Analytical technologies for metabonomics and metabolomics, and multi-omic information recovery

Ion Pair Chromatography for Endogenous Metabolite LC-MS Analysis in Tissue Samples Following HGH Resolution Untargeted Acquisition

A protocol for the preparation of tissue extracts for the targeted analysis ca. 150 polar metabolites, including those involved in central carbon metabolism, is described, using a reversed phase ion pair U(H)PLC-MS method. Data collection enabled in high-resolution mass spectrometry detection provides highly specific and sensitive acquisition of metabolic intermediates with wide range physicochemical properties and pathway coverage. Technical aspects are discussed for method transfer along with the basic principles of sample sequence setup, data analysis, and validation. At last general comments are given to help the assessment of data quality and system performance.

Mitochondrial Biomarkers in the Omics Era: A Clinical-Pathophysiological Perspective

Mitochondrial diseases (MDs) affect 4300 individuals, with different ages of presentation and manifestation in any organ. How defects in mitochondria can cause such a diverse range of human diseases remains poorly understood. In recent years, several published research articles regarding the metabolic and protein profiles of these neurogenetic disorders have helped shed light on the pathogenetic mechanisms. By investigating different pathways in MDs, often with the aim of identifying disease biomarkers, it is possible to identify molecular processes underlying the disease. In this perspective, omics technologies such as proteomics and metabolomics considered in this review, can support unresolved mitochondrial questions, helping to improve outcomes for patients.

Metabolomic and Lipidomic Tools for Tracing Fish Escapes from Aquaculture Facilities

During adverse atmospheric events, enormous damage can occur at marine aquaculture facilities, as was the case during Storm Gloria in the southeastern Spanish Mediterranean in January 2020, with massive fish escapes. Fishes that escape were caught by professional fishermen. The objective of this study was to identify biomarkers in fish that enable differentiation among wild fish, escaped farm-raised fish, and farm-raised fish kept in aquaculture facilities until their slaughter. We focused on gilthead sea bream (Sparus aurata). We used nuclear magnetic resonance to search for possible biomarkers. We found that wild gilthead sea bream showed higher levels of taurine and trimethylamine-N-oxide (TMAO) in their muscle and higher levels of ω-3 fatty acids, whereas farm-escaped and farmed gilthead sea bream raised until slaughter exhibit higher levels of ω-6 fatty acids. From choline, carnitine, creatinine, betaine, or lecithin, trimethylamine (TMA) is synthesized in the intestine by the action of bacterial microflora. In the liver, TMA is oxidized to TMAO and transported to muscle cells. The identified biomarkers will improve the traceability of gilthead sea bream by distinguishing wild specimens from those raised in aquaculture.

Quantitative proteomic and metabolomic profiling reveals different osmoregulation mechanisms of tilapia cells coping with different hyperosmotic stress

This study aimed to investigate the different regulatory mechanisms of euryhaline fish under regular hyperosmotic and extreme hyperosmotic stress. The OmB (Oreochromis mossambicus brain) cells were exposed to three treatments: control, regular hyperosmotic stress and extreme hyperosmotic stress. After 12 h exposure, proteomics, metabolomics analyses and integrative analyses were explored. Both kinds of stress lead to lowering cell growth and morphology changes, while under regular hyperosmotic stress, the up-regulated processes related with compatible organic osmolytes synthesis are crucial strategy for the euryhaline fish cell line to survive; On the other hand, under extreme hyperosmotic stress, the processes related with cell apoptosis and cell cycle arrest are dominant. Furthermore, down-regulated pyrimidine metabolism and several ribosomal proteins partially participated in the lowered cell metabolism and increased cell death under both kinds of hyperosmotic stress. The PI3K-Akt and p53 signaling pathways were involved in the stagnant stage of cell cycles and induction of cell apoptosis under both kinds of hyperosmotic stress. However, HIF-1, FoxO, JAK-STAT and Hippo signaling pathways mainly contribute to disrupting the cell cycle, metabolism and induction of cell apoptosis under extreme hyperosmotic stress. SIGNIFICANCE: In the past, the research on fish osmoregulation mainly focused on the transcription factors and ion transporters of osmoregulation, the processes between osmotic sensing and signal transduction, and the associations between signaling pathways and regulation processes have been poorly understood. Investigating fish cell osmoregulation and potential signal transduction pathways is necessary. With the advancements in omics research, it is now feasible to investigate the relationship between environmental stress and molecular responses. In this study, we aimed to explore the signaling pathways and substance metabolism mode during hyper-osmoregulation in OmB cell line, to reveal the key factors that are critical to cell osmoregulation.

Metabolomics and molecular docking-directed antiarthritic study of the ethyl acetate extract from Celastrus orbiculatus Thunb

ETHNOPHARMACOLOGICAL RELEVANCE

Celastrus orbiculatus Thunb., an important folk medicine, has long been used for the treatment of rheumatoid arthritis and its ethyl acetate extract (COE) has been reported to possess anticancer, antiinflammation and antiarthritic effects. However, the therapeutic effect and mechanism of COE treatment in rheumatoid arthritis has been rarely studied especially from the perspective of metabolomics.

AIM OF STUDY

To reveal the therapeutic effects of COE on adjuvant-induced arthritis (AIA) rats through histopathological analysis, non-targeted metabolomics, and molecular docking study.

MATERIALS AND METHODS

Forty-three Wistar rats were randomly divided into normal group, AIA model group, methotrexate group, and COE groups (80 mg/kg, 160 mg/kg and 320 mg/kg of ethyl acetate extract). Paw swelling and arthritis score were monitored through the experiment. Serum levels of tumor necrosis factor α (TNF-α) and nitric oxide were determined and histopathological evaluation was performed. Furthermore, Ultra-high performance liquid chromatography-linear trap quadrupole-Orbitrap-based metabolomics was employed to characterize metabolic changes of AIA rats after COE treatment and molecular docking was performed to predict the potential phytochemicals of COE against TNF-α.

RESULTS

COE at three dosages could significantly relieve paw swelling and reduce arthritis scores of AIA rat. Histopathological analysis revealed remarkable decrease in synovial inflammation and bone erosion after COE treatment, especially at middle and high dosage. Additionally, COE down-regulated serum levels of TNF-α and nitric oxide. Serum metabolomics showed that 22 potential biomarkers for the COE treatment of AIA rats were identified, which were closely related to fatty acid metabolism, glycerophospholipid catabolism, and tryptophan metabolism. The molecular docking models predicted that olean-type triterpenes in COE may contribute most to therapeutic effects of rheumatoid arthritis through targeting TNF-α.

CONCLUSIONS

COE could significantly relieve the arthritic symptoms in AIA rats and the ultra-high performance liquid chromatography-mass spectrometry based metabolomics proved to be an efficient method to characterize subtle metabolic changes of AIA rats after COE treatment.

A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms

Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties’ resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing.

Metabolomics facilitate the personalized management in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a gastrointestinal disorder characterized by chronic relapsing inflammation and mucosal lesions. Reliable biomarkers for monitoring disease activity, predicting therapeutic response, and disease relapse are needed in the personalized management of IBD. Given the alterations in metabolomic profiles observed in patients with IBD, metabolomics, a new and developing technique for the qualitative and quantitative study of small metabolite molecules, offers another possibility for identifying candidate markers and promising predictive models. With increasing research on metabolomics, it is gradually considered that metabolomics will play a significant role in the management of IBD. In this review, we summarize the role of metabolomics in the assessment of disease activity, including endoscopic activity and histological activity, prediction of therapeutic response, prediction of relapse, and other aspects concerning disease management in IBD. Furthermore, we describe the limitations of metabolomics and highlight some solutions.

Applications of Multi-Omics Technologies for Crop Improvement

Multiple "omics" approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the "phenotype to genotype" and "genotype to phenotype" concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.

Tear Metabolomics in Dry Eye Disease: A Review

Dry eye disease (DED) is a multifactorial syndrome that can be caused by alteration in the quality or quantity of the precorneal tear film. It is considered one of the most common ocular conditions leading patients to seek eye care. The current method for diagnostic evaluations and follow-up examinations of DED is a combination of clinical signs and symptoms determined by clinical tests and questionnaires, respectively. The application of powerful omics technologies has opened new avenues toward analysis of subjects in health and disease. Metabolomics is a new emerging and complementary research discipline to all modern omics in the comprehensive analysis of biological systems. The identification of distinct metabolites and integrated metabolic profiles in patients can potentially inform clinicians at an early stage or during monitoring of disease progression, enhancing diagnosis, prognosis, and the choice of therapy. In ophthalmology, metabolomics has gained considerable attention over the past decade but very limited such studies have been reported on DED. This paper aims to review the application of tear metabolomics in DED.

Antibiotic Discovery: Where Have We Come from, Where Do We Go?

Given the increase in antibiotic-resistant bacteria, alongside the alarmingly low rate of newly approved antibiotics for clinical usage, we are on the verge of not having effective treatments for many common infectious diseases. Historically, antibiotic discovery has been crucial in outpacing resistance and success is closely related to systematic procedures—platforms—that have catalyzed the antibiotic golden age, namely the Waksman platform, followed by the platforms of semi-synthesis and fully synthetic antibiotics. Said platforms resulted in the major antibiotic classes: aminoglycosides, amphenicols, ansamycins, beta-lactams, lipopeptides, diaminopyrimidines, fosfomycins, imidazoles, macrolides, oxazolidinones, streptogramins, polymyxins, sulphonamides, glycopeptides, quinolones and tetracyclines. During the genomics era came the target-based platform, mostly considered a failure due to limitations in translating drugs to the clinic. Therefore, cell-based platforms were re-instituted, and are still of the utmost importance in the fight against infectious diseases. Although the antibiotic pipeline is still lackluster, especially of new classes and novel mechanisms of action, in the post-genomic era, there is an increasingly large set of information available on microbial metabolism. The translation of such knowledge into novel platforms will hopefully result in the discovery of new and better therapeutics, which can sway the war on infectious diseases back in our favor.

Metabolomics in Plant Priming Research: The Way Forward?

A new era of plant biochemistry at the systems level is emerging, providing detailed descriptions of biochemical phenomena at the cellular and organismal level. This new era is marked by the advent of metabolomics—the qualitative and quantitative investigation of the entire metabolome (in a dynamic equilibrium) of a biological system. This field has developed as an indispensable methodological approach to study cellular biochemistry at a global level. For protection and survival in a constantly-changing environment, plants rely on a complex and multi-layered innate immune system. This involves surveillance of ‘self’ and ‘non-self,’ molecule-based systemic signalling and metabolic adaptations involving primary and secondary metabolites as well as epigenetic modulation mechanisms. Establishment of a pre-conditioned or primed state can sensitise or enhance aspects of innate immunity for faster and stronger responses. Comprehensive elucidation of the molecular and biochemical processes associated with the phenotypic defence state is vital for a better understanding of the molecular mechanisms that define the metabolism of plant–pathogen interactions. Such insights are essential for translational research and applications. Thus, this review highlights the prospects of metabolomics and addresses current challenges that hinder the realisation of the full potential of the field. Such limitations include partial coverage of the metabolome and maximising the value of metabolomics data (extraction of information and interpretation). Furthermore, the review points out key features that characterise both the plant innate immune system and enhancement of the latter, thus underlining insights from metabolomic studies in plant priming. Future perspectives in this inspiring area are included, with the aim of stimulating further studies leading to a better understanding of plant immunity at the metabolome level.

Recent advances in the applications of metabolomics in eye research

Metabolomics, the identification and quantitation of metabolites in a system, have been applied to identify new biomarkers or elucidate disease mechanism. In this review, we discussed the application of metabolomics in several ocular diseases and recent developments in metabolomics regarding tear fluids analysis, data acquisition and processing.

Omics in Zebrafish Teratogenesis

The genome revolution represents a complete change on our view of biological systems. The quantitative determination of changes in all major molecular components of the living cells, the "omics" approach, opened whole new fields for all health sciences. Genomics, transcriptomics, proteomics, metabolomics, and others, together with appropriate prediction and modeling tools, will mark the future of developmental toxicity assessment both for wildlife and humans. This is especially true for disciplines, like teratology, which rely on studies in model organisms, as studies at lower levels of organization are difficult to implement. Rodents and frogs have been the favorite models for studying human reproductive and developmental disorders for decades. Recently, the study of the development of zebrafish embryos (ZE) is becoming a major alternative tool to adult animal testing. ZE intrinsic characteristics makes this model a unique system to analyze in vivo developmental alterations that only can be studied applying in toto approaches. Moreover, under actual legislations, ZE is considered as a replacement model (and therefore, excluded from animal welfare regulations) during the first 5 days after fertilization. Here we review the most important components of the zebrafish toolbox available for analyzing early stages of embryotoxic events that could eventually lead to teratogenesis.

Ion Pair Chromatography for Endogenous Metabolites LC-MS Analysis in Tissue Samples Following Targeted Acquisition

A protocol for the preparation of tissue extracts for the targeted analysis of ca. 150 polar metabolites, including those involved in central carbon metabolism is described, using a reversed-phase ion pair U(H)PLC-MS method. Data collection enabled by multiple-reaction monitoring provides highly specific, sensitive acquisition of metabolic intermediates with a wide range of physicochemical properties and pathway coverage. Technical aspects are discussed for method transfer along with the basic principles of sample sequence setup, data analysis, and validation. General comments are given to help the assessment of data quality and system performance.

Metabolomics applied to diabetes-lessons from human population studies

The 'classical' distribution of type 2 diabetes (T2D) across the globe is rapidly changing and it is no longer predominantly a disease of middle-aged/elderly adults of western countries, but it is becoming more common through Asia and the Middle East, as well as increasingly found in younger individuals. This global altered incidence of T2D is most likely associated with the spread of western diets and sedentary lifestyles, although there is still much debate as to whether the increased incidence rates are due to an overconsumption of fats, sugars or more generally high-calorie foods. In this context, understanding the interactions between genes of risk and diet and how they influence the incidence of T2D will help define the causative pathways of the disease. This review focuses on the use of metabolomics in large cohort studies to follow the incidence of type 2 diabetes in different populations. Such approaches have been used to identify new biomarkers of pre-diabetes, such as branch chain amino acids, and associate metabolomic profiles with genes of known risk in T2D from large scale GWAS studies. As the field develops, there are also examples of meta-analysis across metabolomics cohort studies and cross-comparisons with different populations to allow us to understand how genes and diet contribute to disease risk. Such approaches demonstrate that insulin resistance and T2D have far reaching metabolic effects beyond raised blood glucose and how the disease impacts systemic metabolism.

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling

¹H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum ¹H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

Metabolomics and Trace Element Analysis of Camel Tear by GC-MS and ICP-MS

Camel tear metabolomics and elemental analysis are useful in getting the information regarding the components responsible for maintaining the protective system that allows living in the desert and dry regions. The aim of this study was to correlate that the camel tears can be used as artificial tears for the evaluation of dryness in the eye. Eye biomarkers of camel tears were analyzed by gas chromatography-mass spectroscopy (GC-MS) and inductively coupled plasma mass spectroscopy (ICP-MS). The major compounds detected in camel tears by GC-MS were alanine, valine, leucine, norvaline, glycine, cadaverine, urea, ribitol, sugars, and higher fatty acids like octadecanoic acid and hexadecanoic acid. GC-MS analysis of camel tears also finds several products of metabolites and its associated metabolic participants. ICP-MS analysis showed the presence of different concentration of elemental composition in the camel tears.

Automated quantification of metabolites in blood-derived samples by NMR

NMR is widely applied in the field of metabolomics due to the quantitative nature of the technology and the reproducible data generated. However, because of severe spectral crowding, quantifying individual metabolites in body fluids such as serum and plasma remains a challenge. In this study, a method to automatically annotate and quantify a number of small metabolites in human serum and EDTA plasma is introduced. It combines the superior signal-to-noise ratio of the commonly applied CPMG and NOESY1D pulse sequences with the superior resolution of the 2D JRES experiment to construct a model that extracts the metabolite concentrations directly from the 1D spectra without tedious deconvolution. The performance of the method was assessed by comparing the calculated areas of the various glucose peaks with known clinical values, by comparing several peaks of the same metabolite (extracted versus non-extracted), and by comparing areas obtained from various NMR pulse sequences. Additionally, the models were tested on independent datasets. It was found that for many metabolites peaks could be assigned that show a consistent behavior, indicating a precise quantification. The same method should be applicable to other biofluids with a stable composition and pH, such as CSF fluid, cell extracts, and cell media.

Three serum metabolite signatures for diagnosing low-grade and high-grade bladder cancer

To address the shortcomings of cystoscopy and urine cytology for detecting and grading bladder cancer (BC), ultrahigh performance liquid chromatography (UHPLC) coupled with Q-TOF mass spectrometry in conjunction with univariate and multivariate statistical analyses was employed as an alternative method for the diagnosis of BC. A series of differential serum metabolites were further identified for low-grade(LG) and high-grade(HG) BC patients, suggesting metabolic dysfunction in malignant proliferation, immune escape, differentiation, apoptosis and invasion of cancer cells in BC patients. In total, three serum metabolites including inosine, acetyl-N-formyl-5-methoxykynurenamine and PS(O-18:0/0:0) were selected by binary logistic regression analysis, and receiver operating characteristic (ROC) test based on their combined use for HG BC showed that the area under the curve (AUC) was 0.961 in the discovery set and 0.950 in the validation set when compared to LG BC. Likewise, this composite biomarker panel can also differentiate LG BC from healthy controls with the AUC of 0.993 and 0.991 in the discovery and validation set, respectively. This finding suggested that this composite serum metabolite signature was a promising and less invasive classifier for probing and grading BC, which deserved to be further investigated in larger samples.

The application of absolute quantitative (1)H NMR spectroscopy in drug discovery and development

INTRODUCTION

The identification of a drug candidate and its structural determination is the most important step in the process of the drug discovery and for this, nuclear magnetic resonance (NMR) is one of the most selective analytical techniques.

AREA COVERED

The present review illustrates the various perspectives of absolute quantitative (1)H NMR spectroscopy in drug discovery and development. It deals with the fundamentals of quantitative NMR (qNMR), the physiochemical properties affecting qNMR, and the latest referencing techniques used for quantification. The precise application of qNMR during various stages of drug discovery and development, namely natural product research, drug quantitation in dosage forms, drug metabolism studies, impurity profiling and solubility measurements is elaborated. To achieve this, the authors explore the literature of NMR in drug discovery and development between 1963 and 2015. It also takes into account several other reviews on the subject.

EXPERT OPINION

qNMR experiments are used for drug discovery and development processes as it is a non-destructive, versatile and robust technique with high intra and interpersonal variability. However, there are several limitations also. qNMR of complex biological samples is incorporated with peak overlap and a low limit of quantification and this can be overcome by using hyphenated chromatographic techniques in addition to NMR.

Effects of Pinellia ternata (Thunb.) Berit. on the metabolomic profiles of placenta and amniotic fluid in pregnant rats

ETHNOPHARMACOLOGICAL RELEVANCE

Banxia (BX) is the root of Pinellia ternata (Thunb.) Berit. Its processed products, such as Jiang Banxia (JBX), have been clinically used in traditional Chinese medicine to treat vomiting, coughing, and inflammation. However, data for their safety for pregnant women are contradictory and confusing.

AIM OF THE STUDY

To further explore the safety of BX, an ultra-performance liquid chromatography coupled with liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) metabolomics approach was used to evaluate the metabolic perturbation in pregnant rats caused by BX and JBX.

MATERIALS AND METHODS

Placenta and amniotic fluid samples were collected from control Sprague-Dawley pregnant rats and exposed to BX suspension and JBX decoction (1.434g/kg/day). Samples were analyzed using LC-MS and GC-MS. The acquired MS data of above samples were further subjected to multivariate data analysis, and the significantly altered metabolites were identified. The associated pathways were constructed using MetaboAnalyst 3.0.

RESULTS

The weight and histopathology of the placenta from each group of rats had no definite difference. However, we found 20 differential endogenous metabolites that changed significantly in the placenta and amniotic fluid samples. The alterations of identified metabolites indicated a perturbation in glycerophospholipid metabolism, amino acid metabolism, and carbohydrate metabolism in pregnant rats exposed to BX and JBX.

CONCLUSION

In summary, this work suggested that oral administration of BX and JBX may induce disturbances in the intermediary metabolism in pregnant rats. This work contributes to further understanding the safety of BX and its processed products.

Serum Metabolomic Characterization of Liver Fibrosis in Rats and Anti-Fibrotic Effects of Yin-Chen-Hao-Tang

Yin-Chen-Hao-Tang (YCHT) is a famous Chinese medicine formula which has long been used in clinical practice for treating various liver diseases, such as liver fibrosis. However, to date, the mechanism for its anti-fibrotic effects remains unclear. In this paper, an ultra-performance liquid chromatography-time-of-flight mass spectrometry (UPLC-TOF-MS)-based metabolomic study was performed to characterize dimethylnitrosamine (DMN)-induced liver fibrosis in rats and evaluate the therapeutic effects of YCHT. Partial least squares-discriminant analysis (PLS-DA) showed that the model group was well separated from the control group, whereas the YCHT-treated group exhibited a tendency to restore to the controls. Seven significantly changed fibrosis-related metabolites, including unsaturated fatty acids and lysophosphatidylcholines (Lyso-PCs), were identified. Moreover, statistical analysis demonstrated that YCHT treatment could reverse the levels of most metabolites close to the normal levels. These results, along with histological and biochemical examinations, indicate that YCHT has anti-fibrotic effects, which may be due to the suppression of oxidative stress and resulting lipid peroxidation involved in hepatic fibrogenesis. This study offers new opportunities to improve our understanding of liver fibrosis and the anti-fibrotic mechanisms of YCHT.

A Simultaneous Metabolic Profiling and Quantitative Multimetabolite Metabolomic Method for Human Plasma Using Gas-Chromatography Tandem Mass Spectrometry

For the first time it is possible to simultaneously collect targeted and nontargeted metabolomics data from plasma based on GC with high scan speed tandem mass spectrometry (GC-MS/MS). To address the challenge of getting broad metabolome coverage while quantifying known biomarker compounds in high-throughput GC-MS metabolomics, we developed a novel GC-MS/MS metabolomics method using a high scan speed (20 000 Da/second) GC-MS/MS that enables simultaneous data acquisition of both nontargeted full scan and targeted quantitative tandem mass spectrometry data. The combination of these two approaches has hitherto not been demonstrated in metabolomics. This method allows reproducible quantification of at least 37 metabolites using multiple reaction monitoring (MRM) and full mass spectral scan-based detection of 601 reproducible metabolic features from human plasma. The method showed good linearity over normal concentrations in plasma (0.06-343 to 0.86-4800 μM depending on the metabolite) and good intra- and interbatch precision (0.9-16.6 and 2.6-29.6% relative standard deviation). Based on the parameters determined for this method, targeted quantification using MRM can be expanded to cover at least 508 metabolites while still collecting full scan data. The new simultaneous targeted and nontargeted metabolomics method enables more sensitive and accurate detection of predetermined metabolites and biomarkers of interest, while still allowing detection and identification of unknown metabolites. This is the first validated GC-MS/MS metabolomics method with simultaneous full scan and MRM data collection, and clearly demonstrates the utility of GC-MS/MS with high scanning rates for complex analyses.

Metabolome analyses in exposome studies: Profiling methods for a vast chemical space

Metabolic profiling (metabonomics/metabolomics) is now used routinely as a tool to provide information-rich datasets for biomarker discovery, prompting and augmenting detailed mechanistic studies. The experimental design and focus of any individual study will be reflected in the types of biomarkers that can be detected; toxicological studies will likely focus on markers of response to insult, whereas clinical case-control studies may yield diagnostic markers of disease. Population studies can make use of omics analyses, including metabonomics, to provide mechanistically-relevant markers that link environmental exposures to chronic disease endpoints. In this article, examples of how metabolic profiling has played a key role in molecular epidemiological analyses of chronic disease are presented, and how these reflect different aspects of the causal pathway. A commentary on the nature of metabolome analysis as a complex mixture problem as opposed to a coded, sequence or template problem is provided, alongside an overview of current and future analytical platforms that are being applied to meet this analytical challenge. Epidemiological studies are an important nexus for integrating various measures of the human exposome, and the ubiquity, diversity and functions of small molecule metabolites, represent an important way to link individual exposures, genetics and phenotype.

Metabolic profiling in human exposome studies

The human metabolome-the complement of small molecule metabolites present in biofluids and tissues-represents a significant part of the internal chemical milieu and is therefore an important aspect of the human exposome. Metabolic profiling approaches, commonly referred to as metabonomics or metabolomics, permit detailed and efficient characterisation of human biospecimens; application to population studies holds great promise for uncovering new associations and causal relationships between environmental factors and chronic disease. In addition to the insight metabolic information can provide, metabolic phenotypes anchor other molecular readouts and help formulate a systems-level interpretation of biological phenomena. In this commentary, we discuss the general approach for applying metabolic profiling in exposome studies, alongside recent exemplars. We also comment on the complexity and dynamism of the metabolome and highlight both the limitations such properties impart and the requirements for dealing with such issues in real-world phenotyping studies. Given that several large-scale exposome studies are now underway, we offer a perspective on current and future challenges that will need to be addressed to maximise their utility in environmental health research.

Entometabolomics: applications of modern analytical techniques to insect studies

Metabolomic analyses can reveal associations between an organism's metabolome and further aspects of its phenotypic state, an attractive prospect for many life-sciences researchers. The metabolomic approach has been employed in some, but not many, insect study systems, starting in 1990 with the evaluation of the metabolic effects of parasitism on moth larvae. Metabolomics has now been applied to a variety of aspects of insect biology, including behaviour, infection, temperature stress responses, CO 2 sedation, and bacteria-insect symbiosis. From a technical and reporting standpoint, these studies have adopted a range of approaches utilising established experimental methodologies. Here, we review current literature and evaluate the metabolomic approaches typically utilised by entomologists. We suggest that improvements can be made in several areas, including sampling procedures, the reduction in sampling and equipment variation, the use of sample extracts, statistical analyses, confirmation, and metabolite identification. Overall, it is clear that metabolomics can identify correlations between phenotypic states and underlying cellular metabolism that previous, more targeted, approaches are incapable of measuring. The unique combination of untargeted global analyses with high-resolution quantitative analyses results in a tool with great potential for future entomological investigations.

Potential of serum metabolites for diagnosing post-stroke cognitive impairment

A panel of serum metabolite markers (glutamine, kynurenine, and LysoPC(18:2)) was identified as candidate diagnostic biomarkers for post-stroke cognitive impairment.

1H NMR global metabolic phenotyping of acute pancreatitis in the emergency unit

We have investigated the urinary and plasma metabolic phenotype of acute pancreatitis (AP) patients presenting to the emergency room at a single center London teaching hospital with acute abdominal pain using (1)H NMR spectroscopy and multivariate modeling. Patients were allocated to either the AP (n = 15) or non-AP patients group (all other causes of abdominal pain, n = 21) on the basis of the national guidelines. Patients were assessed for three clinical outcomes: (1) diagnosis of AP, (2) etiology of AP caused by alcohol consumption and cholelithiasis, and (3) AP severity based on the Glasgow score. Samples from AP patients were characterized by high levels of urinary ketone bodies, glucose, plasma choline and lipid, and relatively low levels of urinary hippurate, creatine and plasma-branched chain amino acids. AP could be reliably identified with a high degree of sensitivity and specificity (OPLS-DA model R(2) = 0.76 and Q(2)Y = 0.59) using panel of discriminatory biomarkers consisting of guanine, hippurate and creatine (urine), and valine, alanine and lipoproteins (plasma). Metabolic phenotyping was also able to distinguish between cholelithiasis and colonic inflammation among the heterogeneous non-AP group. This work has demonstrated that combinatorial biomarkers have a strong diagnostic and prognostic potential in AP with relevance to clinical decision making in the emergency unit.

Metabolomic analysis of lung epithelial secretions in rats: an investigation of bronchoalveolar lavage fluid by GC-MS and FT-IR

OBJECTIVE

Rat bronchoalveolar lavage fluid (BALF) metabolome can be used to obtain valuable, precise, and accurate information about underlying lung conditions in an experiment. The present study focuses on the evaluation of the lung epithelium metabolome in a rat model using techniques including bronchoalveolar lavage, gas chromatography-mass spectroscopy (GC-MS), and Fourier transform infrared spectroscopy (FT-IR).

MATERIALS AND METHODS

Untargeted metabolites in BALF were extracted in ethyl acetate and derivatized by standard methods for the analysis by GC-MS. FT-IR spectra of ethyl acetate extract of BALF were obtained and read for the characteristic fingerprint of rats under investigation. Analyses were done in individual animals to obtain consistent data. BALF cells were counted by flow cytometry to monitor any inflammatory condition in rats.

RESULTS

FT-IR analysis finds two peaks which are characteristically different from the extract medium, which is ethyl acetate. FT-IR peaks correspond to that of amino acids and carbohydrates, including β-D-glucose, α-D-glucose, and β-D-galactose. GC-MS evaluation of the BALF finds several products of the metabolism or its participants. Main compounds in the BALF detected by GC-MS include succinate, fumarate, glycine, alanine, 2-methyl-3-oxovaleric acid, dodecanoic acid, tetradecanoic acid, hexadecanoic acid, octanoic acid, trans-9-octadecanoic acid, octadecanoic acid, and Prostaglandin F1α.

CONCLUSION

Several research reports reveal metabolomic parameters in murine model lung tissue or BALF, but they rarely reported a complete metabolomics model profile, particularly in rats. The present data of GC-MS and FT-IR suggest that the set up can be exploited to study metabolomic alterations in several lung conditions including acute lung toxicity, inflammation, asthma, bronchitis, fibrosis, and emphysema.

Design and analysis of metabolomics studies in epidemiologic research: a primer on -omic technologies

Metabolomics is the field of "-omics" research concerned with the comprehensive characterization of the small low-molecular-weight metabolites in biological samples. In epidemiology, it represents an emerging technology and an unprecedented opportunity to measure environmental and other exposures with improved precision and far less measurement error than with standard epidemiologic methods. Advances in the application of metabolomics in large-scale epidemiologic research are now being realized through a combination of improved sample preparation and handling, automated laboratory and processing methods, and reduction in costs. The number of epidemiologic studies that use metabolic profiling is still limited, but it is fast gaining popularity in this area. In the present article, we present a roadmap for metabolomic analyses in epidemiologic studies and discuss the various challenges these data pose to large-scale studies. We discuss the steps of data preprocessing, univariate and multivariate data analysis, correction for multiplicity of comparisons with correlated data, and finally the steps of cross-validation and external validation. As data from metabolomic studies accumulate in epidemiology, there is a need for large-scale replication and synthesis of findings, increased availability of raw data, and a focus on good study design, all of which will highlight the potential clinical impact of metabolomics in this field.

Evaluation of horse urine sample preparation methods for metabolomics using LC coupled to HRMS

Background: Horse urine is the medium of choice for the implementation of metabolomic approaches aimed at improving horse doping control. However, drug analysis in this biofluid is a challenging task due to the presence of large amounts of interfering compounds. Methodology & Results: A comparative study of sample preparation has been conducted to evaluate five sample-preparation methods, namely acetonitrile precipitation, proteinase K hydrolysis, membrane filtration and sample dilution with water by factors of five and 20, for metabolome analysis using liquid chromatography coupled to high resolution mass spectrometry. Assessment was performed at both global and targeted levels, by using a few thousand features obtained from peak detection software, and internal standards and 100 annotated or identified metabolites. Conclusion: By considering the number of detected signals, their intensity and their detection repeatability, acetonitrile precipitation was selected as the most efficient sample-preparation method for the analysis of horse urine metabolome in liquid chromatography coupled to high resolution mass spectrometry conditions.

Potential input from metabolomics for exploring and understanding the links between environment and health

Humans may be exposed via their environment to multiple chemicals as a consequence of human activities and use of synthetic products. Little knowledge is routinely generated on the hazards of these chemical mixtures. The metabolomic approach is widely used to identify metabolic pathways modified by diseases, drugs, or exposures to toxicants. This review, based on the state of the art of the current applications of metabolomics in environmental health, attempts to determine whether metabolomics might constitute an original approach to the study of associations between multiple, low-dose environmental exposures in humans. Studying the biochemical consequences of complex environmental exposures is a challenge demanding the development of careful experimental and epidemiological designs, in order to take into account possible confounders associated with the high level of interindividual variability induced by different lifestyles. The choices of populations studied, sampling and storage procedures, statistical tools used, and system biology need to be considered. Suggestions for improved experimental and epidemiological designs are described. Evidence indicates that metabolomics may be a powerful tool in environmental health in the identification of both complex exposure biomarkers directly in human populations and modified metabolic pathways, in an attempt to improve understanding the underlying environmental causes of diseases. Nevertheless, the validity of biomarkers and relevancy of animal-to-human extrapolation remain key challenges that need to be properly explored.

Volume determination with two standards allows absolute quantification and improved chemometric analysis of metabolites by NMR from submicroliter samples

The accurate measurement of metabolite concentrations in miniscule biological sample volumes is often desirable, yet it remains challenging. In many cases, the starting analyte volumes are imprecisely known, or not directly measurable, and hence absolute metabolite concentrations are difficult to calculate. Here, we introduce volume determination using two standards (VDTS) as a general quantitative method for the analysis of polar metabolites in submicrolitre samples using ¹H NMR spectroscopy. This approach permits the back calculation of absolute metabolite concentrations from small biological samples of unknown volume. Where small sample volumes are also variable, VDTS can improve multivariate chemometric analysis. In this context, principal component analysis (PCA) yielded more logically consistent and biologically insightful outputs when we used volume-corrected spectra, calculated using VDTS, rather than probabilistic quotient normalization (PQN) of raw spectra. As proof-of-principle, the VDTS-based method and PCA were used to analyze polar metabolites in the hemolymph (blood) extracted from larvae of the very small but widely used genetic model organism Drosophila. This analysis showed that the hemolymph metabolomes of males and females are markedly different when larvae are well fed. However, gender-specific metabolomes tend to converge when larval dietary nutrients are restricted. We discuss the biological implications of these surprising results and compare and contrast them to previous analyses of Drosophila hemolymph and mammalian blood plasma. Together, these findings reveal an interesting and hitherto unknown sexual dimorphism in systemic Drosophila metabolites, clearly warranting further biological investigation. Importantly, the VDTS approach should be adaptable to many different analytical platforms, including mass spectrometry.

1H-NMR metabolic profiling of cerebrospinal fluid in patients with complex regional pain syndrome-related dystonia

In complex regional pain syndrome (CRPS)-related dystonia, compelling evidence points to the involvement of the central nervous system, but the underpinning pathobiology is still unclear. Thus, to enable a hypothesis-free, unbiased view of the problem and to obtain new insight into the pathobiology of dystonia in CRPS, we applied an exploratory metabolomics analysis of cerebrospinal fluid (CSF) of patients with CRPS-related dystonia. (1)H-NMR spectroscopy in combination with multivariate modeling were used to investigate metabolic profiles of a total of 105 CSF samples collected from patients with CRPS-related dystonia and controls. We found a significantly different metabolic profile of CSF in CRPS patients compared to controls. The differences were already reflected in the first two principal components of the principal component analysis model, which is an indication that the variance associated with CRPS is stronger than variance caused by such classical confounders as gender, age, or individual differences. A supervised analysis generated a strong model pinpointing the most important metabolites contributed to the metabolic signature of patients with CRPS-related dystonia. From the set of identified discriminators, the most relevant metabolites were 2-keto-isovalerate, glucose, glutamine, and lactate, which all showed increased concentrations, and urea, which showed decreased concentration in CRPS subjects. Our findings point at a catabolic state in chronic CRPS patients with dystonia that is likely associated with inflammation.

A Systematic, Integrated Study on the Neuroprotective Effects of Hydroxysafflor Yellow A Revealed by (1)H NMR-Based Metabonomics and the NF-κB Pathway

Hydroxysafflor yellow A (HSYA) is the main active component of the Chinese herb Carthamus tinctorius L.. Purified HSYA is used as a neuroprotective agent to prevent cerebral ischemia. Injectable safflor yellow (50 mg, containing 35 mg HSYA) is widely used to treat patients with ischemic cardiocerebrovascular disease. However, it is unknown how HSYA exerts a protective effect on cerebral ischemia at the molecular level. A systematical integrated study, including histopathological examination, neurological evaluation, blood-brain barrier (BBB), metabonomics, and the nuclear factor-κB (NF-κB) pathway, was applied to elucidate the pathophysiological mechanisms of HSYA neuroprotection at the molecular level. HSYA could travel across the BBB, significantly reducing the infarct volume and improving the neurological functions of rats with ischemia. Treatment with HSYA could lead to relative corrections of the impaired metabolic pathways through energy metabolism disruption, excitatory amino acid toxicity, oxidative stress, and membrane disruption revealed by (1)H NMR-based metabonomics. Meanwhile, HSYA treatment inhibits the NF-κB pathway via suppressing proinflammatory cytokine expression and p65 translocation and binding activity while upregulating an anti-inflammatory cytokine.

An efficient spectra processing method for metabolite identification from 1H-NMR metabolomics data

The spectra processing step is crucial in metabolomics approaches, especially for proton NMR metabolomics profiling. During this step, noise reduction, baseline correction, peak alignment and reduction of the 1D (1)H-NMR spectral data are required in order to allow biological information to be highlighted through further statistical analyses. Above all, data reduction (binning or bucketing) strongly impacts subsequent statistical data analysis and potential biomarker discovery. Here, we propose an efficient spectra processing method which also provides helpful support for compound identification using a new data reduction algorithm that produces relevant variables, called buckets. These buckets are the result of the extraction of all relevant peaks contained in the complex mixture spectra, rid of any non-significant signal. Taking advantage of the concentration variability of each compound in a series of samples and based on significant correlations that link these buckets together into clusters, the method further proposes automatic assignment of metabolites by matching these clusters with the spectra of reference compounds from the Human Metabolome Database or a home-made database. This new method is applied to a set of simulated (1)H-NMR spectra to determine the effect of some processing parameters and, as a proof of concept, to a tomato (1)H-NMR dataset to test its ability to recover the fruit extract compositions. The implementation code for both clustering and matching steps is available upon request to the corresponding author.

(1)H NMR-based metabolic profiling of urinary tract infection: combining multiple statistical models and clinical data

Urinary tract infection (UTI) encompasses a variety of clinical syndromes ranging from mild to life-threatening conditions. As such, it represents an interesting model for the development of an analytically based scoring system of disease severity and/or host response. Here we test the feasibility of this concept using (1)H NMR based metabolomics as the analytical platform. Using an exhaustively clinically characterized cohort and taking advantage of the multi-level study design, which opens possibilities for case-control and longitudinal modeling, we were able to identify molecular discriminators that characterize UTI patients. Among those discriminators a number (e.g. acetate, trimethylamine and others) showed association with the degree of bacterial contamination of urine, whereas others, such as, for instance, scyllo-inositol and para-aminohippuric acid, are more likely to be the markers of morbidity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-012-0411-y) contains supplementary material, which is available to authorized users.

New opportunities from the cancer metabolome

BACKGROUND

Metabolomics, the study of all metabolites produced in the body, which often includes flora and drug metabolites, is the omics approach that can be considered most closely related to a patient's phenotype. Metabolomics has a great and largely untapped potential in the field of oncology, and the analysis of the cancer metabolome to identify biofluid markers and novel druggable targets can now be undertaken in many research laboratories.

CONTENT

The cancer metabolome has been used to identify and begin to evaluate potential biomarkers and therapeutic targets in a variety of malignancies, including breast, prostate, and kidney cancer. We discuss the several standard techniques for metabolite separation and identification, with their potential problems and drawbacks. Validation of biomarkers and targets may entail intensive use of labor and technology and generally requires a large number of study participants as well as laboratory validation studies. The field of pharmacometabolomics, in which specific therapies are chosen on the basis of a patient's metabolomic profile, has shown some promise in the translation of metabolomics into the arena of personalized medicine.

SUMMARY

The relatively new approach using metabolomics has just begun to enter the mainstream of cancer diagnostics and therapeutics. As this field advances, metabolomics will take its well-deserved place next to genomics, transcriptomics, and proteomics in both clinical and basic research in oncology.