LC-MS-based holistic metabolic profiling. Problems, limitations, advantages, and future perspectives

Metabolic Profiling: A Perspective on the Current Status, Challenges, and Future Directions

Metabolic profiling continues to develop, and research is now conducted on this topic globally in hundreds of laboratories, from small groups up to national centers and core facilities. Here we briefly provide a perspective on the current status and challenges facing metabolic phenotyping (metabonomics/metabolomics) and consider future directions for this important area of biomarker and systems biology research.

Rat Fecal Metabolomics-Based Analysis

The gut's symbiome, a hidden metabolic organ, has gained scientific interest for its crucial role in human health. Acting as a biochemical factory, the gut microbiome produces numerous small molecules that significantly impact host metabolism. Metabolic profiling facilitates the exploration of its influence on human health and disease through the symbiotic relationship. Fecal metabolomics-based analysis is an indisputably valuable tool for elucidating the biochemistry of digestion and absorption in the gastrointestinal system, serving as the most suitable specimen to study the symbiotic relationship between the host and the intestinal microbiota. It is well-established that the balance of the intestinal microbiota changes in response to various stimuli, both physiological, such as gender, age, diet, and exercise, and pathological, such as gastrointestinal and hepatic diseases. Fecal samples have been analyzed using widely adopted analytical techniques, including NMR spectroscopy, GC-MS, and LC-MS/MS. Rat fecal samples are frequently used and particularly useful substrates for metabolomics-based studies in related fields.The complexity and diversity of fecal samples necessitate careful and skillful handling to extract metabolites, while avoiding their deterioration, effectively and quantitatively. Several determinative factors, such as the fecal sample weight to extraction solvent solution volume, the nature and pH value of the extraction solvent, and the homogenization process, play crucial roles in achieving optimal extraction for obtaining high-quality metabolic fingerprints, whether for untargeted or targeted metabolomics.

Quality Control and Validation Issues in LC-MS-Based Metabolomics

Metabolic profiling performed using untargeted metabolomics of different, complex biological samples aims to apply agnostic/holistic, hypothesis-free, analysis of the small molecules that are present in the analyzed sample. This approach has been the center of major investments and dedicated efforts from the research community for many years. However, limitations and challenges remain, particularly with regard to the validation and the quality of the obtained results. This has led to increasing community engagement, with the formation of think tanks, the establishment of working groups, and the many seminars on quality control (QC) in metabolomics. Here we describe a quality control (QC) protocol used to monitor LC-MS-based metabolomics analysis. A key target is the monitoring of analytical precision. This methodology is described for the analysis of urine but can be applied to different biological matrices, such as various biofluids, cell, and tissue extracts.

Metabolomics: Unveiling biological matrices in precision nutrition and health

Precision nutrition, an expanding field at the intersection of nutrition science and personalized medicine, is rapidly evolving with metabolomics integration. Metabolomics, facilitated by advanced technologies like mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, facilitates comprehensive profiling of metabolites across diverse biological samples. From the perspective of health care systems, precision nutrition gains relevance due to the substantial impact of prevalent non-communicable diseases (NCDs) on societal well-being, which is directly linked with dietary habits and eating behavior. Furthermore, biomarker products derived from metabolomics have been utilized in Europe, the USA, and Brazil to understand metabolic dysregulations and tailor diets accordingly. Despite its burgeoning status, metabolomics holds great potential in revolutionizing nutritional science, particularly with the integration of artificial intelligence and machine learning, offering novel insights into personalized dietary interventions and disease prediction. This narrative review emphasizes the transformative impact of metabolomics in precision and delineates avenues for future research and application, paving the way for a more tailored and practical approach to nutrition management.

Ciguatera poisoning: A review of the ecology and detection methods for Gambierdiscus and Fukuyoa species

Ciguatera poisoning is the most prevalent non-bacterial seafood illness globally, with an estimated 10,000 to 50,000 human cases reported annually. While most symptoms are generally mild, some cases can result in severe and long-lasting neurological and psychological damage, and in some instances, even death. The known causative agents of ciguatera poisoning are benthic toxic dinoflagellate species belonging to the genera Gambierdiscus and Fukuyoa. These species produce highly potent ciguatoxins that bioaccumulate through the marine food chain, eventually reaching humans through seafood consumption. Although Gambierdiscus and Fukuyoa species are widespread in tropical waters worldwide, the full extent of their distribution remains uncertain. This review provides a detailed examination of the ecological dynamics of these dinoflagellates and explores the diverse range of detection methods used to monitor them. These include a focus on molecular techniques for detection, alongside morphological methods, emerging technologies, and a toxin detection overview. Additionally, we offer recommendations on how the field can advance, highlighting novel solutions and next steps for improving detection and monitoring practices. By assessing the strengths and limitations of current approaches and proposing directions for future research, this review aims to support efforts in better understanding and mitigating the risk of ciguatera poisoning.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Real-Time Monitoring of Molecules in Aqueous Solution via a Surface-Functionalized Ag-Anodic Aluminum Oxide Surface-Enhanced Raman Scattering Platform

Real-time monitoring of molecular species in aqueous solutions is crucial for diverse scientific applications, from biomedical diagnostics to environmental analysis. In this study, we investigate the selective detection and discrimination of specific molecules in aqueous solution samples using a Ag-coated anodized aluminum oxide (Ag-AAO) surface functionalized with thiol molecules. Our investigation harnesses the power of surface-enhanced Raman scattering (SERS) synergized with principal component analysis (PCA) to elucidate the distinctive signatures of aqueous dopamine and l-tyrosine molecules. By scrutinizing the Raman spectra of surface-treated molecules, we unveil nuanced variations driven by the unique functional groups of the thiol molecules and their dynamic interactions with the target molecules in solution. Notably, we observe different alterations in the SERS spectra of Ag-AAO surface-functionalized boronic acid molecules for detection of dopamine and l-tyrosine, even at a concentration as low as 10-8 M. Moreover, the spectral PCA elucidates the discrimination of dopamine and l-tyrosine within the aqueous environment attributed to the different molecular interactions near SERS-active hotspots. Our findings facilitate real-time monitoring of minute analytes with exceptional molecular selectivity, ushering in an era of precise chemical analysis in aqueous solutions.

Application of untargeted liquid chromatography-mass spectrometry to routine analysis of food using three-dimensional bucketing and machine learning

For the detection of food adulteration, sensitive and reproducible analytical methods are required. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is a highly sensitive method that can be used to obtain analytical fingerprints consisting of a variety of different components. Since the comparability of measurements carried out with different devices and at different times is not given, specific adulterants are usually detected in targeted analyses instead of analyzing the entire fingerprint. However, this comprehensive analysis is desirable in order to stay ahead in the race against food fraudsters, who are constantly adapting their adulterations to the latest state of the art in analytics. We have developed and optimized an approach that enables the separate processing of untargeted LC‑HRMS data obtained from different devices and at different times. We demonstrate this by the successful determination of the geographical origin of honey samples using a random forest model. We then show that this approach can be applied to develop a continuously learning classification model and our final model, based on data from 835 samples, achieves a classification accuracy of 94% for 126 test samples from 6 different countries.

Metabolic signature of follicular fluid in infertility-related diseases: a narrative review

Metabolomics offers new methods to identify biomarkers for oocyte and embryo quality, and for a better understanding of the physiopathology of infertility. This review investigated the latest findings regarding metabolome-derived biomarkers in follicular fluid of women with the most common types of infertility, and the potential impact on reproductive medicine outcomes. PubMed was searched for publications on metabolomics and human follicular fluid, and key biomarkers, kinetics and relationships with infertility diseases were identified. A reduced concentration of glucose and increased concentrations of lactate and pyruvate were found in follicular fluid of patients with endometriosis and diminished ovarian reserve, and the opposite was found in patients with polycystic ovary syndrome. These signatures may lead to the hypothesis of changed metabolite concentrations in patients with endometriosis and diminished ovarian reserve, and a metabolic pathway alteration with decreased aerobic glycolysis in patients with polycystic ovary syndrome. However, the pattern found in patients with endometriosis and low responders may also be expected in follicular fluid of fertile women. Larger studies are needed to confirm the results. An international database may help to highlight follicular fluid biomarkers in order to improve the selection of cryopreserved oocytes, and to enrich culture medium to restore normal metabolism and improve reproductive treatment outcomes.

Increased Vasoactive Intestinal Peptide (VIP) in polycystic ovary syndrome patients undergoing IVF

Introduction

Polycystic ovary syndrome (PCOS) is a common multifactorial and polygenic disorder of the endocrine system, affecting up to 20% of women in reproductive age with a still unknown etiology. Follicular fluid (FF) represents an environment for the normal development of follicles rich in metabolites, hormones and neurotransmitters, but in some instances of PCOS the composition can be different. Vasoactive intestinal peptide (VIP) is an endogenous autonomic neuropeptide involved in follicular atresia, granulosa cell physiology and steroidogenesis.

Methods

ELISA assays were performed to measure VIP and estradiol levels in human follicular fluids, while AMH, FSH, LH, estradiol and progesterone in the plasma were quantified by chemiluminescence. UHPLC/QTOF was used to perform the untargeted metabolomic analysis.

Results

Our ELISA and metabolomic results show: i) an increased concentration of VIP in follicular fluid of PCOS patients (n=9) of about 30% with respect to control group (n=10) (132 ± 28 pg/ml versus 103 ± 26 pg/ml, p=0,03) in women undergoing in vitro fertilization (IVF), ii) a linear positive correlation (p=0.05, r=0.45) between VIP concentration and serum Anti-Müllerian Hormone (AMH) concentration and iii) a linear negative correlation between VIP and noradrenaline metabolism. No correlation between VIP and estradiol (E2) concentration in follicular fluid was found. A negative correlation was found between VIP and noradrenaline metabolite 3,4-dihydroxyphenylglycolaldehyde (DOPGAL) in follicular fluids.

Conclusion

VIP concentration in follicular fluids was increased in PCOS patients and a correlation was found with noradrenaline metabolism indicating a possible dysregulation of the sympathetic reflex in the ovarian follicles. The functional role of VIP as noradrenergic modulator in ovarian physiology and PCOS pathophysiology was discussed.

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Quality Control in Targeted GC-MS for Amino Acid-OMICS

Gas chromatography-mass spectrometry (GC-MS) is suitable for the analysis of non-polar analytes. Free amino acids (AA) are polar, zwitterionic, non-volatile and thermally labile analytes. Chemical derivatization of AA is indispensable for their measurement by GC-MS. Specific conversion of AA to their unlabeled methyl esters (d0Me) using 2 M HCl in methanol (CH3OH) is a suitable derivatization procedure (60 min, 80 °C). Performance of this reaction in 2 M HCl in tetradeutero-methanol (CD3OD) generates deuterated methyl esters (d3Me) of AA, which can be used as internal standards in GC-MS. d0Me-AA and d3Me-AA require subsequent conversion to their pentafluoropropionyl (PFP) derivatives for GC-MS analysis using pentafluoropropionic anhydride (PFPA) in ethyl acetate (30 min, 65 °C). d0Me-AA-PFP and d3Me-AA-PFP derivatives of AA are readily extractable into water-immiscible, GC-compatible organic solvents such as toluene. d0Me-AA-PFP and d3Me-AA-PFP derivatives are stable in toluene extracts for several weeks, thus enabling high throughput quantitative measurement of biological AA by GC-MS using in situ prepared d3Me-AA as internal standards in OMICS format. Here, we describe the development of a novel OMICS-compatible QC system and demonstrate its utility for the quality control of quantitative analysis of 21 free AA and metabolites in human plasma samples by GC-MS as Me-PFP derivatives. The QC system involves cross-standardization of the concentrations of the AA in their aqueous solutions at four concentration levels and a quantitative control of AA at the same four concentration levels in pooled human plasma samples. The retention time (tR)-based isotope effects (IE) and the difference (δ(H/D) of the retention times of the d0Me-AA-PFP derivatives (tR(H)) and the d3Me-AA-PFP derivatives (tR(D)) were determined in study human plasma samples of a nutritional study (n = 353) and in co-processed QC human plasma samples (n = 64). In total, more than 400 plasma samples were measured in eight runs in seven working days performed by a single person. The proposed QC system provides information about the quantitative performance of the GC-MS analysis of AA in human plasma. IE, δ(H/D) and a massive drop of the peak area values of the d3Me-AA-PFP derivatives may be suitable as additional parameters of qualitative analysis in targeted GC-MS amino acid-OMICS.

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping

In recent years, feces has surfaced as the matrix of choice for investigating the gut microbiome-health axis because of its non-invasive sampling and the unique reflection it offers of an individual's lifestyle. In cohort studies where the number of samples required is large, but availability is scarce, a clear need exists for high-throughput analyses. Such analyses should combine a wide physicochemical range of molecules with a minimal amount of sample and resources and downstream data processing workflows that are as automated and time efficient as possible. We present a dual fecal extraction and ultra high performance liquid chromatography-high resolution-quadrupole-orbitrap-mass spectrometry (UHPLC-HR-Q-Orbitrap-MS)-based workflow that enables widely targeted and untargeted metabolome and lipidome analysis. A total of 836 in-house standards were analyzed, of which 360 metabolites and 132 lipids were consequently detected in feces. Their targeted profiling was validated successfully with respect to repeatability (78% CV < 20%), reproducibility (82% CV < 20%), and linearity (81% R² > 0.9), while also enabling holistic untargeted fingerprinting (15,319 features, CV < 30%). To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. The latter was benchmarked toward vendor-specific targeted and untargeted software and our isotopologue parameter optimization/XCMS-based untargeted pipeline in LifeLines Deep cohort samples (n = 97). TaPEx clearly outperformed the untargeted approaches (81.3 vs 56.7-66.0% compounds detected). Finally, our novel dual fecal metabolomics-lipidomics-TaPEx method was successfully applied to Flemish Gut Flora Project cohort (n = 292) samples, leading to a sample-to-result time reduction of 60%.

1α,25-Dihydroxyvitamin D3 Improves Follicular Development and Steroid Hormone Biosynthesis by Regulating Vitamin D Receptor in the Layers Model

1α,25-Dihydroxyvitamin D₃ (VitD₃) is the active form of vitamin D, and it regulates gene expression and protein synthesis in mammalian follicle development. However, the function of VitD₃ in the follicular development of layers remains unclear. This study investigated, through in vivo and in vitro experiments, the effects of VitD₃ on follicle development and steroid hormone biosynthesis in young layers. In vivo, ninety 18-week-old Hy-Line Brown laying hens were randomly divided into three groups for different treatments of VitD₃ (0, 10, and 100 μg/kg). VitD₃ supplementation promoted follicle development, increasing the number of small yellow follicles (SYFs) and large yellow follicles (LYFs) and the thickness of the granulosa layer (GL) of SYFs. Transcriptome analysis revealed that VitD₃ supplementation altered gene expression in the ovarian steroidogenesis, cholesterol metabolism, and glycerolipid metabolism signaling pathways. Steroid hormone-targeted metabolomics profiling identified 20 steroid hormones altered by VitD₃ treatment, with 5 being significantly different among the groups. In vitro, it was found that VitD₃ increased cell proliferation, promoted cell-cycle progression, regulated the expression of cell-cycle-related genes, and inhibited the apoptosis of granulosa cells from pre-hierarchical follicles (phGCs) and theca cells from prehierarchical follicles (phTCs). In addition, the steroid hormone biosynthesis-related genes, estradiol (E2) and progesterone (P4) concentrations, and vitamin D receptor (VDR) expression level was significantly altered by VitD₃. Our findings identified that VitD₃ altered the gene expression related to steroid metabolism and the production of testosterone, estradiol, and progesterone in the pre-hierarchical follicles (PHFs), resulting in positive effects on poultry follicular development.

Machine learning-empowered cis-diol metabolic fingerprinting enables precise diagnosis of primary liver cancer

Cis-diol metabolic reprogramming evolves during primary liver cancer (PLC) initiation and progression. However, owing to the low concentrations and highly structural heterogeneity of cis-diols in vivo, severe interference from complex biofluids and limited profiling coverage of existing methods, in-depth profiling of cis-diol metabolites and linking their specific changes with PLC remain challenging. Besides, due to the low specificity of widely used protein biomarkers, accurate classification of PLC from hepatitis still represents an unmet need in clinical diagnostics. Herein, to high-coverage profile cis-diols and explore the translational potential of them as biomarkers, a machine learning-empowered boronate affinity extraction-solvent evaporation assisted enrichment-mass spectrometry (MLE-BESE-MS) was developed. A single analytical platform integrated with multiple complementary functions, including pH-controlled boronate affinity extraction, solvent evaporation-assisted enrichment and nanoelectrospray ionization-based cis-diol identification, was constructed, which significantly improved the metabolite coverage. Meanwhile, by virtue of machine learning (principal components analysis, orthogonal partial least-squares discrimination analysis and random forest), collected cis-diols were statistically screened to extract efficient features for precise PLC diagnosis, and the results outperform the routinely used protein biomarker-based methods both in sensitivity (87.5% vs. less than 70%) and specificity (85.7% vs. ca. 80%). This machine learning-empowered integrated MS platform advanced the targeted metabolic analysis for early cancer diagnosis, rendering great promise for clinical translation.

Untargeted profiling of secondary metabolites and phytotoxins associated with stemphylium blight of lentil

Stemphylium botryosum alters lentil secondary metabolism and differentially affects resistant and susceptible genotypes. Untargeted metabolomics identifies metabolites and their potential biosynthetic pathways that play a crucial role in resistance to S. botryosum. The molecular and metabolic processes that mediate resistance to stemphylium blight caused by Stemphylium botryosum Wallr. in lentil are largely unknown. Identifying metabolites and pathways associated with Stemphylium infection may provide valuable insights and novel targets to breed for enhanced resistance. The metabolic changes following infection of four lentil genotypes by S. botryosum were investigated by comprehensive untargeted metabolic profiling employing reversed-phase or hydrophilic interaction liquid chromatography (HILIC) coupled to a Q-Exactive mass spectrometer. At the pre-flowering stage, plants were inoculated with S. botryosum isolate SB19 spore suspension and leaf samples were collected at 24, 96 and 144 h post-inoculation (hpi). Mock-inoculated plants were used as negative controls. After analyte separation, high-resolution mass spectrometry data was acquired in positive and negative ionization modes. Multivariate modeling revealed significant treatment, genotype and hpi effects on metabolic profile changes that reflect lentil response to Stemphylium infection. In addition, univariate analyses highlighted numerous differentially accumulated metabolites. By contrasting the metabolic profiles of SB19-inoculated and mock-inoculated plants and among lentil genotypes, 840 pathogenesis-related metabolites were detected including seven S. botryosum phytotoxins. These metabolites included amino acids, sugars, fatty acids and flavonoids in primary and secondary metabolism. Metabolic pathway analysis revealed 11 significant pathways including flavonoid and phenylpropanoid biosynthesis, which were affected upon S. botryosum infection. This research contributes to ongoing efforts toward a comprehensive understanding of the regulation and reprogramming of lentil metabolism under biotic stress, which will provide targets for potential applications in breeding for enhanced disease resistance.

Liquid chromatography-mass spectrometry metabolite library for metabolomics: Evaluating column suitability using a scoring approach

Untargeted metabolomic studies require an extensive set of analyte (metabolic) information to be obtained from each analyzed sample. Thus, highly selective, and efficient analytical methodologies together with reversed-phase (RP) or hydrophilic interaction liquid chromatography (HILIC) are usually applied in these approaches. Here, we present a performance comparison of five different chromatographic columns (C18, C8, RP Amide, zicHILIC, OH5 HILIC phases) to evaluate their sufficiency of analysis for a large analyte library, consisting of 817 authentic standards. By taking into account experimental chromatographic parameters (i.e. retention time, peak tailing and asymmetry, FWHM, signal-to-noise ratio and peak area and intensity), the proposed column scoring approach provides a simple criterion that may assist analysis in the select of a stationary phase for those metabolites of interest. RPLC methods offered better results regarding metabolic library coverage, while the zicHILIC stationary phase delivered a bigger number of properly eluted compounds. This study demonstrates the importance of choosing the most suitable configuration for the analysis of different metabolic classes.

Quantitative lipidomics reveals lipid perturbation in the liver of fatty liver hemorrhagic syndrome in laying hens

Fatty liver hemorrhagic syndrome (FLHS) is a metabolic disease that causes decreased egg production and even death in laying hens, which brings huge economic losses to the poultry industry. However, the pathogenesis of FLHS is unclear. The purpose of the present study was to identify the changes in lipid profile and the lipid species related to FLHS. In the present study, the FLHS disease model in Chinese commercial Jing Fen laying hens was induced by a high-energy low-protein diet. A lipidomics approach based on ultra-performance liquid chromatography-mass spectrometry coupled with multivariate statistical analysis was performed for the qualitative and quantitative analyses of the liver lipids. The results showed that a total of 29 lipid subclasses, including 1,302 lipid species, were detected and identified. Among them, the proportions of phosphatidylserine (Control/FLHS, 33.1% vs. 29.1%), phosphatidylethanolamine (22.7% vs. 15.5%), phosphatidylcholine (15.7% vs. 11.7%) and phosphatidylinositol (7% vs. 6%) were reduced, while triacylglycerol (7.1% vs. 18.3%) and diglyceride (3.9% vs. 11.7%) were increased. Between the Control and FLHS groups, distinct changes in lipid profile were observed in the score plots of principal component analysis and orthogonal partial least squares discriminant analysis. Twelve differential lipid species mainly involved in glycerophospholipid metabolism and linoleic acid metabolism were identified and considered to be related to the pathogenesis of FLHS. Fatty acid chain length and unsaturation were reduced, while the mRNA levels of elongation of very long chain fatty acids-2 (ELOVL2) were increased in the liver of laying hens with FLHS. Collectively, this study characterized the liver lipid profile and explored the changes in lipid species related to FLHS, which provided insights into the pathogenesis of FLHS from the view of lipid metabolism.

Review of Recent Modern Analytical Technology Combined with Chemometrics Approach Researches on Mushroom Discrimination and Evaluation

Mushroom is a macrofungus with precious fruiting body, as a food, a tonic, and a medicine, human have discovered and used mushrooms for thousands of years. Nowadays, mushroom is also a "super food" recommended by the World Health Organization (WHO) and Food and Agriculture Organization (FAO), and favored by consumers. Discrimination of mushroom including species, geographic origin, storage time, etc., is an important prerequisite to ensure their edible safety and commodity quality. Moreover, the effective evaluation of its chemical composition can help us better understand the nutritional properties of mushrooms. Modern analytical technologies such as chromatography, spectroscopy and mass spectrometry, etc., are widely used in the discrimination and evaluation researches of mushrooms, and chemometrics is an effective means of scientifically processing the multidimensional information hidden in these analytical technologies. This review will outline the latest applications of modern analytical technology combined with chemometrics in qualitative and quantitative analysis and quality control of mushrooms in recent years. Briefly describe the basic principles of these technologies, and the analytical processes of common chemometrics in mushroom researches will be summarized. Finally, the limitations and application prospects of chromatography, spectroscopy and mass spectrometry technology are discussed in mushroom quality control and evaluation.

A serum metabolomics study based on LC-MS: Chemosensitization effects of Rauvolfia vomitoria Afzel. combined with 5- fluorouracil on colorectal cancer mice

Colorectal cancer (CRC) is one of the malignant tumors with high incidence, and is mainly treated by chemotherapy at present. However, during CRC treatment, long-term use of traditional chemotherapeutic drugs will reduce the sensitivity of chemotherapy. Our previous studies have shown that Rauvolfia vomitoria total alkaloids (RVA) played an important role in 5-fluorouracil (5-FU) chemosensitization in CRC therapy, but its intervention mechanism has not been clarified completely in the metabolic level. Therefore, in this study, LC-MS based metabolomics was employed to explore the mechanism of 5-FU chemosensitization in CRC induced by the combination of RVA and conventional chemotherapeutic with 5-FU. The results showed that the final tumor weight of the high-dose combined group was significantly different from that of the 5-FU alone group. To evaluate the chemosensitization effects of RVA, serum samples collected from six groups (six mice in each group) with different administration methods were analyzed by HPLC-Q-Exactive Orbitrap/MS. After multivariate statistical analysis and metabolites identification, 25 different metabolites were identified between the 5-FU treatment group and combined high-dose treatment group, among which lipid and fatty acid metabolism pathways were mostly affected. These results suggest that RVA may sensitize traditional chemotherapeutic drug 5-FU and exert anti-tumor activity through influencing lipid metabolism and cell energy metabolism. Metabolomics provided a new insight into estimate of the therapeutic effect and dissection of the potential mechanisms of traditional Chinese medicine in treating colorectal cancer.

Protocol for correlation analysis of the murine gut microbiome and meta-metabolome using 16S rDNA sequencing and UPLC-MS

The gut microbiota and metabolites play pivotal roles in the pathobiology of various diseases. Here, we describe a protocol to profile the gut microbiome and meta-metabolome of a mouse disease model for acute graft-versus-host disease. We describe steps for fecal sample collection and processing for 16S sequencing and UPLC-MS. Finally, we detail the steps for data analysis and exhibit multi-omic associations to correlate with pathology.

For complete details on the use and execution of this protocol, please refer to Li et al. (2020).

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Fecal 16S rDNA gene sequencing and LC-MS decodes gut microenvironment during disease

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Screening and correlation analysis between microbiome and metabolome

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Joint analysis of multi-omics data and correlation with pathology

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Follicular Fluid: A Powerful Tool for the Understanding and Diagnosis of Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) represents one of the leading causes of anovulatory infertility and affects 5% to 20% of women worldwide. Until today, both the subsequent etiology and pathophysiology of PCOS remain unclear, and patients with PCOS that undergo assisted reproductive techniques (ART) might present a poor to exaggerated response, low oocyte quality, ovarian hyperstimulation syndrome, as well as changes in the follicular fluid metabolites pattern. These abnormalities originate a decrease of Metaphase II (MII) oocytes and decreased rates for fertilization, cleavage, implantation, blastocyst conversion, poor egg to follicle ratio, and increased miscarriages. Focus on obtaining high-quality embryos has been taken into more consideration over the years. Nowadays, the use of metabolomic analysis in the quantification of proteins and peptides in biological matrices might predict, with more accuracy, the success in assisted reproductive technology. In this article, we review the use of human follicular fluid as the matrix in metabolomic analysis for diagnostic and ART predictor of success for PCOS patients.

Targeted Metabolomics Using LC-MS in Neurospora crassa

The filamentous fungus Neurospora crassa has historically been a model for understanding the relationship between genes and metabolism-auxotrophic mutants of N. crassa were used by Beadle and Tatum to develop the one-gene-one-enzyme hypothesis for which they earned the Nobel Prize in 1958. In the ensuing decades, several techniques have been developed for the systematic analysis of metabolites in N. crassa and other fungi. Untargeted and targeted approaches have been used, with a focus on secondary metabolites over primary metabolism. Here, we describe a pipeline for sample preparation, metabolite extraction, Liquid Chromatography-Mass Spectrometry (LC-MS), and data analysis that can be used for targeted metabolomics of primary metabolites in N. crassa. Liquid cultures are grown with shaking in a defined minimal medium and then collected using filtration. Samples are lyophilized for 2 days at -80°C, pulverized, and mixed with a solution to extract polar metabolites. The metabolites are separated and identified using LC-MS, with downstream analysis using Skyline interpretive software. Relative levels of hundreds of metabolites can be detected and compared across strains. © 2022 Wiley Periodicals LLC. Basic Protocol: Metabolite extraction and detection from Neurospora crassa cell cultures using Liquid Chromatography-Mass Spectrometry.

FROM A NON-TARGETED METABOLOMICS APPROACH TO A TARGETED BIOMARKERS STRATEGY TO HIGHLIGHT TESTOSTERONE ABUSE IN EQUINE. ILLUSTRATION OF A METHODOLOGICAL TRANSFER BETWEEN PLATFORMS AND LABORATORIES

In order to overcome the challenge associated with the screening of Anabolic-Androgenic Steroids (AAS) abuses in animal competitions, a non-targeted LC-HRMS based metabolomics approach was implemented on equine urine samples to highlight potential biomarkers associated with the administration of such compounds, using testosterone esters as model steroids. A statistical model relying on 4 potential biomarkers intensity could be defined to predict the status of the samples. With a routine application perspective, the monitoring of the highlighted potential biomarkers was first transferred into high-throughput LC-SRM. The model's performances and robustness of the approach were preserved and providing a first demonstration of metabolomics-based biomarkers integration within a targeted workflow using common benchtop MS instrumentation. In addition, with a view to the widespread implementation of such biomarker-based tools, we have transferred the method to a second laboratory with similar instrumentation. This proof of concept allows the development and application of biomarker-based strategies to meet current doping control needs.

Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature

Metabolite identification remains a bottleneck and a still unregulated area in untargeted LC-MS metabolomics. The metabolomics research community and, in particular, the metabolomics standards initiative (MSI) proposed minimum reporting standards for metabolomics including those for reporting metabolite identification as long ago as 2007. Initially, four levels were proposed ranging from level 1 (unambiguously identified analyte) to level 4 (unidentified analyte). This scheme was expanded in 2014, by independent research groups, to give five levels of confidence. Both schemes provided guidance to the researcher and described the logical steps that had to be made to reach a confident reporting level. These guidelines have been presented and discussed extensively, becoming well-known to authors, editors, and reviewers for academic publications. Despite continuous promotion within the metabolomics community, the application of such guidelines is questionable. The scope of this meta-analysis was to systematically review the current LC-MS-based literature and effectively determine the proportion of papers following the proposed guidelines. Also, within the scope of this meta-analysis was the measurement of the actual identification levels reported in the literature, that is to find how many of the published papers really reached full metabolite identification (level 1) and how many papers did not reach this level.

Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: Meta-analysis of profiling metabolomics studies

AIM

Diabetic nephropathy (DN) is one of the worst complications of diabetes. Despite a growing number of DN metabolite profiling studies, most studies are suffering from inconsistency in their findings. The main goal of this meta-analysis was to reach to a consensus panel of significantly dysregulated metabolites as potential biomarkers in DN.

DATA SYNTHESIS

To identify the significant dysregulated metabolites, meta-analysis was performed by "vote-counting rank" and "robust rank aggregation" strategies. Bioinformatics analyses were performed to identify the most affected genes and pathways. Among 44 selected studies consisting of 98 metabolite profiles, 17 metabolites (9 up-regulated and 8 down-regulated metabolites), were identified as significant ones by both the meta-analysis strategies (p-value<0.05 and OR>2 or <0.5) and selected as DN metabolite meta-signature. Furthermore, enrichment analyses confirmed the involvement of various effective biological pathways in DN pathogenesis, such as urea cycle, TCA cycle, glycolysis, and amino acid metabolisms. Finally, by performing a meta-analysis over existing time-course studies in DN, the results indicated that lactic acid, hippuric acid, allantoin (in urine), and glutamine (in blood), are the topmost non-invasive early diagnostic biomarkers.

CONCLUSION

The identified metabolites are potentially involved in diabetic nephropathy pathogenesis and could be considered as biomarkers or drug targets in the disease.

PROSPERO REGISTRATION NUMBER

CRD42020197697.

Discovery of donor age markers from bloodstain by LC-MS/MS using a metabolic approach

Bloodstains are frequently encountered at crime scenes and they provide important evidence about the incident, such as information about the victim or suspect and the time of death or other events. Efforts have been made to identify the age of the bloodstain's donor through genomic approaches, but there are some limitations, such as the availability of databases and the quality dependence of DNA. There is a need for the development of a tool that can obtain information at once from a small blood sample. The aim of this study is to identify bloodstain metabolite candidates that can be used to determine donor age. We prepared bloodstain samples and analyzed metabolites using high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Eighteen molecular features (MFs) were selected as candidates using volcano plots and multivariate analysis. Based on the MS/MS spectrum of the MFs, the following nine metabolites were identified from the METaboliteLINk database: Δ2-cis eicosenoic acid, ergothioneine, adenosine 5'-monophosphate, benzaldehyde, phenacylamine, myristic acid ethyl ester, p-coumaric acid, niacinamide, and N-arachidonoyl-L-alanine. These nine age markers at high or low abundances could be used to estimate the age of a bloodstain's donor. This study was the first to develop metabolite age markers that can be used to analyze crime scene bloodstains.

Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research

Ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS) variants currently represent the best tools to tackle the challenges of complexity and lack of comprehensive coverage of the metabolome. UHPLC offers flexible and efficient separation coupled with high-sensitivity detection via HRMS, allowing for the detection and identification of a broad range of metabolites. Here we discuss current common strategies for UHPLC-HRMS-based metabolomics, with a focus on expanding metabolome coverage.

Metabolomics Evaluation of Patients With Stage 5 Chronic Kidney Disease Before Dialysis, Maintenance Hemodialysis, and Peritoneal Dialysis

Objective

Current treatment options for patients with stage 5 chronic kidney disease before dialysis (predialysis CKD-5) are determined by individual circumstances, economic factors, and the doctor’s advice. This study aimed to explore the plasma metabolic traits of patients with predialysis CKD-5 compared with maintenance hemodialysis (HD) and peritoneal dialysis (PD) patients, to learn more about the impact of the dialysis process on the blood environment.

Methods

Our study enrolled 31 predialysis CKD-5 patients, 31 HD patients, and 30 PD patients. Metabolite profiling was performed using a targeted metabolomics platform by applying an ultra-high-performance liquid chromatography-tandem mass spectrometry method, and the subsequent comparisons among all three groups were made to explore metabolic alterations.

Results

Cysteine metabolism was significantly altered between predialysis CKD-5 patients and both groups of dialysis patients. A disturbance in purine metabolism was the most extensively changed pathway identified between the HD and PD groups. A total of 20 discriminating metabolites with large fluctuations in plasma concentrations were screened from the group comparisons, including 2-keto-D-gluconic acid, kynurenic acid, s-adenosylhomocysteine, L-glutamine, adenosine, and nicotinamide.

Conclusion

Our study provided a comprehensive metabolomics evaluation among predialysis CKD-5, HD, and PD patients, which described the disturbance of metabolic pathways, discriminating metabolites and their possible biological significances. The identification of specific metabolites related to dialysis therapy might provide insights for the management of advanced CKD stages and inform shared decision-making.

FoodOmicsGR_RI. A Consortium for Comprehensive Molecular Characterisation of Food Products

The national infrastructure FoodOmicsGR_RI coordinates research efforts from eight Greek Universities and Research Centers in a network aiming to support research and development (R&D) in the agri-food sector. The goals of FoodOmicsGR_RI are the comprehensive in-depth characterization of foods using cutting-edge omics technologies and the support of dietary/nutrition studies. The network combines strong omics expertise with expert field/application scientists (food/nutrition sciences, plant protection/plant growth, animal husbandry, apiculture and 10 other fields). Human resources involve more than 60 staff scientists and more than 30 recruits. State-of-the-art technologies and instrumentation is available for the comprehensive mapping of the food composition and available genetic resources, the assessment of the distinct value of foods, and the effect of nutritional intervention on the metabolic profile of biological samples of consumers and animal models. The consortium has the know-how and expertise that covers the breadth of the Greek agri-food sector. Metabolomics teams have developed and implemented a variety of methods for profiling and quantitative analysis. The implementation plan includes the following research axes: development of a detailed database of Greek food constituents; exploitation of "omics" technologies to assess domestic agricultural biodiversity aiding authenticity-traceability control/certification of geographical/genetic origin; highlighting unique characteristics of Greek products with an emphasis on quality, sustainability and food safety; assessment of diet's effect on health and well-being; creating added value from agri-food waste. FoodOmicsGR_RI develops new tools to evaluate the nutritional value of Greek foods, study the role of traditional foods and Greek functional foods in the prevention of chronic diseases and support health claims of Greek traditional products. FoodOmicsGR_RI provides access to state-of-the-art facilities, unique, well-characterised sample sets, obtained from precision/experimental farming/breeding (milk, honey, meat, olive oil and so forth) along with more than 20 complementary scientific disciplines. FoodOmicsGR_RI is open for collaboration with national and international stakeholders.

Time-dependent metabolomics study of cerebral ischemia-reperfusion and its treatment: focus on the combination of traditional Chinese medicine and Western medicine

Cerebral ischemia is a common cerebrovascular disease with high mortality, and thrombolysis can cause more severe reperfusion injury. In clinical practice, Ginkgo biloba dispersible tablets combined with nimodipine have been widely used to reduce cerebral ischemia-reperfusion injury, but the mechanism has not been clearly elucidated. To explore this relationship, the change in metabolism between a sham operation group, a model group and an administration group was analyzed for the period after cerebral ischemia. Biochemical assays were used to assess injury extent and the therapeutic effects of different dosing regimens. A metabolomics method based on ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was developed to screen biomarkers in plasma of rats and analyze abnormal metabolic pathways. Using statistical analysis, corticosterone, glutamine, oleic acid, isoleucine, phenylalanine and sphingomyelin (d18:1/16:0) were screened as diagnostic biomarkers. The metabolic pathways perturbed by cerebral ischemia-reperfusion involved phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, alpha-linolenic acid metabolism, retinol metabolism, alanine, aspartate and glutamate metabolism, and glycerophospholipid metabolism. Analysis of the adjustment of biomarkers at different time points showed that the best time to evaluate the efficacy of combined administration is about 6 h after administration. Both pathological characteristics and metabolomics confirmed the better effect of the combined group than the individual groups. In this study, a non-targeted metabolomics method was developed to explore the mechanism of action of the combination of traditional Chinese and Western medicine in cerebral ischemia-reperfusion treatment, providing a theoretical basis for disease prognosis and treatment options. Graphical abstract.

Circadian and Sleep Metabolomics Across Species

Under normal circadian function, metabolic control is temporally coordinated across tissues and behaviors with a 24-h period. However, circadian disruption results in negative consequences for metabolic homeostasis including energy or redox imbalances. Yet, circadian disruption has become increasingly prevalent within today's society due to many factors including sleep loss. Metabolic consequences of both have been revealed by metabolomics analyses of circadian biology and sleep. Specifically, two primary analytical platforms, mass spectrometry and nuclear magnetic resonance spectroscopy, have been used to study molecular clock and sleep influences on overall metabolic rhythmicity. For example, human studies have demonstrated the prevalence of metabolic rhythms in human biology, as well as pan-metabolome consequences of sleep disruption. However, human studies are limited to peripheral metabolic readouts primarily through minimally invasive procedures. For further tissue- and organism-specific investigations, a number of model systems have been studied, based upon the conserved nature of both the molecular clock and sleep across species. Here we summarize human studies as well as key findings from metabolomics studies using mice, Drosophila, and zebrafish. While informative, a limitation in existing literature is a lack of interpretation regarding dynamic synthesis or catabolism within metabolite pools. To this extent, future work incorporating isotope tracers, specific metabolite reporters, and single-cell metabolomics may provide a means of exploring dynamic activity in pathways of interest.

The Nanomaterial Metabolite Corona Determined Using a Quantitative Metabolomics Approach: A Pilot Study

Nanomaterials (NMs) are promptly coated with biomolecules in biological systems leading to the formation of the so-called corona. To date, research has predominantly focused on the protein corona and how it affects NM uptake, distribution, and bioactivity by conferring a biological identity to NMs enabling interactions with receptors to mediate cellular responses. Thus, protein corona studies are now integral to nanosafety assessment. However, a larger class of molecules, the metabolites, which are orders of magnitude smaller than proteins (<1000 Da) and regulate metabolic pathways, has been largely overlooked. This hampers the understanding of the bio-nano interface, development of computational predictions of corona formation, and investigations into uptake or toxicity at the cellular level, including identification of molecular initiating events triggering adverse outcome pathways. Here, a capillary electrophoresis-mass spectrometry based metabolomics approach reveals that pure polar ionogenic metabolite standards differentially adsorb to a range of 6 NMs (SiO₂ , 3 TiO₂ with different surface chemistries, and naïve and carboxylated polystyrene NMs). The metabolite corona composition is quantitatively compared using protein-free and complete plasma samples, revealing that proteins in samples significantly change the composition of the metabolite corona. This key finding provides the basis to include the metabolite corona in future nanosafety endeavors.

Identification of novel biomarkers of hepatocellular carcinoma by high-definition mass spectrometry: Ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry and desorption electrospray ionization mass spectrometry imaging

RATIONALE

Hepatocellular carcinoma (HCC) is a highly malignant disease for which the development of prospective or prognostic biomarkers is urgently required. Although metabolomics is widely used for biomarker discovery, there are some bottlenecks regarding the comprehensiveness of detected features, reproducibility of methods, and identification of metabolites. In addition, information on localization of metabolites in tumor tissue is needed for functional analysis. Here, we developed a wide-polarity global metabolomics (G-Met) method, identified HCC biomarkers in human liver samples by high-definition mass spectrometry (HDMS), and demonstrated localization in cryosections using desorption electrospray ionization MS imaging (DESI-MSI) analysis.

METHODS

Metabolic profiling of tumor (n = 38) and nontumor (n = 72) regions in human livers of HCC was performed by an ultrahigh-performance liquid chromatography quadrupole time-of-flight MS (UHPLC/QTOFMS) instrument equipped with a mixed-mode column. The HCC biomarker candidates were extracted by multivariate analyses and identified by matching values of the collision cross section and their fragment ions on the mass spectra obtained by HDMS. Cryosections of HCC livers, which included both tumor and nontumor regions, were analyzed by DESI-MSI.

RESULTS

From the multivariate analysis, m/z 904.83 and m/z 874.79 were significantly high and low, respectively, in tumor samples and were identified as triglyceride (TG) 16:0/18:1(9Z)/20:1(11Z) and TG 16:0/18:1(9Z)/18:2(9Z,12Z) using the synthetic compounds. The TGs were clearly localized in the tumor or nontumor areas of the cryosection.

CONCLUSIONS

Novel biomarkers for HCC were identified by a comprehensive and reproducible G-Met method with HDMS using a mixed-mode column. The combination analysis of UHPLC/QTOFMS and DESI-MSI revealed that the different molecular species of TGs were associated with tumor distribution and were useful for characterizing the progression of tumor cells and discovering prospective biomarkers.

Non-targeted Screening in Environmental Monitoring Programs

Contaminant monitoring programs have been tasked with understanding the fate and transport of toxic chemicals in the environment. Mass spectrometry based methods have traditionally been developed to maximize sensitivity and accuracy of a select set of target compounds. As mass spectrometry methods have advanced, so has the breadth of questions proposed by environmental chemists. Incorporating these methods in chemical monitoring programs provides large data sets to explore the effects of complex mixtures on environmental systems.

Engineering a Model Cell for Rational Tuning of GPCR Signaling

G protein-coupled receptor (GPCR) signaling is the primary method eukaryotes use to respond to specific cues in their environment. However, the relationship between stimulus and response for each GPCR is difficult to predict due to diversity in natural signal transduction architecture and expression. Using genome engineering in yeast, we constructed an insulated, modular GPCR signal transduction system to study how the response to stimuli can be predictably tuned using synthetic tools. We delineated the contributions of a minimal set of key components via computational and experimental refactoring, identifying simple design principles for rationally tuning the dose response. Using five different GPCRs, we demonstrate how this enables cells and consortia to be engineered to respond to desired concentrations of peptides, metabolites, and hormones relevant to human health. This work enables rational tuning of cell sensing while providing a framework to guide reprogramming of GPCR-based signaling in other systems.

Validated comprehensive metabolomics and lipidomics analysis of colon tissue and cell lines

Current untargeted approaches for metabolic fingerprinting of colon tissue and cell lines lack validation of reproducibility and/or focus on a selection of metabolites as opposed to the entire metabolome. Yet, both are critical to ensure reliable results and pursue a fully holistic analysis. Therefore, we have optimized and validated a platform for analyzing the polar metabolome and lipidome of colon-derived cell and tissue samples based on a consecutive extraction of polar and apolar components. Peak areas of selected targeted analytes and the number of untargeted components were assessed. Analysis was performed using ultra-high performance liquid-chromatography (UHPLC) coupled to hybrid quadrupole-Orbitrap high-resolution mass spectrometry (HRMS). This resulted in an optimized extraction protocol using 50% methanol/ultrapure water to obtain the polar fraction followed by a dichloromethane-based lipid extraction. Using this comprehensive approach, we have detected more than 15,000 components with CV < 30% in internal quality control (IQC) samples and were able to discriminate the non-transformed (NT) and transformed (T) state in human colon tissue and cell lines based on validated OPLS-DA models (R²Y > 0.719 and Q² > 0.674). To conclude, our validated polar metabolomics and lipidomics fingerprinting approach could be of great value to reveal gastrointestinal disease-associated biomarkers and mechanisms.

Metabolomics approaches for the discrimination of disease suppressive soils for Rhizoctonia solani AG8 in cereal crops using 1H NMR and LC-MS

The suppression of soilborne crop pathogens such as Rhizoctonia solani AG8 may offer a sustainable and enduring method for disease control, though soils with these properties are difficult to identify. In this study, we analysed the soil metabolic profiles of suppressive and non-suppressive soils over 2 years of cereal production. We collected bulk and rhizosphere soil at different cropping stages and subjected soil extracts to liquid chromatography-mass spectrometry (LC-MS) and proton nuclear magnetic resonance spectroscopy (¹H NMR) analyses. Community analyses of suppressive and non-suppressive soils using principal component analyses and predictive modelling of LC-MS and NMR datasets respectively, revealed distinct biochemical profiles for the two soil types with clustering based on suppressiveness and cropping stage. NMR spectra revealed the suppressive soils to be more abundant in sugar molecules than non-suppressive soils, which were more abundant in lipids and terpenes. LC-MS features that were significantly more abundant in the suppressive soil were identified and assessed as potential biomarkers for disease suppression. The structures of a potential class of LC-MS biomarkers were elucidated using accurate mass data and MS fragmentation spectrum information. The most abundant compound found in association with suppressive soils was confirmed to be a macrocarpal, which is an antimicrobial secondary metabolite. Our study has demonstrated the utility of environmental metabolomics for the study of disease suppressive soils, resulting in the discovery of a macrocarpal biomarker for R. solani AG8 suppressive soil which can be further studied functionally in association with suppression pot trials and microbial isolation studies.

Wine and grape marc spirits metabolomics

INTRODUCTION

Mass spectrometry (MS)-based and nuclear magnetic resonance (NMR) spectroscopic analyses play a key role in the field of metabolomics due to their important advantages. The use of metabolomics in wine and grape marc spirits allows a more holistic perspective in monitoring and gaining information on the making processes and thus it can assist on the improvement of their quality.

OBJECTIVES

This review surveys the latest metabolomics approaches for wine and grape marc spirits with a focus on the description of MS-based and NMR spectroscopic analytical techniques.

METHODS

We reviewed the literature to identify metabolomic studies of wine and grape marc spirits that were published until the end of 2017, with the key term combinations of 'metabolomics', 'wine' and 'grape marc spirits'. Through the reference lists from these studies, additional articles were identified.

RESULTS

The results of this review showed that the application of different metabolomics approaches has significantly increased the knowledge of wine metabolome and grape marc spirits; however there is not yet a single analytical platform that can completely separate, detect and identify all metabolites in one analysis.

CONCLUSIONS

The authentication and quality control of wines and grape marc spirits has to be taken with caution, since the product's chemical composition could be affected by many factors. Despite intrinsic limitations, NMR spectroscopy and MS based strategies remain the key analytical methods in metabolomics studies. Authenticity, traceability and health issues related to their consumption are the major research initiatives in wine and grape marc spirits metabolomics analysis.

Human White Adipose Tissue Metabolome: Current Perspective

OBJECTIVE

Interest in metabolites produced by adipose tissue has increased substantially in the past several decades. Previously regarded as an inert energy storage depot, adipose tissue is now viewed as a complex metabolically active organ with considerable impact on human health. The emerging field of mass spectrometry-based metabolomics presents a powerful tool for the study of processes in complex biological matrices including adipose tissue.

RESULTS

A large number of structurally distinct metabolites can be analyzed to facilitate the investigation of differences between physiological and pathophysiological metabolic profiles associated with adipose tissue. Understanding the molecular basis of adipose tissue regulation can thereby provide insight into the monitoring of obesity-related metabolic disorders and lead to the development of novel diagnostic and prognostic biomarkers.

CONCLUSIONS

This review provides the current state of knowledge, recent progress, and critical evaluation of metabolomics approaches in the context of white adipose tissue and obesity. An overview of basic principles and resources describing individual groups of metabolites analyzed in white adipose tissue and biological fluids is given. The focus is on metabolites that can serve as reliable biomarkers indicative of metabolic alterations associated with obesity.

Trace Phosphate Improves ZIC-pHILIC Peak Shape, Sensitivity, and Coverage for Untargeted Metabolomics

Existing hydrophilic interaction liquid chromatography (HILIC) methods, considered individually, each exhibit poor chromatographic performance for a substantial fraction of polar metabolites. In addition to limiting metabolome coverage, such deficiencies also complicate automated data processing. Here we show that some of these analytical challenges can be addressed for the ZIC-pHILIC, a zwitterionic stationary phase commonly used in metabolomics, with the addition of trace levels of phosphate. Specifically, micromolar phosphate extended metabolome coverage by hundreds of credentialed features, improved peak shapes, and reduced peak-detection errors during informatic processing. Although the addition of high levels of phosphate (millimolar) as a HILIC mobile phase buffer has been explored previously, such concentrations interfere with mass spectrometric (MS) detection. We show that using phosphate as a trace additive at micromolar concentrations improves analysis by electrospray MS, increasing signal for a diverse set of polar standards. Given the small amount of phosphate needed, comparable chromatographic improvements were also achieved by direct addition of phosphate to the sample during reconstitution. Our results suggest that defects in ZIC-pHILIC performance are predominantly driven by electrostatic interactions, which can be modulated by phosphate. These findings constitute both a methodological improvement for untargeted metabolomics and an advance in our understanding of the mechanisms limiting HILIC coverage.

Comparative assessment of Graves' disease and main extrathyroidal manifestation, Graves' ophthalmopathy, by non-targeted metabolite profiling of blood and orbital tissue

Graves' disease (GD) is an autoimmune disorder that causes the overproduction of thyroid hormones and consequent cascade of systemic metabolism dysfunction. Moreover, Graves' ophthalmopathy (GO) is the main extrathyroidal manifestation of Graves' disease (GD). The goal of the study was to identify metabolic signatures in association with diagnostic biomarkers of GD without GO and GO, respectively. Ninety metabolites were profiled and analyzed based on a non-targeted primary metabolite profiling from plasma samples of 21 GD patients without GO, 26 subjects with GO, and 32 healthy subjects. Multivariate statistics showed a clear discrimination between healthy controls and disease group (R2Y = 0.518, Q2 = 0.478) and suggested a biomarker panel consisting of 10 metabolites. Among them, most of metabolites showed the positive association with the levels of thyrotropin receptor antibodies. With combination of proline and 1,5-anhydroglucitol, which were identified as GO-specific modulators, the re-constructed biomarker model greatly improved the statistical power and also facilitated simultaneous discrimination among healthy control, GO, and GD without GO groups (AUC = 0.845-0.935). Finally, the comparative analysis of tissue metabolite profiles from GO patients proposed putative metabolic linkage between orbital adipose/connective tissues and the biofluidic consequences, in which fumarate, proline, phenylalanine, and glycerol were coordinately altered with the blood metabolites.

Mass spectrometry in untargeted liquid chromatography/mass spectrometry metabolomics: Electrospray ionisation parameters and global coverage of the metabolome

RATIONALE

Liquid chromatography coupled to mass spectrometry (LC/MS) is a dominant analytical platform in metabolomics, because of the high sensitivity and resolution, thus enabling large-scale coverage of metabolomes. Correspondingly, electrospray ionisation (ESI) is the favoured ionisation method in untargeted LC/MS metabolomics given the ability to produce large numbers of ions. In the workflow of LC/ESI-MS metabolomics, maximising the ionisation efficiency over a wide mass range is inevitably an essential and determining step, subsequently defining the extent of coverage of the metabolome under investigation. Thus in this study, electronic factors related to the functioning of the ESI source, namely the capillary and sample cone voltages, were explored to investigate the influence on the data acquired in metabolomic investigations.

METHODS

Hydromethanolic samples from an untargeted study (sorghum plants responding dynamically to fungal infection) were analysed on a high-resolution/definition LC/ESI-MS system. Here the capillary and sample cone voltages of the ZSpray™ ESI source were varied between 1.5-3.0 kV and 10.0-40.0 V, respectively. The acquired data were processed with MarkerLynx™ software and analysed using central composite design response surface methodology and chemometric approaches (principal component analysis and orthogonal projection latent structures-discriminant analysis).

RESULTS

The results evidently demonstrate that both capillary and sampling cone voltages not only significantly influence the recorded MS signals with regard to the number and abundance of features, but also the overall structure of the collected data. This consequently impacts on the information extracted from the data and thus affects coverage of the metabolome.

CONCLUSIONS

The observations postulate in that, untargeted LC/MS metabolomics, 'what you see is what you ionise'. Although there is convergence of collected data under different ESI conditions, the nuances observed indicate that the exploration of different ion source settings could be the best trade-off in expanding and maximising the metabolome coverage in untargeted metabolomic experiments.

The human microbiome and metabolomics: Current concepts and applications

The mammalian gastrointestinal tract has co-developed with a large number of microbes in a symbiotic relationship over millions of years. Recent studies indicate that indigenous bacteria are intimate with the intestine and play essential roles in health and disease. In the quest to maintain a stable niche, these prokaryotes influence multiple host metabolic pathways, resulting from an interactive host-microbiota metabolic signaling and impacting strongly on the metabolic phenotypes of the host. Since dysbiosis of the gut bacteria result in alteration in the levels of certain microbial and host co-metabolites, identifying these markers could enhance early detection of diseases. Also, identification of these metabolic fingerprints could give us clues as to how to manipulate the microbiome to promote health or treat diseases. This review provides an overview of our current knowledge of the microbiome and metablomics, applications and the future perspectives.