Gas chromatography/mass spectrometry in metabolic profiling of biological fluids

The Potential of Fecal Volatile Organic Compound Analysis for the Early Diagnosis of Late-Onset Sepsis in Preterm Infants: A Narrative Review

Early diagnosis and treatment of late-onset sepsis (LOS) is crucial for survival, but challenging. Intestinal microbiota and metabolome alterations precede the clinical onset of LOS, and the preterm gut is considered an important source of bacterial pathogens. Fecal volatile organic compounds (VOCs), formed by physiologic and pathophysiologic metabolic processes in the preterm gut, reflect a complex interplay between the human host, the environment, and microbiota. Disease-associated fecal VOCs can be detected with an array of devices with various potential for the development of a point-of-care test (POCT) for preclinical LOS detection. While characteristic VOCs for common LOS pathogens have been described, their VOC profiles often overlap with other pathogens due to similarities in metabolic pathways, hampering the construction of species-specific profiles. Clinical studies have, however, successfully discriminated LOS patients from healthy individuals using fecal VOC analysis with the highest predictive value for Gram-negative pathogens. This review discusses the current advancements in the development of a non-invasive fecal VOC-based POCT for early diagnosis of LOS, which may potentially provide opportunities for early intervention and targeted treatment and could improve clinical neonatal outcomes. Identification of confounding variables impacting VOC synthesis, selection of an optimal detection device, and development of standardized sampling protocols will allow for the development of a novel POCT in the near future.

User-friendly relative quantification procedure for gas chromatography/mass spectrometry-based plasma metabolome analysis

RATIONALE

Recently, metabolome analysis has been applied to a variety of research fields, but differences between batches or facilities can cause discrepancies in the results of such analyses. To resolve these issues using comprehensive metabolome analysis, in which it is difficult to perform quantitative analyses of all detected metabolites, internal standard compounds are used to obtain relative metabolite levels. This study investigated gas chromatography/mass spectrometry-based plasma metabolome analysis methods that are superior to relative quantification using internal standard compounds.

METHODS

In experiment I, four analyses were performed under different analytical conditions at one facility, and then the data from the four analyses were compared. In experiment II, the same samples were analyzed at three facilities, and then the data from the three facilities were compared.

RESULTS

Regarding the relative values obtained through comparisons with the internal standard compound, differences in the analytical results were observed among the four analytical conditions in experiment I and among the three facilities in experiment II, and the differences observed among the three facilities (experiment II) were larger. When correction was performed using plasma as a quality control, which is the procedure suggested in this study, these differences were markedly ameliorated.

CONCLUSION

The suggested procedure involves the analysis of a plasma standard as a quality control for each batch and the calculation of relative target plasma to quality-control plasma values for each metabolite. This is an easy and low-cost method and could be readily employed by researchers during comprehensive plasma metabolome analysis.

Mass Spectrometry-Based Metabolomics Multi-platform for Alzheimer's Disease Research

The integration of complementary analytical platforms is nowadays the most common strategy for comprehensive metabolomics analysis of complex biological systems. In this chapter, we describe methods and tips for the application of a mass spectrometry multi-platform in Alzheimer's disease research, based on the combination of direct mass spectrometry and orthogonal hyphenated approaches, namely, reversed-phase ultrahigh-performance liquid chromatography and gas chromatography. These procedures have been optimized for the analysis of multiple biological samples from human patients and transgenic animal models, including blood serum, various brain regions (e.g., hippocampus, cortex, cerebellum, striatum, olfactory bulbs), and other peripheral organs (e.g., liver, kidney, spleen, thymus).

Mass Spectrometric Identification of Metabolites after Magnetic-Pulse Treatment of Infected Pyrus communis L. Microplants

The major goal of this study is to create a venue for further work on the effect of pulsed magnetic fields on plant metabolism. It deals with metabolite synthesis in the aforementioned conditions in microplants of Pyrus communis L. So far, there have been glimpses into the governing factors of plant biochemistry in vivo, and low-frequency pulsed magnestatic fields have been shown to induce additional electric currents in plant tissues, thus perturbing the value of cell membrane potential and causing the biosynthesis of new metabolites. In this study, sixty-seven metabolites synthesized in microplants within 3-72 h after treatment were identified and annotated. In total, thirty-one metabolites were produced. Magnetic-pulse treatment caused an 8.75-fold increase in the concentration of chlorogenic acid (RT = 8.33 ± 0.0197 min) in tissues and the perturbation of phenolic composition. Aucubin, which has antiviral and antistress biological activity, was identified as well. This study sheds light on the effect of magnetic fields on the biochemistry of low-molecular-weight metabolites of pear plants in vitro, thus providing in-depth metabolite analysis under optimized synthetic conditions. This study utilized high-resolution gas chromatography-mass spectrometry, metabolomics methods, stochastic dynamics mass spectrometry, quantum chemistry, and chemometrics, respectively. Stochastic dynamics uses the relationships between measurands and molecular structures of silylated carbohydrates, showing virtually identical mass spectra and comparable chemometrics parameters.

Scientific characterization methods for better utilization of cattle dung and urine: a concise review

Cattle are usually raised for food, manure, leather, therapeutic, and draught purposes. Biowastes from cattle, such as dung and urine, harbor a diverse group of crucial compounds, metabolites/chemicals, and microorganisms that may benefit humans for agriculture, nutrition, therapeutics, industrial, and other utility products. Several bioactive compounds have been identified in cattle dung and urine, which possess unique properties and may vary based on agro-climatic zones and feeding practices. Therefore, cattle dung and urine have great significance, and a balanced nutritional diet may be a key to improved quality of these products/by-products. This review primarily focuses on the scientific aspects of biochemical and microbial characterization of cattle biowastes. Various methods including genomics for analyzing cattle dung and gas chromatography-mass spectroscopy for cattle urine have been reviewed. The presented information might open doors for the further characterization of cattle resources for heterogeneous applications in the production of utility items and addressing research gaps. Methods for cattle's dung and urine characterization.

Mass spectrometry-based techniques for identification of compounds in milk and meat matrix

Food including milk and meat is often viewed as the mixture of different components such as fat, protein, carbohydrates, moisture and ash, which are estimated using well-established protocols and techniques. However, with the advent of metabolomics, low-molecular weight substances, also known as metabolites, have been recognized as one of the major factors influencing the production, quality and processing. Therefore, different separation and detection techniques have been developed for the rapid, robust and reproducible separation and identification of compounds for efficient control in milk and meat production and supply chain. Mass-spectrometry based techniques such as GC-MS and LC-MS and nuclear magnetic resonance spectroscopy techniques have been proven successful in the detailed food component analysis owing to their associated benefits. Different metabolites extraction protocols, derivatization, spectra generated, data processing followed by data interpretation are the major sequential steps for these analytical techniques. This chapter deals with not only the detailed discussion of these analytical techniques but also sheds light on various applications of these analytical techniques in milk and meat products.

Determination of the intermediates in glycolysis and tricarboxylic acid cycle with an improved derivatization strategy using gas chromatography-mass spectrometry in complex samples

Traditional Chinese medicine (TCM) is recognized as a complex matrix, and improved analytical methods are crucial to extract the key indicators and depict the interaction and alteration of the complex matrix. Shenqi Fuzheng Injection (SQ), a water extract of Radix Codonopsis and Radix Astragali, has demonstrated preventative effects on myotube atrophy induced by chemotherapeutic agents. To achieve the improved analytical capability of complex biological samples, we established a highly reproducible, sensitive, specific, and robust gas chromatography-tandem mass spectrometry (GC-MS) method to detect glycolysis and tricarboxylic acid (TCA) cycle intermediates with optimized factors in the extraction and derivatization process. Our method detected fifteen metabolites and covered most intermediate metabolites in glycolysis and TCA cycles, including glucose, glucose-6-phosphate, fructose-6-phosphate, dihydroxyacetone phosphate, 3-diphosphoglycerate, phosphoenolpyruvate, pyruvate, lactate, citrate, cis-aconitate, isocitrate, α-ketoglutarate, succinate, fumarate, and malate. Through methodological verification of the method, it was found that the linear correlation coefficients of each compound in the method were greater than 0.98, all of which had lower limits of quantification, the recovery rate was 84.94-104.45%, and the accuracy was 77.72-104.92%. The intraday precision was 3.72-15.37%, the interday precision was 5.00-18.02%, and the stability was 7.85-15.51%. Therefore, the method has good linearity, accuracy, precision, and stability. The method was further applied to study the attenuating effects of the SQ in a chemotherapeutic agents-induced C2C12 myotube atrophy model to evaluate the changes in the tricarboxylic acid cycle and glycolytic products under the action by the complex systems of TCM and disease model. Our study provided an improved method to explore TCM's pharmacodynamic constituents and action mechanisms.

Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets

Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.

Metabolomics-based safety evaluation of acute exposure to electronic cigarettes in mice

INTRODUCTION

A growing number of epidemiological evidence reveals that electronic cigarettes (E-cigs) were associated with pneumonia, hypertension and atherosclerosis, but the toxicological evaluation and mechanism of E-cigs were largely unknown.

OBJECTIVE

Our study was aimed to explore the adverse effects on organs and metabolomics changes in C57BL/6J mice after acute exposure to E-cigs.

METHODS AND RESULTS

Hematoxylin and eosin (H&E) staining found pathological changes in tissues after acute exposure to E-cigs, such as inflammatory cell infiltration, nuclear pyknosis, and intercellular interstitial enlargement. E-cigs could increase apoptosis-positive cells in a time-dependent way using Terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL) assay. Oxidative damage indicators of reactive oxygen species (ROS), malondialdehyde (MDA) and 4-hydroxynonena (4-HNE) were also elevated after E-cigs exposure. There was an increasing trend of total glycerol and cholesterol in serum, while the glucose and liver enzymes including alanine aminotransferase (ALT), aspartate transaminase (AST), gamma-glutamyltranspeptidase (γ-GT) had no significant change compared to that of control. Further, Q Exactive high field (HF) mass spectrometer was used to conduct metabolomics, which revealed that differential metabolites including l-carnitine, Capryloyl glycine, etc. Trend analysis showed the type of compounds that change over time. Pathway enrichment analysis indicated that E-cigs affected 24 metabolic pathways, which were mainly regulated amino acid metabolism, further affected the tricarboxylic acid (TCA) cycle. Additionally, metabolites-diseases network analysis found that the type 2 diabetes mellitus, propionic acidemia, defect in long-chain fatty acids transport and lung cancer may be related to E-cigs exposure.

CONCLUSIONS

Our findings provided important clues for metabolites biomarkers of E-cigs acute exposure and are beneficial for disease prevention.

Extraction of High-Value Chemicals from Plants for Technical and Medical Applications

Plants produce a variety of high-value chemicals (e.g., secondary metabolites) which have a plethora of biological activities, which may be utilised in many facets of industry (e.g., agrisciences, cosmetics, drugs, neutraceuticals, household products, etc.). Exposure to various different environments, as well as their treatment (e.g., exposure to chemicals), can influence the chemical makeup of these plants and, in turn, which chemicals will be prevalent within them. Essential oils (EOs) usually have complex compositions (>300 organic compounds, e.g., alkaloids, flavonoids, phenolic acids, saponins and terpenes) and are obtained from botanically defined plant raw materials by dry/steam distillation or a suitable mechanical process (without heating). In certain cases, an antioxidant may be added to the EO (EOs are produced by more than 17,500 species of plants, but only ca. 250 EOs are commercially available). The interesting bioactivity of the chemicals produced by plants renders them high in value, motivating investment in their production, extraction and analysis. Traditional methods for effectively extracting plant-derived biomolecules include cold pressing and hydro/steam distillation; newer methods include solvent/Soxhlet extractions and sustainable processes that reduce waste, decrease processing times and deliver competitive yields, examples of which include microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE), subcritical water extraction (SWE) and supercritical CO2 extraction (scCO2). Once extracted, analytical techniques such as chromatography and mass spectrometry may be used to analyse the contents of the high-value extracts within a given feedstock. The bioactive components, which can be used in a variety of formulations and products (e.g., displaying anti-aging, antibacterial, anticancer, anti-depressive, antifungal, anti-inflammatory, antioxidant, antiparasitic, antiviral and anti-stress properties), are biorenewable high-value chemicals.

GC-TOF/MS-Based Metabolomics for Comparison of Volar and Non-Volar Skin Types

Skin has heterogenous identities on different body sites despite similar cellular compositions. There are two types of skin, volar (palmoplantar) and non-volar (dorsal), which are characterized by epidermal thickness, pigmentation, and presence of hair follicles. However, the mechanisms underlying the development of these different skin types remain unclear. To investigate these, we profiled the cellular metabolites of volar and non-volar skin in mice using gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS), and further assessed the metabolic differences between them. In total, 96 metabolites from both volar and non-volar skin of mice were identified using the BinBase database system. Metabolomics analysis revealed important differences associated with amino acid metabolism (phenylalanine, tyrosine, and tryptophan biosynthesis; aspartate and glutamate metabolism), sugar metabolism (pentose phosphate pathway), and nucleotide metabolism (pyrimidine metabolism) in volar skin. Fifty metabolites were identified as potential biomarkers differentiating the physiological characteristics of these skin types. Of these, nine were highly increased whereas 41 were significantly decreased in volar skin compared with those in non-volar skin. Overall, these results provide valuable information for understanding the metabolic differences between volar and non-volar skin.

Metabolomics Research in Periodontal Disease by Mass Spectrometry

Periodontology is a newer field relative to other areas of dentistry. Remarkable progress has been made in recent years in periodontology in terms of both research and clinical applications, with researchers worldwide now focusing on periodontology. With recent advances in mass spectrometry technology, metabolomics research is now widely conducted in various research fields. Metabolomics, which is also termed metabolomic analysis, is a technology that enables the comprehensive analysis of small-molecule metabolites in living organisms. With the development of metabolite analysis, methods using gas chromatography–mass spectrometry, liquid chromatography–mass spectrometry, capillary electrophoresis–mass spectrometry, etc. have progressed, making it possible to analyze a wider range of metabolites and to detect metabolites at lower concentrations. Metabolomics is widely used for research in the food, plant, microbial, and medical fields. This paper provides an introduction to metabolomic analysis and a review of the increasing applications of metabolomic analysis in periodontal disease research using mass spectrometry technology.

Targeted and untargeted metabolomic analysis of Procambarus clarkii exposed to a "chemical cocktail" of heavy metals and diclofenac

Water pollution poses an important problem, but limited information is available about the joined effects of xenobiotics of different chemical groups to evaluate the real biological response. Procambarus clarkii (P. clarkii) has been demonstrated to be a good bioindicator for assessing the quality of aquatic ecosystems. In this work, we studied the bioaccumulation of cadmium (Cd), arsenic (As) and diclofenac (DCF) in different tissues of P. clarkii during 21 days after the exposure to a "chemical cocktail" of As, Cd and DCF, and until 28 days considering a depuration period. In addition, a combined untargeted and targeted metabolomic analysis was carried out to delve the metabolic impairments caused as well as the metabolization of DCF. Our results indicate that As and Cd were mainly accumulated in the hepatopancreas followed by gills and finally abdominal muscle. As and Cd show a general trend to increase the concentration throughout the exposure experience, while a decrease in the concentration of these elements is observed after 7 days of the depuration process. This is also the case in the abdominal muscle for Cd, but not for As and DCF, which increased the concentration in this tissue in the depuration phase. The hepatopancreas showed the greatest number of metabolic pathways affected. Thus, we observed a crucial bioaccumulation of xenobiotics and impairments of metabolites in different tissues. This is the first study combining the exposure to metals and pharmaceutically active compounds in P. clarkii by untargeted metabolomics including the biotransformation of DCF.

Gas chromatography-mass spectrometry (GC-MS) profiling reveals substantial metabolome diversity in seabuckthorn (Hippophae rhamnoides L.) berries originating from different geographical regions in the Indian Himalayas

INTRODUCTION

Seabuckthorn (Hippophae rhamnoides L.) is a high-altitude plant with immense medicinal, nutritional, and therapeutic value. Earlier studies have documented the presence of various useful bioactive substances in this species; however, comprehensive metabolome profiling of seabuckthorn berries originating from different regions of the Indian Himalayas has not been undertaken.

OBJECTIVE

Metabolomic profiling of seabuckthorn berries originating from different geographical sites in the Himachal Pradesh and Jammu & Kashmir regions of the Indian Himalayas was performed by using gas chromatography-mass spectrometry.

MATERIALS AND METHODS

The GC-MS metabolome profiles of seabuckthorn berries collected from different sites (altitude 1,400-4,270 m; average temperature 8°C-27°C) were subjected to multivariate analysis following principal component analysis and hierarchical clustering analysis.

RESULTS

The GC-MS results showed substantial variability for berry metabolites, including fatty acids, alkyl ethers, and alkyl esters. Fatty acids and their esters were mainly responsible for the variation in the berry metabolome. The metabolite expression profile heat map revealed two distinct groups of seabuckthorn berries originating from Himachal Pradesh (Lahaul and Spiti) and Jammu & Kashmir (Leh, Nubra, and Kargil), the former showing higher expression of metabolites. Interestingly, a strong negative association existed between altitude and the amounts of metabolites such as amides, alkyl esters, alcohols, sugars, and sugar esters. In contrast, temperature showed a strong positive association with ketone and alkyl ether levels.

CONCLUSION

GC-MS profiling provides important phytochemical indicators to distinguish between seabuckthorn berries from different geographical sites. Our metabolome profiling analysis generated valuable information that will be useful in the formulation of various seabuckthorn products, benefiting farmers and industries.

Improved Non-Invasive Diagnosis of Bladder Cancer with an Electronic Nose: A Large Pilot Study

BACKGROUND

Bladder cancer (BCa) emits specific volatile organic compounds (VOCs) in the urine headspace that can be detected by an electronic nose. The diagnostic performance of an electronic nose in detecting BCa was investigated in a pilot study.

METHODS

A prospective, single-center, controlled, non-randomized, phase 2 study was carried out on 198 consecutive subjects (102 with proven BCa, 96 controls). Urine samples were evaluated with an electronic nose provided with 32 volatile gas analyzer sensors. The tests were repeated at least two times per sample. Accuracy, sensitivity, specificity, and variability were evaluated using mainly the non-parametric combination method, permutation tests, and discriminant analysis classification.

RESULTS

Statistically significant differences between BCa patients and controls were reported by 28 (87.5%) of the 32 sensors. The overall discriminatory power, sensitivity, and specificity were 78.8%, 74.1%, and 76%, respectively; 13/96 (13.5%) controls and 29/102 (28.4%) BCa patients were misclassified as false positive and false negative, respectively. Where the most efficient sensors were selected, the sensitivity and specificity increased up to 91.1% (72.5-100) and 89.1% (81-95.8), respectively. None of the tumor characteristics represented independent predictors of device responsiveness.

CONCLUSIONS

The electronic nose might represent a potentially reliable, quick, accurate, and cost-effective tool for non-invasive BCa diagnosis.

Analytical Platforms for Mass Spectrometry-Based Metabolomics of Polar and Ionizable Metabolites

Metabolomics studies rely on the availability of suitable analytical platforms to determine a vast collection of chemically diverse metabolites in complex biospecimens. Liquid chromatography-mass spectrometry operated under reversed-phase conditions is the most commonly used platform in metabolomics, which offers extensive coverage for nonpolar and moderately polar compounds. However, complementary techniques are required to obtain adequate separation of polar and ionic metabolites, which are involved in several fundamental metabolic pathways. This chapter focuses on the main mass-spectrometry-based analytical platforms used to determine polar and/or ionizable compounds in metabolomics (GC-MS, HILIC-MS, CE-MS, IPC-MS, and IC-MS). Rather than comprehensively describing recent applications related to GC-MS, HILIC-MS, and CE-MS, which have been covered in a regular basis in the literature, a brief discussion focused on basic principles, main strengths, limitations, as well as future trends is presented in this chapter, and only key applications with the purpose of illustrating important analytical aspects of each platform are highlighted. On the other hand, due to the relative novelty of IPC-MS and IC-MS in the metabolomics field, a thorough compilation of applications for these two techniques is presented here.

Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases

Metabolomics is a branch of ‘omics’ sciences that utilises a couple of analytical tools for the identification of small molecules (metabolites) in a given sample. The overarching goal of metabolomics is to assess these metabolites quantitatively and qualitatively for their diagnostic, therapeutic, and prognostic potentials. Its use in various aspects of life has been documented. We have also published, howbeit in animal models, a few papers where metabolomic approaches were used in the study of metabolic disorders, such as metabolic syndrome, diabetes, and obesity. As the goal of every research is to benefit humankind, the purpose of this review is to provide insights into the applicability of metabolomics in medicine vis-à-vis its role in biomarker discovery for disease diagnosis and management. Here, important biomarkers with proven diagnostic and therapeutic relevance in the management of disease conditions, such as Alzheimer’s disease, dementia, Parkinson’s disease, inborn errors of metabolism (IEM), diabetic retinopathy, and cardiovascular disease, are noted. The paper also discusses a few reasons why most metabolomics-based laboratory discoveries are not readily translated to the clinic and how these could be addressed going forward.

Urine Metabolomics Profiling of Lumbar Disc Herniation and its Traditional Chinese Medicine Subtypes in Patients Through Gas Chromatography Coupled With Mass Spectrometry

Lumbar disc herniation (LDH) possesses complex pathogenesis, which has not been well elucidated yet. To date, specific or early diagnosis of LDH remains unavailable, resulting in missed opportunity for effective treatment. According to Traditional Chinese medicine (TCM) theory, LDH can be divided into two subtypes (reality syndrome and deficiency syndrome). The purpose of this study was to analyze the metabolic disorders of LDH and its TCM subtypes and screen out potential biomarkers for LDH diagnosis. Gas chromatography coupled with mass spectrometry (GC-MS) was applied to test the urine samples from 66 participants (30 healthy volunteers, 18 LDH patients with deficiency syndrome and 18 patients with reality syndrome). PCA analysis showed a distinct separation tendency between the healthy subjects and LDH patients but no obvious separation between the different syndromes (reality syndrome and deficiency syndrome) of LDH patients. As a result, 23 metabolites were identified significantly altered in the LDH patients, as compared with the healthy subjects. The altered metabolites belong to amino acid metabolism, nucleic acid metabolism, carbohydrate metabolism, and vitamin metabolism, which are related to osteoporosis and inflammation. Our results indicate metabolic disorders of LDH and thereby propose a group of metabolic biomarkers for potential application in early diagnosis of LDH in clinic, which provide a reasonable explanation for the pathogenesis of LDH.

Putative markers for the detection of early-stage bladder cancer selected by urine metabolomics

BACKGROUND

Early detection of bladder cancer remains challenging because patients with early-stage bladder cancer usually have no incentive to take cytology or cystoscopy tests if they are asymptomatic. Our goal is to find non-invasive marker candidates that may help us gain insight into the metabolism of early-stage bladder cancer and be examined in routine health checks.

RESULTS

We acquired urine samples from 124 patients diagnosed with early-stage bladder cancer or hernia (63 cancer patients and 61 controls). In which 100 samples were included in our marker discovery cohort, and the remaining 24 samples were included in our independent test cohort. We obtained metabolic profiles of 922 compounds of the samples by gas chromatography-mass spectrometry. Based on the metabolic profiles of the marker discovery cohort, we selected marker candidates using Wilcoxon rank-sum test with Bonferroni correction and leave-one-out cross-validation; we further excluded compounds detected in less than 60% of the bladder cancer samples. We finally selected eight putative markers. The abundance of all the eight markers in bladder cancer samples was high but extremely low in hernia samples. Moreover, the up-regulation of these markers might be in association with sugars and polyols metabolism.

CONCLUSIONS

In the present study, comparative urine metabolomics selected putative metabolite markers for the detection of early-stage bladder cancer. The suggested relations between early-stage bladder cancer and sugars and polyols metabolism may create opportunities for improving the detection of bladder cancer.

Impact of sample containers on gas chromatography mass spectrometry based plasma untargeted and targeted metabolomics

Plasticware and glassware used in the sample processing stage could result in different analysis performance for macromolecules, which led to the speculation that a similar effect could happen to small molecules. To confirm the speculation, pooled human plasma sample spiked with and without metabolite standards was prepared with three most commonly used container materials (glass, deactivated glass and polypropylene) supplied by different manufacturers after a two-step liquid-liquid extraction, followed by gas chromatography mass spectrometry (GC-MS) based untargeted and targeted metabolomics. The results showed that both GC-MS-based targeted and untargeted metabolomics could be influenced significantly by not only the container material but also the manufacturing procedures employed by different vendors. As a general guideline, it would be highly practical to use the same type of container from the same manufacturer during a whole project. Our study could be extremely valuable for the fellow researchers when dealing with the sample containers for GC-MS-based untargeted and targeted metabolomics.

The Role of Lipidomics in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome commonly diagnosed in early childhood; it is usually characterized by impairment in reciprocal communication and speech, repetitive behaviors, and social withdrawal with loss in communication skills. Its development may be affected by a variety of environmental and genetic factors. Trained physicians diagnose and evaluate the severity of ASD based on clinical evaluations of observed behaviors. As such, this approach is inevitably dependent on the expertise and subjective assessment of those administering the clinical evaluations. There is a need to identify objective biological markers associated with diagnosis or clinical severity of the disorder. Several important issues and concerns exist regarding the diagnostic competence of the many abnormal plasma metabolites produced in the different biochemical pathways evaluated in individuals with ASD. The search for high-performing bio-analytes to diagnose and follow-up ASD development is still a major target in medicine. Dysregulation in the oxidative stress response and proinflammatory processes are major etiological causes of ASD pathogenesis. Furthermore, dicarboxylic acid metabolites, cholesterol-related metabolites, phospholipid-related metabolites, and lipid transporters and mediators are impaired in different pathological conditions that have a role in the ASD etiology. A mechanism may exist by which pro-oxidant environmental stressors and abnormal metabolites regulate clinical manifestations and development of ASD.

The Comprehensive and Reliable Detection of Secondary Metabolites in Trichoderma reesei: A Tool for the Discovery of Novel Substances

A method based on reversed phase high-performance liquid chromatography coupled with electrospray ionization high-resolution mass spectrometry (RP-HPLC-ESI-HRMS) for the comprehensive and reliable detection of secondary metabolites of Trichoderma reesei cultured in synthetic minimal liquid medium is presented. A stable isotope-assisted (SIA) workflow is used, which allows the automated, comprehensive extraction of truly fungal metabolite-derived LC-MS signals from the acquired chromatographic data. The subsequent statistical data analysis and a typical outcome of such a metabolomics data evaluation are shown by way of example in a previously published study on the influence of the pleiotropic regulator transcription factor Xylanase promoter binding protein 1 (Xpp1) in T. reesei on secondary metabolism.

Teleological role of L-2-hydroxyglutarate dehydrogenase in the kidney

L-2-hydroxyglutarate (L-2HG) is an oncometabolite found elevated in renal tumors. However, this molecule might have physiological roles that extend beyond its association with cancer, as L-2HG levels are elevated in response to hypoxia and during Drosophila larval development. L-2HG is known to be metabolized by L-2HG dehydrogenase (L2HGDH), and loss of L2HGDH leads to elevated L-2HG levels. Despite L2HGDH being highly expressed in the kidney, its role in renal metabolism has not been explored. Here, we report our findings utilizing a novel CRISPR/Cas9 murine knockout model, with a specific focus on the role of L2HGDH in the kidney. Histologically, L2hgdh knockout kidneys have no demonstrable histologic abnormalities. However, GC-MS metabolomics demonstrates significantly reduced levels of the TCA cycle intermediate succinate in multiple tissues. Isotope labeling studies with [U-13C] glucose demonstrate that restoration of L2HGDH in renal cancer cells (which lowers L-2HG) leads to enhanced incorporation of label into TCA cycle intermediates. Subsequent biochemical studies demonstrate that L-2HG can inhibit the TCA cycle enzyme α-ketoglutarate dehydrogenase. Bioinformatic analysis of mRNA expression data from renal tumors demonstrates that L2HGDH is co-expressed with genes encoding TCA cycle enzymes as well as the gene encoding the transcription factor PGC-1α, which is known to regulate mitochondrial metabolism. Restoration of PGC-1α in renal tumor cells results in increased L2HGDH expression with a concomitant reduction in L-2HG levels. Collectively, our analyses provide new insight into the physiological role of L2HGDH as well as mechanisms that promote L-2HG accumulation in disease states.

Gut Biofactory-Neurocompetent Metabolites within the Gastrointestinal Tract. A Scoping Review

The gut microbiota have gained much scientific attention recently. Apart from unravelling the taxonomic data, we should understand how the altered microbiota structure corresponds to functions of this complex ecosystem. The metabolites of intestinal microorganisms, especially bacteria, exert pleiotropic effects on the human organism and contribute to the host systemic balance. These molecules play key roles in regulating immune and metabolic processes. A subset of them affect the gut brain axis signaling and balance the mental wellbeing. Neurotransmitters, short chain fatty acids, tryptophan catabolites, bile acids and phosphatidylcholine, choline, serotonin, and L-carnitine metabolites possess high neuroactive potential. A scoping literature search in PubMed/Embase was conducted up until 20 June 2020, using three major search terms "microbiota metabolites" AND "gut brain axis" AND "mental health". This review aimed to enhance our knowledge regarding the gut microbiota functional capacity, and support current and future attempts to create new compounds for future clinical interventions.

A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm

A urine metabolomics study based on gas chromatography-mass spectrometry (GC-MS) and multivariate statistical analysis was applied to distinguish rat bladder cancer. Urine samples with different stages were collected from animal models, i.e., the early stage, medium stage, and advanced stage of the bladder cancer model group and healthy group. After resolving urea with urease, the urine samples were extracted with methanol and, then, derived with N, O-Bis(trimethylsilyl) trifluoroacetamide and trimethylchlorosilane (BSTFA + TMCS, 99 : 1, v/v), before analyzed by GC-MS. Three classification models, i.e., healthy control vs. early- and middle-stage groups, healthy control vs. advanced-stage group, and early- and middle-stage groups vs. advanced-stage group, were established to analyze these experimental data by using Random Forests (RF) algorithm, respectively. The classification results showed that combining random forest algorithm with metabolites characters, the differences caused by the progress of disease could be effectively exhibited. Our results showed that glyceric acid, 2, 3-dihydroxybutanoic acid, N-(oxohexyl)-glycine, and D-turanose had higher contributions in classification of different groups. The pathway analysis results showed that these metabolites had relationships with starch and sucrose, glycine, serine, threonine, and galactose metabolism. Our study results suggested that urine metabolomics was an effective approach for disease diagnosis.

Metabolomic Biomarkers in Anxiety Disorders

Anxiety disorders range among the most prevalent psychiatric disorders and belong to the leading disorders in the study of the total global burden of disease. Anxiety disorders are complex conditions, with not fully understood etiological mechanisms. Numerous factors, including psychological, genetic, biological, and chemical factors, are thought to be involved in their etiology. Although the diagnosis of anxiety disorders is constantly evolving, diagnostic manuals rely on symptom lists, not on objective biomarkers and treatment effects are small to moderate. The underlying biological factors that drive anxiety disorders may be better suited to serve as biomarkers for guiding personalized medicine, as they are objective and can be measured externally. Therefore, the incorporation of novel biomarkers into current clinical methods might help to generate a classification system for anxiety disorders that can be linked to the underlying dysfunctional pathways. The study of metabolites (metabolomics) in a large-scale manner shows potential for disease diagnosis, for stratification of patients in a heterogeneous patient population, for monitoring therapeutic efficacy and disease progression, and for defining therapeutic targets. All of these are important properties for anxiety disorders, which is a multifactorial condition not involving a single-gene mutation. This review summarizes recent investigations on metabolomics studies in anxiety disorders.

An improved pseudotargeted GC-MS/MS-based metabolomics method and its application in radiation-induced hepatic injury in a rat model

The liver is the pivotal metabolic organ primarily responsible for metabolic activities, detoxification and regulation of carbohydrate, protein, amino acid, and lipid metabolism. However, very little is known about the complicated pathophysiologic mechanisms of liver injury result from ionizing radiation exposure. Therefore, a pseudotargeted metabolomics approach based on gas chromatography-tandem mass spectrometry with selected reaction monitoring (GC-MS-SRM) was developed to study metabolic alterations of liver tissues in radiation-induced hepatic injury. The pseudotargeted GC-MS-SRM method was validated with satisfactory analytical characteristics in terms of precision, linearity, sensitivity and recovery. Compared to the SIM-based approach, the SRM scanning method had mildly better precision, higher sensitivity, and wider linear ranges. A total of 37 differential metabolites associated with radiation-induced hepatic injury were identified using the GC-MS-SRM metabolomics method. Global metabolic clustering analysis showed that amino acids, carbohydrates, unsaturated fatty acids, organic acids, metabolites associated with pyrimidine metabolism, ubiquinone biosynthesis and oxidative phosphorylation appeared significantly declined after high dose irradiation exposure, whereas metabolites related to lysine catabolism, glycerolipid metabolism and glutathione metabolism presented the opposite behavior. These changes indicate energy deficiency, antioxidant defense damage, accumulation of ammonia and lipid oxidation of liver tissues in response to radiation exposure. It is shown that the developed pseudotargeted method based on GC-MS-SRM is a useful tool for metabolomics study.

Analyses of Cholesterol Metabolites of Optic Nerve Using GC-MS Methods

Gas chromatography-mass spectrometry (GC-MS) is considered the gold standard for analyzing and quantifying the presence of biological compounds in tissue samples due to its high sensitivity, peak resolution, and reproducibility. In this chapter, we describe a step-by-step modified Bligh and Dyer protocol for lipid extraction from the optic nerve tissue and a procedure for GC-MS analyses of the lipid extract. These protocols are based on our experience and can be modified depending on samples and compounds of interest.

Metabolomics in Psychiatric Disorders: What We Learn from Animal Models

Biomarkers are a recent research target within biological factors of psychiatric disorders. There is growing evidence for deriving biomarkers within psychiatric disorders in serum or urine samples in humans, however, few studies have investigated this differentiation in brain or cerebral fluid samples in psychiatric disorders. As brain samples from humans are only available at autopsy, animal models are commonly applied to determine the pathogenesis of psychiatric diseases and to test treatment strategies. The aim of this review is to summarize studies on biomarkers in animal models for psychiatric disorders. For depression, anxiety and addiction disorders studies, biomarkers in animal brains are available. Furthermore, several studies have investigated psychiatric medication, e.g., antipsychotics, antidepressants, or mood stabilizers, in animals. The most notable changes in biomarkers in depressed animal models were related to the glutamate-γ-aminobutyric acid-glutamine-cycle. In anxiety models, alterations in amino acid and energy metabolism (i.e., mitochondrial regulation) were observed. Addicted animals showed several biomarkers according to the induced drugs. In summary, animal models provide some direct insights into the cellular metabolites that are produced during psychiatric processes. In addition, the influence on biomarkers due to short- or long-term medication is a noticeable finding. Further studies should combine representative animal models and human studies on cerebral fluid to improve insight into mental disorders and advance the development of novel treatment strategies.

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite

There is a lack of experimental reference materials and standards for metabolomics measurements, such as urine, plasma, and other human fluid samples. Reasons include difficulties with supply, distribution, and dissemination of information about the materials. Additionally, there is a long lead time because reference materials need their compositions to be fully characterized with uncertainty, a labor-intensive process for material containing thousands of relevant compounds. Furthermore, data analysis can be hampered by different methods using different software by different vendors. In this work, we propose an alternative implementation of reference materials. Instead of characterizing biological materials based on their composition, we propose using untargeted metabolomic data such as nuclear magnetic resonance (NMR) or gas and liquid chromatography-mass spectrometry (GC-MS and LC-MS) profiles. The profiles are then distributed with the material accompanying the certificate, so that researchers can compare their own metabolomic measurements with the reference profiles. To demonstrate this approach, we conducted an interlaboratory study (ILS) in which seven National Institute of Standards and Technology (NIST) urine Standard Reference Material^®s (SRM^®s) were distributed to participants, who then returned the metabolomic data to us. We then implemented chemometric methods to analyze the data together to estimate the uncertainties in the current measurement techniques. The participants identified similar patterns in the profiles that distinguished the seven samples. Even when the number of spectral features is substantially different between platforms, a collective analysis still shows significant overlap that allows reliable comparison between participants. Our results show that a urine suite such as that used in this ILS could be employed for testing and harmonization among different platforms. A limited quantity of test materials will be made available for researchers who are willing to repeat the protocols presented here and contribute their data.

Effects of oral monosodium glutamate administration on serum metabolomics of suckling piglets

This study was conducted to determine the effects of oral administration with glutamate on metabolism of suckling piglets based on ¹ H-Nuclear magnetic resonance (¹ H NMR) spectroscopy through the level of metabolism. Forty-eight healthy [(Yorkshire × Landrace) × Duroc] piglets born on the same day with a similar birth bodyweight (1.55 ± 0.20 kg) were obtained from six sows (8 piglets per sow). The piglets from each sow were randomly assigned into four treatments (2 piglets per treatment). The piglets were given 0.09 g/kg body weight (BW) of sodium chloride (CN group), 0.03 g/kg BW monosodium glutamate (LMG group), 0.25 g/kg BW monosodium glutamate (MMG group) and 0.50 g/kg BW monosodium glutamate (HMG group) twice a day respectively. An ¹ H NMR-based metabolomics' study found that the addition of monosodium glutamate (MSG) significantly reduced serum citrate content in 7-day-old piglets, while HMG significantly increased serum trimethylamine content and significantly reduced unsaturated fat content in 7-day-old piglets (p < .05). The content of glutamine, trimethylamine, albumin, choline and urea nitrogen was significantly increased and the creatinine content decreased significantly in the 21-day-old HMG (p < .05). Analysis of serum hormones revealed that glucagon-like peptide-1 (GLP-1) content in the 21-day-old HMG was highest (p < .05). The cholecystokinin (CCK) content in the HMG of 7-day-old piglets was lower than that in the LMG (p < .05), and the CCK content in the serum of the 21-day-old MMG was highest (p < .05). The serum leptin levels in the 21-day-old HMG were the lowest (p < .05). The serum insulin content in the 7-day-old MMG was highest (p < .05). This study suggests that MSG plays an important role in the metabolism of sugar, fat and protein (amino acids). These results provide a theoretical basis for designing piglet feed formulations.

Amino and organic acid analysis: Essential tools in the diagnosis of inborn errors of metabolism

Inborn errors of metabolism (IEMs) are a large class of genetic disorders that result from defects in enzymes involved in energy production and metabolism of nutrients. For every metabolic pathway, there are defects that can occur and potentially result in an IEM. While some defects can go undetected in one's lifetime, some have moderate to severe clinical consequences. In the latter case, the biochemical defect leads to accumulation of metabolites and byproducts that are toxic or interfere with normal biological function. Disorders of amino acid metabolism, organic acid metabolism and the urea cycle comprise a large portion of IEMs. Two essential tools required for the diagnosis of these categories of disorders are amino acid and organic acid profiling. Most all clinical laboratories offering metabolic testing perform amino acid analysis, while organic acid profiling is restricted to more specialized pediatric hospitals and reference laboratories. In this chapter, we will provide an overview of various methodologies employed for amino acid and organic acid profiling as well as specific examples to demonstrate how these techniques are applied in clinical laboratories for the diagnosis of IEMs.

Fluorescence photo-bleaching of urine and its applicability in oral cancer diagnosis

Photo-stability of urine is of crucial importance for the applicability of fluorescence spectroscopy of urine samples for diagnosis of cancer. We report the results of a detailed study on fluorescence photo-bleaching of human urine samples. We also present the results of a preliminary investigation on evaluation of the applicability of photo-bleaching characteristics of urine for discriminating patients with oral cancer from healthy volunteers. The time-lapse fluorescence induced by continuous shining of 405 nm radiation from a diode laser was recorded from the urine samples obtained from 18 patients with oral cancer as well as from 22 healthy volunteers with history of no known major illness in the past two months. The integrated fluorescence intensity (ΣI), calculated for each spectrum, was found to decrease with time till a point after which no further decrease was observed. Further, while significant differences were observed in the spectra of cancerous patients and healthy volunteers, these differences were found to be varying with time till the intensities of the observed fluorescence spectra corresponding to the two categories of urine samples became stable. The curve, generated by plotting ΣI vs. time, was found to be best fitted (R² > 0.95) with a double-exponential decay function. The photo-bleaching constants, obtained from curve-fitting, were found to have statistically significant differences corresponding to the urine samples of cancerous patients and healthy volunteers. A classification algorithm developed based on nearest-mean classifier (NMC) and applied on the photo-bleaching constants in leave-one-subject-out cross-validation mode was found to provide a sensitivity and specificity of up to ∼ 86% in discriminating the two categories of urine samples.

Current Progress of Lipid Analysis in Metabolic Diseases by Mass Spectrometry Methods

BACKGROUND

Obesity, insulin resistance, diabetes, and metabolic syndrome are associated with lipid alterations, and they affect the risk of long-term cardiovascular disease. A reliable analytical instrument to detect changes in the composition or structures of lipids and the tools allowing to connect changes in a specific group of lipids with a specific disease and its progress, is constantly lacking. Lipidomics is a new field of medicine based on the research and identification of lipids and lipid metabolites present in human organism. The primary aim of lipidomics is to search for new biomarkers of different diseases, mainly civilization diseases.

OBJECTIVE

We aimed to review studies reporting the application of mass spectrometry for lipid analysis in metabolic diseases.

METHOD

Following an extensive search of peer-reviewed articles on the mass spectrometry analysis of lipids the literature has been discussed in this review article.

RESULTS

The lipid group contains around 1.7 million species; they are totally different, in terms of the length of aliphatic chain, amount of rings, additional functional groups. Some of them are so complex that their complex analyses are a challenge for analysts. Their qualitative and quantitative analysis of is based mainly on mass spectrometry.

CONCLUSION

Mass spectrometry techniques are excellent tools for lipid profiling in complex biological samples and the combination with multivariate statistical analysis enables the identification of potential diagnostic biomarkers.

A Metabolomic Study on the Intervention of Traditional Chinese Medicine Qushi Huayu Decoction on Rat Model of Fatty Liver Induced by High-Fat Diet

Qushi Huayu Decoction (QHD), an important clinically proved herbal formula, has been reported to be effective in treating fatty liver induced by high-fat diet in rats. However, the mechanism of action has not been clarified at the metabolic level. In this study, a urinary metabolomic method based on gas chromatography-mass spectrometry (GC-MS) coupled with pattern recognition analysis was performed in three groups (control, model, and QHD group), to explore the effect of QHD on fatty liver and its mechanism of action. There was obvious separation between the model group and control group, and the QHD group showed a tendency of recovering to the control group in metabolic profiles. Twelve candidate biomarkers were identified and used to explore the possible mechanism. Then, a pathway analysis was performed using MetaboAnalyst 3.0 to illustrate the pathways of therapeutic action of QHD. QHD reversed the urinary metabolite abnormalities (tryptophan, uridine, and phenylalanine, etc.). Fatty liver might be prevented by QHD through regulating the dysfunctions of phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism, and tryptophan metabolism. This work demonstrated that metabolomics might be helpful for understanding the mechanism of action of traditional Chinese medicine for future clinical evaluation.

Exploratory GC/MS-Based Metabolomics of Body Fluids

GC/MS-based metabolomics is a powerful tool for metabolic phenotyping and biomarker discovery from body biofluids. In this chapter, we describe an untargeted metabolomic approach for plasma/serum and fecal water sample profiling. It describes a multistep procedure, from sample preparation, oximation/silylation derivatization, and data acquisition using GC/QToF to data processing consisting in data extraction and identification of metabolites.

Influence of the sebaceous gland density on the stratum corneum lipidome

The skin surface lipids (SSL) result from the blending of sebaceous and epidermal lipids, which derive from the sebaceous gland (SG) secretion and the permeability barrier of the stratum corneum (SC), respectively. In humans, the composition of the SSL is distinctive of the anatomical distribution of the SG. Thus, the abundance of sebum biomarkers is consistent with the density of the SG. Limited evidence on the influence that the SG exerts on the SC lipidome is available. We explored the differential amounts of sebaceous and epidermal lipids in areas at different SG density with lipidomics approaches. SC was sampled with adhesive patches from forearm, chest, and forehead of 10 healthy adults (8F, 2M) after mechanical removal of sebum with absorbing paper. Lipid extracts of SC were analysed by HPLC/(-)ESI-TOF-MS. In the untargeted approach, the naïve molecular features extraction algorithm was used to extract meaningful entities. Aligned and normalized data were evaluated by univariate and multivariate statistics. Quantitative analysis of free fatty acids (FFA) and cholesterol sulfate (CHS) was performed by targeted HPLC/(-)ESI-TOF-MS, whereas cholesterol and squalene were quantified by GC-MS. Untargeted approaches demonstrated that the relative abundance of numerous lipid species was distinctive of SC depending upon the different SG density. The discriminating species included FFA, CHS, and ceramides. Targeted analyses confirmed that sebaceous FFA and epidermal FFA were increased and decreased, respectively, in areas at high SG density. CHS and squalene, which are biomarkers of epidermal and sebaceous lipid matrices, respectively, were both significantly higher in areas at elevated SG density. Overall, results indicated that the SG secretion intervenes in shaping the lipid composition of the epidermal permeability barrier.

GC/MS-based urine metabolomics analysis of renal allograft recipients with acute rejection

BACKGROUND

Acute renal allograft rejection is a common complication after renal transplantation that often leads to chronic rejection and ultimate graft loss. While renal allograft biopsy remains the gold standard for diagnosis of acute rejection, the possibility of biopsy-associated complications cannot be overlooked. The development of noninvasive methods for accurate detection of acute renal allograft rejection is thus of significant clinical importance.

METHODS

Gas chromatography-mass spectrometry (GC/MS) was employed for analysis of urine metabolites in 15 renal allograft recipients with acute rejection and 15 stable renal transplant recipients. Partial least squares (PLS) regression and leave-one-out analyses were performed to ascertain whether the metabolites identified could be exploited to distinguish acute rejection from stable groups as well as their sensitivity and specificity.

RESULTS

Overall, 14 metabolites were significantly altered in the acute rejection group (11 and 3 metabolites displayed higher and lower levels, respectively) relative to the stable transplant group. Data from PLS and leave-one-out analyses revealed that the differential metabolites identified not only distinguished acute rejection from stable transplant recipients but also showed high sensitivity and specificity for diagnosis of renal allograft recipients with acute rejection.

CONCLUSION

Urine metabolites identified with GC/MS can effectively distinguish acute rejection from stable transplant recipients, supporting the potential utility of metabolome analysis in non-invasive diagnosis of acute rejection.

Preparation of Drosophila Larval Samples for Gas Chromatography-Mass Spectrometry (GC-MS)-based Metabolomics

Recent advances in the field of metabolomics have established the fruit fly Drosophila melanogaster as a powerful genetic model for studying animal metabolism. By combining the vast array of Drosophila genetic tools with the ability to survey large swaths of intermediary metabolism, a metabolomics approach can reveal complex interactions between diet, genotype, life-history events, and environmental cues. In addition, metabolomics studies can discover novel enzymatic mechanisms and uncover previously unknown connections between seemingly disparate metabolic pathways. In order to facilitate more widespread use of this technology among the Drosophila community, here we provide a detailed protocol that describes how to prepare Drosophila larval samples for gas chromatography-mass spectrometry (GC-MS)-based metabolomic analysis. Our protocol includes descriptions of larval sample collection, metabolite extraction, chemical derivatization, and GC-MS analysis. Successful completion of this protocol will allow users to measure the relative abundance of small polar metabolites, including amino acids, sugars, and organic acids involved in glycolysis and the TCA cycles.

Fast and accurate quantitative organic acid analysis with LC-QTOF/MS facilitates screening of patients for inborn errors of metabolism

Since organic acid analysis in urine with gaschromatography-mass spectrometry (GC-MS) is a time-consuming technique, we developed a new liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) method to replace the classical analysis for diagnosis of inborn errors of metabolism (IEM). Sample preparation is simple and experimental time short. Targeted mass extraction and automatic calculation of z-scores generated profiles characteristic for the IEMs in our panel consisting of 71 biomarkers for defects in amino acids, neurotransmitters, fatty acids, purine, and pyrimidine metabolism as well as other disorders. In addition, four medication-related metabolites were included in the panel. The method was validated to meet Dutch NEN-EN-ISO 15189 standards. Cross validation of 24 organic acids from 28 urine samples of the ERNDIM scheme showed superiority of the UPLC-QTOF/MS method over the GC-MS method. We applied our method to 99 patient urine samples with 32 different IEMs, and 88 control samples. All IEMs were unambiguously established/diagnosed using this new QTOF method by evaluation of the panel of 71 biomarkers. In conclusion, we present a LC-QTOF/MS method for fast and accurate quantitative organic acid analysis which facilitates screening of patients for IEMs. Extension of the panel of metabolites is easy which makes this application a promising technique in metabolic diagnostics/laboratories.