 stress due to heterogeneous contacts between the protagonists under environmental conditions.

Metabolic reprogramming during the Trypanosoma brucei life cycle

Cellular metabolic activity is a highly complex, dynamic, regulated process that is influenced by numerous factors, including extracellular environmental signals, nutrient availability and the physiological and developmental status of the cell. The causative agent of sleeping sickness, Trypanosoma brucei, is an exclusively extracellular protozoan parasite that encounters very different extracellular environments during its life cycle within the mammalian host and tsetse fly insect vector. In order to meet these challenges, there are significant alterations in the major energetic and metabolic pathways of these highly adaptable parasites. This review highlights some of these metabolic changes in this early divergent eukaryotic model organism.

Metabolic reprogramming during the Trypanosoma brucei life cycle

Cellular metabolic activity is a highly complex, dynamic, regulated process that is influenced by numerous factors, including extracellular environmental signals, nutrient availability and the physiological and developmental status of the cell. The causative agent of sleeping sickness, Trypanosoma brucei, is an exclusively extracellular protozoan parasite that encounters very different extracellular environments during its life cycle within the mammalian host and tsetse fly insect vector. In order to meet these challenges, there are significant alterations in the major energetic and metabolic pathways of these highly adaptable parasites. This review highlights some of these metabolic changes in this early divergent eukaryotic model organism.

Commentary on "Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks"

This is an invited review/commentary by the first and last authors of a paper that was the most cited in FEMS Microbiology Letters for 1996, presently showing in excess of 150 citations at Web of Science, and over 200 at Google Scholar. It was the first paper in which diffuse reflectance absorbance FT-IR spectroscopy was used with a supervised learning method in the form of artificial neural networks, and showed that this combination could succeed in discriminating a series of closely related, clinically relevant, Gram-positive bacterial strains.

Metabolomic approaches in the discovery of potential urinary biomarkers of drug-induced liver injury (DILI)

Drug-induced liver injury (DILI) is a major safety issue during drug development, as well as the most common cause for the withdrawal of drugs from the pharmaceutical market. The identification of DILI biomarkers is a labor-intensive area. Conventional biomarkers are not specific and often only appear at significant levels when liver damage is substantial. Therefore, new biomarkers for early identification of hepatotoxicity during the drug discovery process are needed, thus resulting in lower development costs and safer drugs. In this sense, metabolomics has been increasingly playing an important role in the discovery of biomarkers of liver damage, although the characterization of the mechanisms of toxicity induced by xenobiotics remains a huge challenge. These new-generation biomarkers will offer obvious benefits for the pharmaceutical industry, regulatory agencies, as well as a personalized clinical follow-up of patients, upon validation and translation into clinical practice or approval for routine use. This review describes the current status of the metabolomics applied to the early diagnosis and prognosis of DILI and in the discovery of new potential urinary biomarkers of liver injury.

Mass spectrometry imaging shows major derangements in neurogranin and in purine metabolism in the triple-knockout 3×Tg Alzheimer mouse model

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) can simultaneously measure hundreds of biomolecules directly from tissue. Using different sample preparation strategies, proteins and metabolites have been profiled to study the molecular changes in a 3×Tg mouse model of Alzheimer's disease. In comparison with wild-type (WT) control mice MALDI-MSI revealed Alzheimer's disease-specific protein profiles, highlighting dramatic reductions of a protein with m/z 7560, which was assigned to neurogranin and validated by immunohistochemistry. The analysis also revealed substantial metabolite changes, especially in metabolites related to the purine metabolic pathway, with a shift towards an increase in hypoxanthine/xanthine/uric acid in the 3×Tg AD mice accompanied by a decrease in AMP and adenine. Interestingly these changes were also associated with a decrease in ascorbic acid, consistent with oxidative stress. Furthermore, the metabolite N-arachidonyl taurine was increased in the diseased mouse brain sections, being highly abundant in the hippocampus. Overall, we describe an interesting shift towards pro-inflammatory molecules (uric acid) in the purinergic pathway associated with a decrease in anti-oxidant level (ascorbic acid). Together, these observations fit well with the increased oxidative stress and neuroinflammation commonly observed in AD. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

The Personalization of Clopidogrel Antiplatelet Therapy: The Role of Integrative Pharmacogenetics and Pharmacometabolomics

Dual antiplatelet therapy of aspirin and clopidogrel is pivotal for patients undergoing percutaneous coronary intervention. However, the variable platelets reactivity response to clopidogrel may lead to outcome failure and recurrence of cardiovascular events. Although many genetic and nongenetic factors are known, great portion of clopidogrel variable platelets reactivity remain unexplained which challenges the personalization of clopidogrel therapy. Current methods for clopidogrel personalization include CYP2C19 genotyping, pharmacokinetics, and platelets function testing. However, these methods lack precise prediction of clopidogrel outcome, often leading to insufficient prediction. Pharmacometabolomics which is an approach to identify novel biomarkers of drug response or toxicity in biofluids has been investigated to predict drug response. The advantage of pharmacometabolomics is that it does not only predict the response but also provide extensive information on the metabolic pathways implicated with the response. Integrating pharmacogenetics with pharmacometabolomics can give insight on unknown genetic and nongenetic factors associated with the response. This review aimed to review the literature on factors associated with the variable platelets reactivity response to clopidogrel, as well as appraising current methods for the personalization of clopidogrel therapy. We also aimed to review the literature on using pharmacometabolomics approach to predict drug response, as well as discussing the plausibility of using it to predict clopidogrel outcome.

Diagnostic Applications of Nuclear Magnetic Resonance-Based Urinary Metabolomics

Metabolomics is a rapidly growing field with potential applications in various disciplines. In particular, metabolomics has received special attention in the discovery of biomarkers and diagnostics. This is largely due to the fact that metabolomics provides critical information related to the downstream products of many cellular and metabolic processes which could provide a snapshot of the health/disease status of a particular tissue or organ. Many of these cellular products eventually find their way to urine; hence, analysis of urine via metabolomics has the potential to yield useful diagnostic and prognostic information. Although there are a number of analytical platforms that can be used for this purpose, this review article will focus on nuclear magnetic resonance-based metabolomics. Furthermore, although there have been many studies addressing different diseases and metabolic disorders, the focus of this review article will be in the following specific applications: urinary tract infection, kidney transplant rejection, diabetes, some types of cancer, and inborn errors of metabolism. A number of methodological considerations that need to be taken into account for the development of a clinically useful optimal test are discussed briefly.

Five Micron High Resolution MALDI Mass Spectrometry Imaging with Simple, Interchangeable, Multi-Resolution Optical System

High-spatial resolution mass spectrometry imaging (MSI) is crucial for the mapping of chemical distributions at the cellular and subcellular level. In this work, we improved our previous laser optical system for matrix-assisted laser desorption ionization (MALDI)-MSI, from ~9 μm practical laser spot size to a practical laser spot size of ~4 μm, thereby allowing for 5 μm resolution imaging without oversampling. This is accomplished through a combination of spatial filtering, beam expansion, and reduction of the final focal length. Most importantly, the new laser optics system allows for simple modification of the spot size solely through the interchanging of the beam expander component. Using 10×, 5×, and no beam expander, we could routinely change between ~4, ~7, and ~45 μm laser spot size, in less than 5 min. We applied this multi-resolution MALDI-MSI system to a single maize root tissue section with three different spatial resolutions of 5, 10, and 50 μm and compared the differences in imaging quality and signal sensitivity. We also demonstrated the difference in depth of focus between the optical systems with 10× and 5× beam expanders. Graphical Abstract ᅟ.

The role of metabolomics in determination of new dietary biomarkers

Traditional methods for the assessment of dietary intake are prone to error; in order to improve and enhance these methods increasing interest in the identification of dietary biomarkers has materialised. Metabolomics has emerged as a key tool in the area of dietary biomarker discovery and to date the use of metabolomics has identified a number of putative biomarkers. Applications to identify novel biomarkers of intake have in general taken three approaches: (1) specific acute intervention studies to identify specific biomarkers of intake; (2) searching for biomarkers in cohort studies by correlating to self-reported intake of a specific food/food group(s); (3) analysing dietary patterns in conjunction with metabolomic profiles to identify biomarkers and nutritypes. A number of analytical technologies are employed in metabolomics as currently there is no single technique capable of measuring the entire metabolome. These approaches each have their own advantages and disadvantages. The present review will provide an overview of current technologies and applications of metabolomics in the determination of new dietary biomarkers. In addition, it will address some of the current challenges in the field and future outlooks.

Numerical Taxonomy Helps Identification of Merliniidae and Telotylenchidae (Nematoda: Tylenchoidea) from Iran

Numerical taxonomy was used for identification and grouping of the genera, species, and populations in the families Merliniidae and Telotylenchidae. The variability of each of 44 morphometric characters was evaluated by calculation of the coefficient of variability (CV) and the ratio of extremes (max/min) in the range of 1,020 measured females. Also correlation and regression analyses were made between characters to find potential collinearities. Hierarchical cluster analysis (HCA) was used for (i) grouping 21 genera in the superfamily Dolichodoroidea based on literature data coded for states of 18 diagnostic characters, and (ii) for grouping Iranian populations belonging to selected genera. Furthermore, STEPDISC analysis was used for (i) grouping 11 genera of Merliniidae and Telotylenchidae based on the measurements of 35 characters from 1,007 Iranian female specimens, and (ii) grouping measured females of eight species of Amplimerlinius and Pratylenchoides. The multivariate data analysis approach showed robust enough to summarize relationship between morphometric characters and group genera, species, and populations of the nematodes and in particular help to identify the genera and species of Amplimerlinius and Pratylenchoides.