Utility of separation science in metabolomic studies

Metabolome and transcriptome reprogramming underlying tomato drought resistance triggered by a Pseudomonas strain

Although amelioration of drought stress by Plant Growth Promoting Rhizobacteria (PGPR) is a well-documented phenomenon, the combined molecular and metabolic mechanisms governing this process remain unclear. In these lines, the present study aimed to provide new insights in the underlying drought attenuating mechanisms of tomato plants inoculated with a PGP Pseudomonas putida strain, by using a combination of metabolomic and transcriptomic approaches. Following Differentially Expressed Gene analysis, it became evident that inoculation resulted in a less disturbed plant transcriptome upon drought stress. Untargeted metabolomics highlighted the differential metabolite accumulation upon inoculation, as well as the less metabolic reprograming and the lower accumulation of stress-related metabolites for inoculated stressed plants. These findings were in line with morpho-physiological evidence of drought stress mitigation in the inoculated plants. The redox state modulation, the more efficient nitrogen assimilation, as well as the differential changes in amino acid metabolism, and the induction of the phenylpropanoid biosynthesis pathway, were the main drought-attenuating mechanisms in the SAESo11-inoculated plants. Shifts in pathways related to hormonal signaling were also evident upon inoculation at a transcript level and in conjunction with carbon metabolism regulation, possibly contributed to a drought-attenuation preconditioning. The identified signatory molecules of SAESo11-mediated priming against drought included aspartate, myo-inositol, glutamate, along with key genes related to trehalose, tryptophan and cysteine synthesis. Taken together, SAESo11-inoculation provides systemic effects encompassing both metabolic and regulatory functions, supporting both seedling growth and drought stress amelioration.

Investigation of dichlorodiphenyltrichloroethane (DDT) on xenobiotic enzyme disruption and metabolomic bile acid biosynthesis in DDT-sprayed areas using wild rats

Dichlorodiphenyltrichloroethane (DDT) is an organochlorine insecticide used worldwide. Several studies have reported the toxic effects of DDT and its metabolites on steroid hormone biosynthesis; however, its environmental effects are not well understood. This study examined wild rats collected in DDT-sprayed areas of South Africa and quantified plasma metabolites using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). Fold change analysis of the metabolome revealed the effect of DDT on bile acid biosynthesis. Gene expression of the related enzyme in rat liver samples was also quantified. Significant association was found between DDT and gene expression levels related to constitutive androstane receptor mediated enzymes, such as Cyp2b1 in rat livers. However, our results could not fully demonstrate that enzymes related to bile acid biosynthesis were strongly affected by DDT. The correlation between DDT concentration and gene expression involved in steroid hormone synthesis in testis was also evaluated; however, no significant correlation was found. The disturbance of metabolic enzymes occurred in rat liver in the target area. Our results suggest that DDT exposure affects gene expression in wild rats living in DDT-sprayed areas. Therefore, there is a need for DDT toxicity evaluation in mammals living in DDT-sprayed areas. We could not find an effective biomarker that could reflect the mechanism of DDT exposure; however, this approach can provide new insights for future research to evaluate DDT effects in sprayed areas.

Systematic characterization of components of Makyo-kanseki-to granule and serum metabolomics for exploring its protective mechanism against acute lung injury in lipopolysaccharide-induced rats

Makyo-kanseki-to has been used for the treatment of pneumonia, becoming a basic formula for coronavirus disease 2019. However, the chemical profile of Makyo-kanseki-to granule and its possible mechanism against acute lung injury from terminal metabolic regulation have been unclear. The aim of this study was to characterize the constituents in Makyo-kanseki-to granule and reveal the potential related mechanism of Makyo-kanseki-to granule treatment for acute lung injury using a rat model of lipopolysaccharide-induced acute lung injury. Totally, 78 constituents were characterized based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Makyo-kanseki-to granule could alleviate acute lung injury through modulating rectal temperature, pulmonary edema, histopathology, and processes of inflammatory and oxidative stress. Twenty-two potential biomarkers in acute lung injury rats were identified by metabolomics based on ultra-performance liquid chromatography coupled with quadrupole exactive high-field mass spectrometry. They were mainly involved in amino acids and glycerophospholipid metabolism, which were regulated by Makyo-kanseki-to granule. The present results not only increase the understanding of the chemical profile and molecular mechanism of Makyo-kanseki-to granule mediated protection against acute lung injury but also provide an experimental basis and new ideas for further development and clinical application of Makyo-kanseki-to granule.

Identification of Plasma Fatty Acids in Four Lipid Classes to Understand Energy Metabolism at Different Levels of Ketonemia in Dairy Cows Using Thin Layer Chromatography and Gas Chromatographic Techniques (TLC-GC)

Excessive mobilization of adipose tissue in high milk producing dairy cows predisposes to metabolic diseases. The aim of this research was to identify the plasma fatty acids in four lipid classes as biomarkers for the diagnosis of hyperketonemia in bovines using thin layer chromatography and gas chromatographic techniques (TLC-GC). Sixty multiparous Holstein-Friesian dairy cows were enrolled in the study. Blood samples from the coccygeal vein were collected and β-hydroxybutyrate (BHB) was evaluated. All animals were divided into three groups on the basis of ketonemia: BHB < 0.50 mmol/L, 0.50 < BHB < 1.0 mmol/L, and BHB > 1.0 mmol/L. Plasma fatty acid concentrations were evaluated in four lipid classes: Free Fatty Acids (FFA), Triglycerides (TG), Cholesterol Esters (CE) And Phospholipids (PL). The concentration of fatty acids was analyzed using TLC-GC. The results showed the following significance in the lipid classes: 19 fatty acids were significant (p < 0.053) in FFA, nine fatty acids were significant (p < 0.050) in TG, eight fatty acids were significant (p < 0.050) in CE and three fatty acids were significant (p < 0.049) in PL. Eleven parameters were considered as predictive fatty acids related to animals in hyperketonemia. The FFA increased simultaneously with blood BHB levels, although the identified predictive fatty acids related to the TG and CE lipid classes decreased, meanwhile the BHB values increased. In the PL lipid class, no fatty acids were predictive.

Untargeted metabolomic profiling of accessory sex gland fluid from Morada Nova rams

The present study was conducted to characterize the metabolome of accessory gland fluid (AGF) of locally adapted Morada Nova rams, raised in the Brazilian Northeast. AGF was collected by an artificial vagina from five vasectomized rams. Metabolites were identified by gas chromatography-mass spectrometry (GC/MS) and high-performance liquid chromatography-mass spectrometry (LC/MS), with the support of Human Metabolome Database, PubChem, LIPID Metabolites, Pathways Strategy databases, and MetaboAnalyst platforms. There were 182 and 190 metabolites detected by GC/MS and LC/MS, respectively, with an overlap of one molecule. Lipids and lipid-like molecules were the most abundant class of metabolites in the ram AGF (127 compounds), followed by amino acids, peptides, and analogs(103 metabolites). Considering all GC/MS and LC/MS, fructose, glycerol, citric acid, d-mannitol, d-glucose, and l-(+)-lactic acid were the most abundant single metabolites present in the ram AGF. Meaningful pathways associated with AGF metabolites included glycine, serine and threonine metabolism; pantothenate and CoA biosynthesis; galactose metabolism; glutamate metabolism and phenylalanine metabolism, and so forth. In conclusion, the combined use of LC/MS and GC/MS was essential for getting a holistic view of the compounds embedded in the ram AGF. Chemical analysis of the accessory sex gland secretion is relevant for understanding sperm function and fertilization.

Biomarker for Ischemic Stroke Using Metabolome: A Clinician Perspective

Finding ischemic stroke biomarker is highly desirable because it can improve diagnosis even before a patient arrives to the hospital. Metabolome is one of new technologies that help to find biomarkers. Most metabolome-related ischemic stroke studies were done in Asia and had exploratory designs. Although failed to find specific biomarkers, they discovered several important metabolite-stroke associations which belong to three pathophysiological mechanisms: Excitotoxicity with activation of glutamate, resulting in the increase of glutamate derivatives proline and pyroglutamate; Oxidative stress with production of free radicals and perturbed concentrations of uric acid, matrix metalloproteinase-9, branch-chained amino acids, sphingolipids, homocysteine, asymmetric dimethylarginine, nitric oxide and folate cycle metabolites; and Stroke mediated inflammation, affecting phospholipid metabolism with perturbed levels of lysophosphatidylethanolamine and lysophosphatidylcholine. The discovered metabolite-stroke associations need further evaluation in prospective, high-quality studies with patients matched for age, risk factors, and medications.

Lessons from Metabonomics on the Neurobiology of Stroke

The application of metabonomic science to interrogate stroke permits the study of metabolite entities, small enough to cross the blood-brain barrier, that provide insight into neuronal dysfunction, and may serve as reservoirs of biomarker discovery. This systematic review examines the applicability of metabolic profiling in ischemic stroke research. Six human studies utilizing metabolic profiling to analyze biofluids from ischemic stroke patients have been included, employing ¹H-NMR and/or mass spectrometry to analyze plasma, serum, and/or urine in a targeted or untargeted fashion. Three are diagnostic studies, and one investigates prognostic biomarkers of stroke recurrence following transient ischemic attack. Two studies focus on metabolic distinguishers of depression or cognitive impairment following stroke. Identified biomarkers from blood and urine predominantly relate to homocysteine and folate, branched chain amino acid, and lipid metabolism. Statistical models are well fitted and reproducible, with excellent validation outcomes, demonstrating the feasibility of metabolic profiling to study a complex disorder with multicausal pathology, such as stroke.

Rapid and High-Throughput Detection and Quantitation of Radiation Biomarkers in Human and Nonhuman Primates by Differential Mobility Spectrometry-Mass Spectrometry

Radiation exposure is an important public health issue due to a range of accidental and intentional threats. Prompt and effective large-scale screening and appropriate use of medical countermeasures (MCM) to mitigate radiation injury requires rapid methods for determining the radiation dose. In a number of studies, metabolomics has identified small-molecule biomarkers responding to the radiation dose. Differential mobility spectrometry-mass spectrometry (DMS-MS) has been used for similar compounds for high-throughput small-molecule detection and quantitation. In this study, we show that DMS-MS can detect and quantify two radiation biomarkers, trimethyl-L-lysine (TML) and hypoxanthine. Hypoxanthine is a human and nonhuman primate (NHP) radiation biomarker and metabolic intermediate, whereas TML is a radiation biomarker in humans but not in NHP, which is involved in carnitine synthesis. They have been analyzed by DMS-MS from urine samples after a simple strong cation exchange-solid phase extraction (SCX-SPE). The dramatic suppression of background and chemical noise provided by DMS-MS results in an approximately 10-fold reduction in time, including sample pretreatment time, compared with liquid chromatography-mass spectrometry (LC-MS). DMS-MS quantitation accuracy has been verified by validation testing for each biomarker. Human samples are not yet available, but for hypoxanthine, selected NHP urine samples (pre- and 7-d-post 10 Gy exposure) were analyzed, resulting in a mean change in concentration essentially identical to that obtained by LC-MS (fold-change 2.76 versus 2.59). These results confirm the potential of DMS-MS for field or clinical first-level rapid screening for radiation exposure. Graphical Abstract ᅟ.

Environmental Metabolic Footprinting: A novel application to study the impact of a natural and a synthetic β-triketone herbicide in soil

This study presents a novel approach for assessing the risk of agrochemicals in soil microcosms through the use of non-targeted metabolomics. The metabolome of treated soils was extracted and tested through LCMS profiling in order to generate an "Environmental Metabolic Footprint" (EMF). A dynamic characterization of pollution biomarkers was obtained through a multivariate statistical analysis of EMF data, where our results show the possible evolution towards a state of resilience. The EMF methodology was applied to two β-triketone herbicides in soil microcosms: one natural, leptospermone, and one synthetic, sulcotrione. In spite of a four-fold higher application dose, leptospermone exhibited a lower resilience time than did sulcotrione (ca. 30 days vs ca. 45 days respectively).

NMR- and LC-MS/MS-based urine metabolomic investigation of the subacute effects of hexabromocyclododecane in mice

In the present study, both untargeted and targeted metabolomics approaches were used to evaluate the subacute effects of hexabromocyclododecane (HBCD) on mice urine metabolome. Untargeted metabolomics based on (1)H NMR showed that HBCD exposure disturbed mice metabolism in both dosed groups, especially in high dosed group. The low-dose HBCD led to a decrease in alanine, malonic acid, and trimethylamine (TMA). High-dose HBCD-treated mice developed high levels of citric acid and 2-ketoglutarate, together with decreased alanine, acetate, formate, TMA, 3-hydroxybutyrate, and malonic acid. Targeted metabolomics for metabolic profiling of 20 amino acids identified alanine, lysine, and phenylalanine as significantly disturbed metabolites. These results indicated that subchronic exposure to HBCD caused a disturbance of mice metabolism, especially in TCA cycle, lipid metabolism, gut microbial metabolism, and homeostasis of amino acids, and the application of untargeted and targeted metabolomics combined with conventional toxicology approaches to evaluate the subacute effects of pollutants will provide more comprehensive information and aid in predicting health risk of these pollutants.

Metabolomic studies of human gastric cancer: Review

Metabolomics is a field of study in systems biology that involves the identification and quantification of metabolites present in a biological system. Analyzing metabolic differences between unperturbed and perturbed networks, such as cancerous and non-cancerous samples, can provide insight into underlying disease pathology, disease prognosis and diagnosis. Despite the large number of review articles concerning metabolomics and its application in cancer research, biomarker and drug discovery, these reviews do not focus on a specific type of cancer. Metabolomics may provide biomarkers useful for identification of early stage gastric cancer, potentially addressing an important clinical need. Here, we present a short review on metabolomics as a tool for biomarker discovery in human gastric cancer, with a primary focus on its use as a predictor of anticancer drug chemosensitivity, diagnosis, prognosis, and metastasis.

Comparative metabolomics analysis on hematopoietic functions of herb pair Gui-Xiong by ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry and pattern recognition approach

The compatibility of Angelicae Sinensis Radix (Danggui, DG) and Chuanxiong Rhizoma (Chuanxiong, CX), a famous herb pair Gui-Xiong (GX), can produce synergistic and complementary hematopoiesis. In present study, global metabolic profiling with ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS) combined with pattern recognition method was performed to discover the underlying hematopoietic regulation mechanisms of DG, CX and GX on hemolytic and aplastic anemia rats (HAA) induced by acetyl phenylhydrazine (APH) and cyclophosphamide (CP). Thirteen endogenous metabolites contributing to the separation of model group and control group were tentatively identified. The levels of LPCs including lysoPC (18:0), lysoPC (20:4), lysoPC (16:0) and lysoPC (18:2), sphinganine, nicotinic acid, thiamine pyrophosphate, phytosphingosine, and glycerophosphocholine increased significantly (p<0.05) in HAA, while the levels of oleic acid, 8,11,14-eicosatrienoic acid, ceramides (d18:1/14:0), and 17a-hydroxypregnenolone decreased significantly (p<0.05) in comparison with control rats. Those endogenous metabolites were chiefly involved in thiamine metabolism and sphingolipid metabolism. The metabolic deviations could be regulated closer to normal level after DG, CX and GX intervention. In term of hematopoietic function, GX was the most effective as shown by the relative distance in PLS-DA score plots and relative intensity of metabolomic strategy, reflecting the synergic action between DG and CX. The relative distance calculation was firstly used in metabolomics for semi-quantization.

Rapid LC-MS-based metabolomics method to study the Fusarium infection of barley

Ultra high performance liquid chromatography with quadrupole/time-of-flight mass spectrometry was applied to evaluate the potential of nontarget metabolomic fingerprinting in order to distinguish Fusarium-infected and control barley samples. First, the sample extraction and instrumental conditions were optimized to obtain the broadest possible representation of polar/medium-polar compounds occurring in extracts obtained from barley grain samples. Next, metabolomic fingerprints of extracts obtained from nine barley varieties were acquired under ESI conditions in both positive and negative mode. Each variety of barley was tested in two variants: artificially infected by Fusarium culmorum at the beginning of heading and a control group (no infection). In addition, the dynamics of barley infection development was monitored using this approach. The experimental data were statistically evaluated by principal component analysis, hierarchical clustering analysis, and orthogonal partial least-squares discriminant analysis. The differentiation of barley in response to F. culmorum infection was feasible using this metabolomics-based method. Analysis in positive mode provided a higher number of molecular features as compared to that performed under negative mode setting. However, the analysis in negative mode permitted the detection of deoxynivalenol and deoxynivalenol-3-glucoside considered as resistance-indicator metabolites in barley.

Metabolomics as a diagnostic tool in gastroenterology

Metabolomics has increasingly been applied in addition to other “omic” approaches in the study of the pathophysiology of different gastrointestinal diseases. Metabolites represent molecular readouts of the cell status reflecting a physiological phenotype. In addition, changes in metabolite concentrations induced by exogenous factors such as environmental and dietary factors which do not affect the genome, are taken into account. Metabolic reactions initiated by the host or gut microbiota can lead to “marker” metabolites present in different biological fluids that allow differentiation between health and disease. Several lines of evidence implicated the involvement of intestinal microbiota in the pathogenesis of inflammatory bowel disease (IBD). Also in irritable bowel syndrome (IBS), a role of an abnormal microbiota composition, so-called dysbiosis, is supported by experimental data. These compositional alterations could play a role in the aetiology of both diseases by altering the metabolic activities of the gut bacteria. Several studies have applied a metabolomic approach to identify these metabolite signatures. However, before translating a potential metabolite biomarker into clinical use, additional validation studies are required. This review summarizes contributions that metabolomics has made in IBD and IBS and presents potential future directions within the field.

LC–MS metabolomics of polar compounds

The metabolome is the complete set of small molecules coming from protein activity (anabolism and catabolism) in living systems. They have a broad range of chemical structures and physicochemical properties and therefore different analytical methodologies are necessary. Highly polar metabolites, such as sugars and most amino acids are not retained by conventional reversed-phase LC columns. Without sufficient retention, coelution may result in identification problems while the detection of compounds by MS at low concentrations may also be problematic due to ion suppression. In order to retain compounds based on their hydrophilicity, polar stationary phases and hydrophilic-interaction LC provide a complementary tool to reversed-phase LC for untargeted comprehensive metabolite fingerprinting. However, robustness of the methods is still limiting their applications. This review focuses on sample pretreatment, stationary phases, analytical methods and applications for polar compound analysis in biological matrices.

Comparison of RP-HPLC columns used for determination of nucleoside metabolic patterns in urine of cancer patients

Background: Metabolic profiling allows the measurement of a large set of both known and unknown metabolites (such as nucleosides and nucleobases) present in a biological sample (e.g., urine). Results: Separation of the isolated urinary nucleosides was performed on two connected Gemini C18 columns – 3 µm pore size (50 cm total length) – at 55°C using mobile-phase gradient elution. The Mann–Whitney U test was used to distinguish differences in the concentration of compounds in urine from urogenital cancer patients and healthy controls. Comparison of mean concentration values from the healthy and cancer groups revealed statistically significant differences (p < 0.01) for most of the metabolites studied (excluding m7G, m3C and A). Observed elevated levels of nucleosides mean concentrated values in urine in the case of cancer patients are between 1.5 and 2.0.Conclusion: These results verify the usefulness of the RP-HPLC method to investigate the urinary pattern of normal and modified nucleosides.

Radiation metabolomics and its potential in biodosimetry

PURPOSE

Radiation exposure triggers a complex network of molecular and cellular responses that impacts metabolic processes and alters the levels of metabolites. Such metabolites have potential as biomarkers for radiation dosimetry. This review provides an overview of radiation signalling and metabolism, of metabolomic approaches used in the discovery phase, and of instrumentation with the potential to assess radiation injury in the field.

APPROACH

Recent developments in fast, high-resolution chromatography and mass spectrometry and new data analysis methods allow the quantitative assessment of thousands of metabolites based on biofluids obtained non-invasively. This complex analysis leads to the discovery-phase identification of groups of metabolites useful for screening and biodosimetry by targeted quantitative measurement. Instrumentation for target analysis can be simpler than that used for discovery, so we examine current technologies based on ion mobility.

CONCLUSIONS

Recent published results and ongoing studies examine the complex changes in the levels of many metabolites caused by radiation exposure, and identify groups of small-molecule biomarkers for radiation biodosimetry. Based on results showing separation orthogonal to mass, chemical noise suppression, and high sensitivity, differential mobility mass spectrometry (DMS-MS) ion mobility spectrometry appears highly promising for the development of deployable instrumentation.

Computational strategies for metabolite identification in metabolomics

Most metabolomic data are characterized by complex spectra or chromatograms containing hundreds of peaks or features. While there are many methods for aligning or comparing these spectral features, there are few approaches for actually identifying which peaks match to which compounds. Indeed, one of the biggest unmet needs in the field of metabolomics lies in the problem of compound identification. This review describes some of the newly emerging computational strategies in metabolomics that are being used to aid in the identification of metabolites from biofluid mixtures analyzed by NMR and MS. The most successful compound-identification strategies typically involve matching spectral features of the unknown compound(s) to curated spectral databases of reference compounds. This approach is known as the identification of 'known unknowns'. However, the identification of truly novel compounds (the 'unknown unknowns') is particularly challenging and requires the use of computer-aided structure elucidation methods being applied to the purified compound. The strengths and limitations of these approaches as applied to different analytical technologies (GC-MS, LC-MS and NMR) will be discussed, as will prospects for potential improvements to existing strategies.

Advances in mass spectrometry applied to pharmaceutical metabolomics

Metabolomics, also referred to in the literature as metabonomics, is a relatively new systems biology tool for drug discovery and development and is increasingly being used to obtain a detailed picture of a drug's effect on the body. Metabolomics is the qualitative assessment and relative or absolute quantitative measurement of the endogenous metabolome, defined as the complement of all native small molecules (metabolites less than 1,500 Da). A metabolomics study frequently involves the comparative analysis of sample sets from a normal state and a perturbed state, where the perturbation can be of any nature, such as genetic knockout, administration of a drug, or change in diet or lifestyle. Advances in mass spectrometry (MS) technologies including direct introduction or in-line chromatographic separation modes, ionization techniques, mass analyzers, and detection methods have provided powerful tools to assess the molecular changes in the metabolome. This review focuses on advances in MS pertaining to the analytical data generation for the main metabolomics methods, namely, fingerprinting, nontargeted, and targeted approaches, as they are applied to pharmaceutical drug discovery and development.

Recent and potential developments in the analysis of urine: a review

Analysis of urine is a widely used diagnostic tool that traditionally measured one or, at most, a few metabolites. However, the recognition of the need for a holistic approach to metabolism led to the application of metabolomics to urine for disease diagnostics. This review looks at various aspects of urinalysis including sampling and traditional approaches before reviewing recent developments using metabolomics. Spectrometric approaches are covered briefly since there are already a number of very good reviews on NMR spectroscopy and mass spectrometry and other spectrometries are not as highly developed in their applications to metabolomics. On the other hand, there has been a recent surge in chromatographic applications dedicated to characterising the human urinary metabolome. While developments in the analysis of urine encompassing both classical approaches of urinalysis and metabolomics are covered, it must be emphasized that these approaches are not orthogonal - they both have their uses and are complementary. Regardless, the need to normalise analytical data remains an important impediment.

Comparative evaluation of software for retention time alignment of gas chromatography/time-of-flight mass spectrometry-based metabonomic data

In chromatography-based metabonomic research, retention time (RT) alignment of chromatographic peaks poses a challenge for the accurate profiling of biomarkers. Although a number of RT alignment software has been reported, the performance of these software packages have not been comprehensively evaluated. This study aimed to evaluate the RT alignment accuracy of publicly available and commercial RT alignment software. Two gas chromatography/mass spectrometry (GC/MS) datasets acquired from a mixture of standard metabolites and human bladder cancer urine samples, were used to assess three publicly available software packages, MetAlign, MZmine and TagFinder, and two commercial applications comprising the Calibration feature and Statistical Compare of ChromaTOF software. The overall RT alignment accuracies in aligning standard compounds mixture were 93, 92, 74, 73 and 42% for Calibration feature, MZmine, MetAlign, Statistical Compare and TagFinder, respectively. Additionally, unique trends were observed for the individual software with regards to the different experimental conditions related to extent and direction of RT shifts. Conflicting performance was observed for human urine samples suggesting that RT misalignments still occurred despite the use of RT alignment software. While RT alignment remains an inevitable step in data preprocessing, metabonomic researchers are recommended to perform manual check on the RT alignment of important biomarkers as part of their validation process.

Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems

To address the challenges associated with metabolomics analyses, such as identification of chemical structures and elimination of experimental artifacts, we developed a platform that integrated the chemical analysis, including identification and relative quantification, data reduction, and quality assurance components of the process. The analytical platform incorporated two separate ultrahigh performance liquid chromatography/tandem mass spectrometry (UHPLC/MS/MS(2)) injections; one injection was optimized for basic species, and the other was optimized for acidic species. This approach permitted the detection of 339 small molecules, a total instrument analysis time of 24 min (two injections at 12 min each), while maintaining a median process variability of 9%. The resulting MS/MS(2) data were searched against an in-house generated authentic standard library that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as their associated MS/MS spectra for all molecules in the library. The library allowed the rapid and high-confidence identification of the experimentally detected molecules based on a multiparameter match without need for additional analyses. This integrated platform enabled the high-throughput collection and relative quantitative analysis of analytical data and identified a large number and broad spectrum of molecules with a high degree of confidence.

Multitarget quantitative metabolic profiling of hydrophilic metabolites in fermentation broths of β-lactam antibiotics production by HILIC-ESI-MS/MS

The presented work deals with the development and comprehensive validation of a quantitative LC-electrospray ionization (ESI)-tandem mass spectrometry (MS/MS) method using a triple quadrupole instrument in the MRM mode for the metabolic profiling of amino acids, organic acids, vitamins, some biogenic amines, secondary metabolites of β-lactam antibiotics biosynthesis as well as their intermediates, and degradation products in fermentation broths of β-lactam antibiotics production (in total 57 hydrophilic compounds). A great number of chromatographic systems (22 different stationary phase/mobile phase conditions) were screened for their adequate chromatographic selectivity to cope with isobaric compounds and other critical analyte pairs. Finally, a hydrophilic interaction liquid chromatography (HILIC) method employing a zwitterionic ZIC-HILIC column was selected as best compromise. Particular focus was given on the elucidation of absolute and relative matrix effects via comparison of slopes of calibration functions of spiked matrix and standard solutions. These data as well as precision and accuracy data confirm suitability of the HILIC-ESI-MS/MS assay for metabolic profiling studies in fermentation samples. Detailed comprehensive data sets are presented which should illustrate critical issues, problems, and challenges of multitarget quantitative metabolic profiling and should outline possible strategies to circumvent pitfalls and overcome common problems.

Analytical and statistical approaches to metabolomics research

Metabolomics, the global profiling of metabolites in different living systems, has experienced a rekindling of interest partially due to the improved detection capabilities of the instrumental techniques currently being used in this area of biomedical research. The analytical methods of choice for the analysis of metabolites in search of disease biomarkers in biological specimens, and for the study of various low molecular weight metabolic pathways include NMR spectroscopy, GC/MS, CE/MS, and HPLC/MS. Global metabolite analysis and profiling of two different sets of data results in a plethora of data that is difficult to manage or interpret manually because of their subtle differences. Multivariate statistical methods and pattern-recognition programs were developed to handle the acquired data and to search for the discriminating features between data acquired from two sample sets, healthy and diseased. Metabolomics have been used in toxicology, plant physiology, and biomedical research. In this paper, we discuss various aspects of metabolomic research including sample collection, handling, storage, requirements for sample analysis, peak alignment, data interpretation using statistical approaches, metabolite identification, and finally recommendations for successful analysis.

Metabolomics for assessment of nutritional status

PURPOSE OF REVIEW

The current rise in diet-related diseases continues to be one of the most significant health problems facing both the developed and the developing world. The use of metabolomics - the accurate and comprehensive measurement of a significant fraction of important metabolites in accessible biological fluids - for the assessment of nutritional status is a promising way forward. The basic toolset, targets and knowledge are all being developed in the emerging field of metabolomics, yet important knowledge and technology gaps will need to be addressed in order to bring such assessment to practice.

RECENT FINDINGS

Dysregulation within the principal metabolic organs (e.g. intestine, adipose, skeletal muscle and liver) are at the center of a diet-disease paradigm that includes metabolic syndrome, type 2 diabetes and obesity. The assessment of both essential nutrient status and the more comprehensive systemic metabolic response to dietary, lifestyle and environmental influences (e.g. metabolic phenotype) are necessary for the evaluation of status in individuals that can identify the multiple targets of intervention needed to address metabolic disease.

SUMMARY

The first proofs of principle building the knowledge to bring actionable metabolic diagnostics to practice through metabolomics are now appearing.

Comparing capillary electrophoresis-mass spectrometry fingerprints of urine samples obtained after intake of coffee, tea, or water

Metabolomic fingerprinting is a growing strategy for characterizing complex biological samples without detailed prior knowledge about the metabolic system. A two-way analysis system with liquid separation and mass spectrometric detection provides detail-rich data suitable for such fingerprints. As a model study, human urine samples, obtained after intake of coffee, tea, or water, were analyzed with capillary electrophoresis electrospray ionization time-of-flight mass spectrometry (CE-ESI-TOF-MS). In-house-developed software (in Matlab) was utilized to manage and explore the large amount of data acquired (230 CE-MS runs, each with 50-100 million nonzero data points). After baseline and noise reduction, followed by suitable binning in time and m/z, the data sets comprised 9 and 14 million data points in negative and positive ESI mode, respectively. Finally, a signal threshold was applied, further reducing the number to about 100 000 data points per data set. A set of interactive exploratory tools, utilizing principal component analysis (PCA) and analysis of variance (ANOVA) results based on a general linear model, facilitated visual interpretation with score plots (for group assessment) and differential fingerprints (for "hot spot" detection). In the model study highly significant differences due to beverage intake were obtained among the 10 first principal components (p < 10(-6) for two of the components in both ESI modes). Especially, the contrasts between "coffee" and "tea or water" indicated several "hot spots" with highly elevated intensities (e.g., for uncharged masses 93, 94, 109, 119, 123, 132, 148, 169, 178, 187, 190, and 193) suitable for further analysis, for example, with tandem MS.