GC-MS with ethyl chloroformate derivatization for comprehensive analysis of metabolites in serum and its application to human uremia

Microbiome metabolite quantification methods enabling insights into human health and disease

Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.

Determination of Gaseous and Particulate Secondary Amines in the Atmosphere Using Gas Chromatography Coupled with Electron Capture Detection

The aim of this study was to develop and optimize methods for the determination of gaseous and particulate (PM2.5) secondary amines (SAs) in the atmosphere using gas chromatography coupled with electron capture detection (GC-ECD) following chemical derivatization. The methods employed the liquid–liquid extraction (LLE) of pentafluorobenzenesulfonyl derivatives of the SAs before analytical samples were injected into GC-ECD. The optimized methods were applied to the determination of SAs in gaseous and particulate samples at two sites (urban and rural areas) from June to September in 2021. Gaseous samples were collected into an SPE cartridge containing a mixture of silica gel and sulfamic acid at a flow rate of 2 L·min−1 for 48 h. Particulate samples were collected onto 47 mm filters by a cyclone sampler at a flow rate of 16.7 L·min−1 for 48 h. The linearity of calibration curves, accuracy, and precision of the methods were satisfactory. In most of the field samples, dimethylamine (DMA), methylethylamine (MEA), diethylamine (DEA), and dipropylamine (DPA) were found to be the most frequently encountered compounds at the sampling sites.

Serum metabolomic profiling for patients with adenocarcinoma of the esophagogastric junction

INTRODUCTION

The incidence of adenocarcinoma in the esophagogastric junction (AEG) has increased in the recent years. AEG is reported to have a worse prognosis compared with tumor confined to the stomach (non-AEG). Although the metabolic changes of non-AEG have been investigated in extensive studies, little effort focused on the metabolic profiling of AEG serum.

OBJECTIVES

Here we report an untargeted gas chromatography-mass spectrometry (GC-MS) method to explore the abnormal metabolism underlying AEG.

METHODS

GC-MS-based untargeted metabolomics approach combined with multivariate statistical analyses were used to study the metabolic profiling of serum samples from AEG patients (n = 70), non-AEG patients (n = 70) and health controls (n = 71).

RESULTS

A novel serum metabolic profiling of 18 metabolites from patients of AEG and non-AEG was indicated, in comparison with health controls. Moreover, AEG and non-AEG were also well-classified with 9 distinguishing metabolites including hypoxanthine, alanine, proline, pyroglutamate, glycine, lactate, succinic acid, glutamate and kynurenine, which produced a discriminatory model with an area under the Receiver Operating Characteristic (ROC) curve of 0.852, suggesting a distinct metabolic signature of AEG. Importantly, lactate and glutamate disclosed outcome-prediction values by multivariate cox-proportional hazard model and Kaplan-Meier method based on follow-up information for 2-5 years.

CONCLUSION

This is the first metabolomics study to identify serum metabolic signature of AEG. The distinguishing metabolites show a promising application on clinical diagnose and outcome prediction, and allow us to unveil several key metabolic variations coexisting in AEG, which may aid to understand the underlying metabolic mechanisms.

Integrated Omic Analyses Identify Pathways and Transcriptomic Regulators Associated With Chemical Alterations of In Vitro Neural Network Formation

Development of in vitro new approach methodologies has been driven by the need for developmental neurotoxicity (DNT) hazard data on thousands of chemicals. The network formation assay characterizes DNT hazard based on changes in network formation but provides no mechanistic information. This study investigated nervous system signaling pathways and upstream physiological regulators underlying chemically induced neural network dysfunction. Rat primary cortical neural networks grown on microelectrode arrays were exposed for 12 days in vitro to cytosine arabinoside, 5-fluorouracil, domoic acid, cypermethrin, deltamethrin, or haloperidol as these exposures altered network formation in previous studies. RNA-seq from cells and gas chromatography/mass spectrometry analysis of media extracts collected on days in vitro 12 provided gene expression and metabolomic identification, respectively. The integration of differentially expressed genes and metabolites for each neurotoxicant was analyzed using ingenuity pathway analysis. All 6 compounds altered gene expression that linked to developmental disorders and neurological diseases. Other enriched canonical pathways overlapped among compounds of the same class; eg, genes and metabolites altered by both cytosine arabinoside and 5-fluorouracil exposures are enriched in axonal guidance pathways. Integrated analysis of upstream regulators was heterogeneous across compounds, but identified several transcriptomic regulators including CREB1, SOX2, NOTCH1, and PRODH. These results demonstrate that changes in network formation are accompanied by transcriptomic and metabolomic changes and that different classes of compounds produce differing responses. This approach can enhance information obtained from new approach methodologies and contribute to the identification and development of adverse outcome pathways associated with DNT.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

Simultaneous quantification of eleven short-chain fatty acids by derivatization and solid phase microextraction - Gas chromatography tandem mass spectrometry

As metabolites of the gut microbiome, short-chain fatty acids (SCFAs) played an important role in the diagnosis of the metabolic diseases. Because of the high polarity, high volatility and complex matrix of biological samples, the highly sensitive, selective and accurate method to determine SCFAs remains a major challenge. Herein, a new method for simultaneous quantification of eleven SCFAs by derivatization combined with solid phase microextraction (SPME) and gas chromatography tandem mass spectrometry (GC-MS/MS) was developed. Isobutyl chloroformate coupled with isobutanol was used as the reaction reagent to derivatize SCFAs. The method validation data showed a satisfactory linearity with the linear regression coefficients (R) ranging from 0.9964 to 0.9996. The limit of detection (LOD) of all SCFAs ranges from 0.01 ng·mL^-1 to 0.72 ng·mL^-1 and the limit of quantification (LOQ) ranges from 0.04 ng·mL^-1 to 2.41 ng·mL^-1. The intra-day and inter-day precision (RSDs) ranged from 0.65% to 8.92% and 1.62% to 15.61%, respectively. The recovery ranged from 88.10% to 108.71%. Finally, the developed method was successfully used to determine SCFAs in mice fecal sample, and ten of the SCFAs were found in feces of mice, including formic acid.

Gas chromatography/mass spectrometry analysis of organic acid profiles in human serum: A protocol of direct ultrasound-assisted derivatization

RATIONALE

Low-molecular-weight organic acids that generally contain one to three carboxyl groups are involved in many important biological processes; therefore, it is important to develop a quantitative method for analyzing organic acids in serum in order to allow an evaluation of metabolic changes. In this study, we evaluated a protocol for detecting 26 organic acids in serum based on ultrasound-assisted derivatization by gas chromatography/mass spectrometry (GC/MS).

METHODS

Serum samples were prepared using ultrasound-assisted silane derivatization before GC/MS analysis to quantify concentrations of organic acids. Additionally, we investigated the variables affecting derivatization yields, including the extraction solvent, derivatization reagents, and derivatization conditions (reaction temperature, duration, and sonication parameters). The protocol was ultimately applied to detect organic acid profiles related to obesity.

RESULTS

We used acetone as the extraction solvent and determined suitable derivatization conditions, as follows: BSTFA + 1% TMCS, 50°C, 10 min, and 100% ultrasound power. The protocol showed satisfactory linearity (r = 0.9958-0.9996), a low limit of detection (0.04-0.42 μmol/L), good reproducibility (coefficient of variation (CV) %: 0.32-13.76%), acceptable accuracy (recovery: 82.97-114.96%), and good stability within 5 days (CV%: 1.35-12.01% at room temperature, 1.24-14.09% at 4°C, and 1.01-11.67% at -20°C). Moreover, the protocol was successfully applied to obtain the organic acid profiles from obese and healthy control subjects.

CONCLUSIONS

We identified and validated a protocol for ultrasound-assisted derivatization prior to GC/MS analysis for detecting 26 kinds of organic acids in serum. The results suggest the efficacy of this protocol for clinical applications to determine metabolic changes related to fluctuations in organic acid profiles.

Prebiotic characteristics of arabinogalactans during in vitro fermentation through multi-omics analysis

BACKGROUND AND OBJECTIVES

Dietary fibers have beneficial effects on human health through the interaction with gut microbiota. Larch wood arabinogalactan (LA-AG) is one kind of complex soluble dietary fibers that may be utilized by human gut microbiota.

METHODS AND RESULTS

In this study, the LA-AG degradation by gut microbiota were characterized by investigating the change of LA-AG, microbiota composition, and the production of short-chain fatty acids (SCFAs), lactic acid, succinic acid, as well as volatile organic metabolites. During the fermentation, pH decreased continuously, along with the organic acids (especially acetic acid and lactic acid) accumulating. LA-AG was degraded by gut microbiota then some beneficial metabolites were produced. In addition, LA-AG inhibited the proliferation of some gut microbiota (Unclassified_Enterobacteriaceae and Citrobacter) and the accumulation of some metabolites (Sulfide and indole) released by gut microbiota.

CONCLUSION

LA-AG was partly fermentable fibers with prebiotic potential for human gut health.

Mass Spectrometry-based Metabolomics in Translational Research

Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites. Metabolism is integrated with all biochemical reactions in biological systems; thus metabolite profiles provide collective information on biochemical processes induced by genetic or environmental perturbations. Transcriptomes or proteomes may not be functionally active and not always reflect phenotypic variations. The metabolome, however, consists of the biomolecules closest to the phenotype of living organisms, and is often called the molecular phenotype of biological systems. Thus, metabolome alterations can easily result in disease states, providing important clues to understand pathophysiological mechanisms contributing to various biomedical symptoms. The metabolome and metabolomics have been emphasized in translational research related to biomarker discovery, drug target discovery, drug responses, and disease mechanisms. This review describes the basic concepts, workflows, and applications of mass spectrometry-based metabolomics in translational research.

Determination of atmospheric amines at Seoul, South Korea via gas chromatography/tandem mass spectrometry

Due to their important roles in salt-producing acid-base reactions, new particle formation (NPF), and as precursors in secondary organic aerosol (SOA) producing reactions, the atmospheric concentrations of particulate volatile amines (dimethylamine (DMA), ethylamine, diethylamine (DEA), propylamine, and butylamine) at Seoul were analyzed and evaluated. To quantify the presence of volatile amines in particulate matter with aerodynamic diameters less than or equal to a nominal 2.5 μm (PM_2.5), an efficient and rapid analytical method based on in-matrix ethyl chloroformate (ECF) derivatization followed by headspace solid-phase microextraction (HS-SPME) was developed and validated using gas chromatography coupled with tandem mass spectrometry (GC-MS/MS) in the multiple reaction monitoring (MRM) mode. The annual mean concentration of the total 5 target amines was 5.56±2.76 ng/m³ and the seasonal difference was small. The concentrations of particulate amines measured in this study were lower than those observed in Zongludak, Turkey, Nanjing, China, and Jeju, Korea but slightly higher than that reported in Kobe, Japan. The concentrations of the nitrosamines (nitrosodimethylamine (NDMA) and nitrosodiethylamine (NDEA)), and of the nitramines (dimethylnitramine (DMN) and diethylnitramine (DEN)) measured along with those of the target amines were used in a simple linear regression analysis. It indicates the contribution of DMA to the formation of NDMA in all seasons (except the fall) and DEA to the formation of NDEA in the summer, while DMA and DEA did not significantly contribute to the formation of nitramines.

Non-separative method based on a single quadrupole mass spectrometer for the semi-quantitative determination of amino acids in saliva samples. A preliminary study

Amino acids have been of great interest in clinical studies since variation in their concentration may provide information about different disorders. For the first time, a non-separative method based on single quadrupole mass spectrometry (qMS) for the simultaneous semiquantitative determination of sixteen amino acids in saliva samples has been developed. The method includes derivatisation of amino acids with ethyl chloroformate-pyridine-ethanol to obtain volatile products, liquid-liquid extraction (LLE) and further analysis using a programmed temperature vaporizer (PTV) coupled to qMS. This method could be applied to the analysis of a great number of saliva samples, limiting the use of separative methods only when abnormal concentrations of amino acids were found, reducing analysis time and cost. The results obtained in the determination of amino acids using the non-separative method were compared to those obtained when a separative method based on gas chromatography (GC) was used, providing values of average relative predictive error (E %) ranging between 2 and 48%. Repeatability and reproducibility were tested, obtaining relative standard deviation (RSD) values equal to or lower than 11% and 16%, respectively. Detection limits were in the range of 0.076-8.747 mg L^-1 for the non-separative method.

A sensitive method for the quantification of short-chain fatty acids by benzyl chloroformate derivatization combined with GC-MS

Short-chain fatty acids (SCFAs) were identified as critical markers in the diagnosis of chronic and metabolic diseases, but a sensitive and stable method to determine SCFAs in feces is a challenge for analysts due to the high volatility.

Droplet bubbling evaporatively cools a blowfly

Terrestrial animals often use evaporative cooling to lower body temperature. Evaporation can occur from humid body surfaces or from fluids interfaced to the environment through a number of different mechanisms, such as sweating or panting. In Diptera, some flies move tidally a droplet of fluid out and then back in the buccopharyngeal cavity for a repeated number of cycles before eventually ingesting it. This is referred to as the bubbling behaviour. The droplet fluid consists of a mix of liquids from the ingested food, enzymes from the salivary glands, and antimicrobials, associated to the crop organ system, with evidence pointing to a role in liquid meal dehydration. Herein, we demonstrate that the bubbling behaviour also serves as an effective thermoregulatory mechanism to lower body temperature by means of evaporative cooling. In the blowfly, Chrysomya megacephala, infrared imaging revealed that as the droplet is extruded, evaporation lowers the fluid´s temperature, which, upon its re-ingestion, lowers the blowfly's body temperature. This effect is most prominent at the cephalic region, less in the thorax, and then in the abdomen. Bubbling frequency increases with ambient temperature, while its cooling efficiency decreases at high air humidities. Heat transfer calculations show that droplet cooling depends on a special heat-exchange dynamic, which result in the exponential activation of the cooling effect.

GC-MS/MS detects potential pregabalin abuse in susceptible subjects' hair

Pregabalin, a GABA analogue, binds to the alpha 2 delta subunit of voltage-dependent calcium channels. It is recognised as efficacious in pathologies such as epilepsy, neuropathic pain, and anxiety disorders. Since pregabalin prescriptions have increased worldwide, reports of its abuse have been accumulating, mainly in patients with opioid abuse disorders. The present study investigated potential pregabalin abuse by means of hair analysis, a matrix that provides valuable retrospective information. Half of the pool of 280 susceptible patients had been occasional drug users and were being monitored for driving licence renewals. The other 140 patients had a history of opiate dependency and were monitored to assess compliance with methadone therapy. In view of determining pregabalin in hair samples, it was extracted in methanol, successfully derivatised to give the ethyl chloroformate derivative, and finally pregabalin was analysed by gas chromatography-tandem mass spectrometry. Selectivity, linearity, limit of detection, limit of quantification, recovery, intra- and inter-day precision, and accuracy of the quantification procedure were appraised. Pregabalin limits of detection and quantification were 30 pg/mg and 50 pg/mg, respectively. We found 10.7% of hair samples from methadone patients and 4.29% from occasional drug users were positive to pregabalin without medical prescription. The mean pregabalin concentration in hair was higher than in consumers with medical indications (1.45 ng/mg vs 0.74 ng/mg). These results suggest that pregabalin possesses a significant abuse potential particularly among individuals attending opiate dependence services and that pregabalin abuse is a serious emerging issue, which should be carefully monitored.

High Throughput and Quantitative Measurement of Microbial Metabolome by Gas Chromatography/Mass Spectrometry Using Automated Alkyl Chloroformate Derivatization

The ability to identify and quantify small molecule metabolites derived from gut microbial-mammalian cometabolism is essential for the understanding of the distinct metabolic functions of the microbiome. To date, analytical protocols that quantitatively measure a complete panel of microbial metabolites in biological samples have not been established but are urgently needed by the microbiome research community. Here, we report an automated high-throughput quantitative method using a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) platform to simultaneously measure over one hundred microbial metabolites in human serum, urine, feces, and Escherichia coli cell samples within 15 min per sample. A reference library was developed consisting of 145 methyl and ethyl chloroformate (MCF and ECF) derivatized compounds with their mass spectral and retention index information for metabolite identification. These compounds encompass different chemical classes including fatty acids, amino acids, carboxylic acids, hydroxylic acids, and phenolic acids as well as benzoyl and phenyl derivatives, indoles, etc., that are involved in a number of important metabolic pathways. Within an optimized range of concentrations and sample volumes, most derivatives of both reference standards and endogenous metabolites in biological samples exhibited satisfactory linearity (R² > 0.99), good intrabatch reproducibility, and acceptable stability within 6 days (RSD < 20%). This method was further validated by examination of the analytical variability of 76 paired human serum, urine, and fecal samples as well as quality control samples. Our method involved using high-throughput sample preparation, measurement with automated derivatization, and rapid GC/TOFMS analysis. Both techniques are well suited for microbiome metabolomics studies.

Gene and protein expressions and metabolomics exhibit activated redox signaling and wnt/β-catenin pathway are associated with metabolite dysfunction in patients with chronic kidney disease

Changes in plasma concentration of small organic metabolites could be due to their altered production or urinary excretion and changes in their urine concentration may be due to the changes in their filtered load, tubular reabsorption, and/or altered urine volume. Therefore, these factors should be considered in interpretation of the changes observed in plasma or urine of the target metabolite(s). Fasting plasma and urine samples from 180 CKD patients and 120 age-matched healthy controls were determined by UPLC-HDMS-metabolomics and quantitative real-time RT-PCR techniques. Compared with healthy controls, patients with CKD showed activation of NF-κB and up-regulation of pro-inflammatory and pro-oxidant mRNA and protein expression as well as down-regulation of Nrf2-associated anti-oxidant gene mRNA and protein expression, accompanied by activated canonical Wnt/β-catenin signaling. 124 plasma and 128 urine metabolites were identified and 40 metabolites were significantly altered in both plasma and urine. Plasma concentration and urine excretion of 25 metabolites were distinctly different between CKD and controls. They were related to amino acid, methylamine, purine and lipid metabolisms. Logistic regression identified four plasma and five urine metabolites. Parts of them were good correlated with eGFR or serum creatinine. 5-Methoxytryptophan and homocystine and citrulline were good correlated with both eGFR and creatinine. Clinical factors were incorporated to establish predictive models. The enhanced metabolite model showed 5-methoxytryptophan, homocystine and citrulline have satisfactory accuracy, sensitivity and specificity for predictive CKD. The dysregulation of CKD was related to amino acid, methylamine, purine and lipid metabolisms. 5-methoxytryptophan, homocystine and citrulline could be considered as additional GFR-associated biomarker candidates and for indicating advanced renal injury. CKD caused dysregulation of the plasma and urine metabolome, activation of inflammatory/oxidative pathway and Wnt/β-catenin signaling and suppression of antioxidant pathway.

Metabolomics, Nutrition, and Potential Biomarkers of Food Quality, Intake, and Health Status

Diet, dietary patterns, and other environmental factors such as exposure to toxins are playing an important role in the prevention/development of many diseases, like obesity, type 2 diabetes, and consequently on the health status of individuals. A major challenge nowadays is to identify novel biomarkers to detect as early as possible metabolic dysfunction and to predict evolution of health status in order to refine nutritional advices to specific population groups. Omics technologies such as genomics, transcriptomics, proteomics, and metabolomics coupled with statistical and bioinformatics tools have already shown great potential in this research field even if so far only few biomarkers have been validated. For the past two decades, important analytical techniques have been developed to detect as many metabolites as possible in human biofluids such as urine, blood, and saliva. In the field of food science and nutrition, many studies have been carried out for food authenticity, quality, and safety, as well as for food processing. Furthermore, metabolomic investigations have been carried out to discover new early biomarkers of metabolic dysfunction and predictive biomarkers of developing pathologies (obesity, metabolic syndrome, type-2 diabetes, etc.). Great emphasis is also placed in the development of methodologies to identify and validate biomarkers of nutrients exposure.

Applying gas chromatography to monitor extracellular free amino acids content in cultivation medium during lactic acid fermentation

The aim of this work was the adaptation of a Gas Chromatographic-Flame Ionization Detector (GC-FID) method for detection and quantification of extracellular free amino acids in demineralized water, De Mann Rogosa Sharpe (MRS) medium and corn grits (CG) withdrawn during lactic acid fermentation. In order to analyze free amino acids by the GC-FID method it was necessary to convert free amino acids to volatile compounds. This was accomplished by derivatization of free amino acids with ethylchlor formate in aqueous medium followed by extraction of volatile free amino acid esters with chloroform. It was proven that the combination of derivatization and extraction procedure with developed GC-FID method gave accurate, reproducible and sensitive analytical results. Quantification of 15 (Ala, Gly, Val, Leu, Ile, Phe, Tyr, Trp, Asn, Met, Pro, Lys, His, Asp and Glu) out of 20 ethoxycarbonyl-ethyl esters of free amino acids in demineralized water and MRS medium was achieved by established methods. In corn grits medium all of the above mentioned 15 amino acids, except His, were quantified with this GC-FID method. The established method was efficiently verified in monitoring of extracellular free amino acid concentration during lactic acid production with Lactobacillus rhamnosus DSM 20021T in MRS medium and Lactobacillus amylovorus DSM 20531T in corn grits medium.

PCI-GC-MS-MS approach for identification of non-amino organic acid and amino acid profiles

Alkyl chloroformate have been wildly used for the fast derivatization of metabolites with amino and/or carboxyl groups, coupling of powerful separation and detection systems, such as GC-MS, which allows the comprehensive analysis of non-amino organic acids and amino acids. The reagents involving n-alkyl chloroformate and n-alcohol are generally employed for providing symmetric labeling terminal alkyl chain with the same length. Here, we developed an asymmetric labeling strategy and positive chemical ionization gas chromatography-tandem mass spectrometry (PCI-GC-MS-MS) approach for determination of non-amino organic acids and amino acids, as well as the short chain fatty acids. Carboxylic and amino groups could be selectively labelled by propyl and ethyl groups, respectively. The specific neutral loss of C₃H₈O (60Da), C₃H₅O₂ (74Da) and C₄H₈O₂ (88Da) were useful in the selective identification for qualitative analysis of organic acids and amino acid derivatives. PCI-GC-MS-MS using multiple reaction monitoring (MRM) was applied for semi-quantification of typical non-amino organic acids and amino acids. This method exhibited a wide range of linear range, good regression coefficient (R²) and repeatability. The relative standard deviation (RSD) of targeted metabolites showed excellent intra- and inter-day precision (<5%). Our method provided a qualitative and semi-quantitative PCI-GC-MS-MS, coupled with alkyl chloroformate derivatization.

A GC-MS urinary quantitative metabolomics analysis in depressed patients treated with TCM formula of Xiaoyaosan

Xiaoyaosan, one of the best-known traditional Chinese medicine prescriptions, has been widely used in China for the treatment of mental disorders such as depression. Although both clinical application and animal experiments indicate that Xiaoyaosan has an obvious antidepressant effect, the mechanism still remains unclarified, and there are few studies quantitatively measured the biomarkers of Xiaoyaosan treatment by metabolomics to determination. In this study, 25 depressed patients and 33 healthy volunteers were recruited. A GC-MS based metabolomics approach and the multivariate statistical methods were used for analyzing the urine metabolites of depressed patients before and after treatment compared with healthy controls. Then the biomakers through metabolomics determination were carried out the quantitative analysis. In total, 5 metabolites were identified as the potential diseased and therapeutic biomarkers of depression and Xiaoyaosan. Alanine, citrate and hippurate levels were significantly increased in the urine samples from depressed patients compared with healthy controls, while phenylalanie and tyrosine levels were significantly decreased. However, after Xiaoyaosan treatment for 6 weeks, phenylalanie and tyrosine levels were significantly increased (p<0.05) and alanine, citrate and hippurate levels significantly decreased (p<0.05). Xiaoyaosan has a good priority on the treatment of depression and the ability to adjust the neurotransmitters to obtain the best treated response and also could regulate the metabolism of amino acids and promote to produce energy meet the needs of the body.

Application of GC-MS coupled with chemometrics for scanning serum metabolic biomarkers from renal fibrosis rat

Renal interstitial fibrosis closely relates to chronic kidney disease and is regarded as the final common pathway in most cases of end-stage renal disease. Metabolomic biomarkers can facilitate early diagnosis and allow better understanding of the pathogenesis underlying renal fibrosis. Gas chromatography-mass spectrometry (GC/MS) is one of the most promising techniques for identification of metabolites. However, the existence of the background, baseline offset, and overlapping peaks makes accurate identification of the metabolites unachievable. In this study, GC/MS coupled with chemometric methods was successfully developed to accurately identify and seek metabolic biomarkers for rats with renal fibrosis. By using these methods, seventy-six metabolites from rat serum were accurately identified and five metabolites (i.e., urea, ornithine, citric acid, galactose, and cholesterol) may be useful as potential biomarkers for renal fibrosis.

Dynamic analysis of the endogenous metabolites in depressed patients treated with TCM formula Xiaoyaosan using urinary (1)H NMR-based metabolomics

ETHNOPHAMACOLOGICAL RELEVANCE

Xiaoyaosan (XYS), one of the best-known traditional Chinese medicine prescriptions with a long history of use, is composed of Bupleurum chinense DC., Paeonia lactiflora Pall., Poria cocos (Schw.) Wolf, Angelica sinensis (Oliv.) Diels, Zingiber officinale Rosc., Atractylodes macrocephala Koidz., Glycyrrhiza uralensis Fisch., and Mentha haplocalyx Briq. For centuries, XYS has been widely used in China for the treatment of mental disorders such as depression. However, the complicated mechanism underlying the antidepressant activity of XYS is not yet well-understood. This understanding is complicated by the sophisticated pathophysiology of depression and by the complexity of XYS, which has multiple constituents acting on different metabolic pathways. The variations of endogenous metabolites in depressed patients after administration of XYS may help elucidate the anti-depressant effect and mechanism of action of XYS. The aim of this study is to establish the metabolic profile of depressive disorder and to investigate the changes of endogenous metabolites in the depressed patients before and after the treatment of Xiaoyaosan using the dynamic analysis of urine metabolomics profiles based on (1)H NMR.

MATERIALS AND METHODS

Twenty-one depressed patients were recruited from the Traditional Chinese Medicine Department of the First Affiliated Hospital of Shanxi Medical University. Small endogenous metabolites present in urine samples were measured by nuclear magnetic resonance (NMR) and analyzed by multivariate statistical methods. The patients then received XYS treatment for six weeks, after which their Hamilton Depression Scale (HAMD) scores were significantly decreased compared with their baseline scores (p≤0.01). Eight components in urine specimens were identified that enabled discrimination between the pre- and post-XYS-treated samples.

RESULTS

Urinary of creatinine, taurine, 2-oxoglutarate and xanthurenic acid increased significantly after XYS treatment (p≤0.05), while the urinary levels of citrate, lactate, alanine and dimethylamine decreased significantly (p≤0.05) compared with pre-treatment urine samples. These statistically significant perturbations are involved in energy metabolism, gut microbes, tryptophan metabolism and taurine metabolism.

CONCLUSIONS

The symptoms of depression had been improved after 6 weeks׳ treatment of XYS according to evaluation of HAMD scores. The dynamic tendency of the 8 metabolites that changed significantly during the treatment of XYS is consistent with the improvement in symptoms of depression. These metabolites may be used as biomarkers for the diagnosis of depressive disorders or the evaluation of the antidepressant as well as the exploration of the mechanism of depression.

Improvement of lichenysin production in Bacillus licheniformis by replacement of native promoter of lichenysin biosynthesis operon and medium optimization

Lichenysin is a biodegradable surfactant with huge potential for recovering crude oil from the oil reservoir. The current production of lichenysin is made through fermentation from wild strain of Bacillus licheniformis, which is limited by low yield. The aim of this work was to improve lichenysin-producing capability of a wide strain B. licheniformis WX-02. Lichenysin produced from WX-02 was first extracted, purified, and identified. Through the substitution of the promoter of lichenysin biosynthesis operon, the mutants B. licheniformis WX02-P43lch, WX02-Pxyllch, and WX02-Psrflch were constructed with the constitutive promoter (P43), the xylose-inducible promoter (P xyl ), and the surfactin operon promoter (P srf ), respectively. A consistent change trend was observed between lichenysin production and lchAA gene transcription, confirming the strength of the promoters as an important factor for lichenysin synthesis. Among the three mutants, WX02-Psrflch produced the highest lichenysin yield. The production by the mutant WX02-Psrflch was further improved with the optimization of the major medium components including glucose, NH4NO3, and Na2HPO4/KH2PO4. Under 30 g/L glucose, 5 g/L NH4NO3, and 80 mM/60 mM Na2HPO4/KH2PO4, the strain WX02-Psrflch produced 2,149 mg/L lichenysin, a 16.8-fold improvement compared to that of wild strain WX-02.

Characterization and quantitative amino acids analysis of analgesic peptides in cinobufacini injection by size exclusion chromatography, matrix-assisted laser desorption/ionization time of flight mass spectrometry and gas chromatography mass spectrometry

Cinobufacini injection that comes from the water extract of Bufo bufo gargarizans Cantor skin is widely used for cancer treatment in China. Peptide is one of its major types of constituents, however the biological effects and content of this injection are little reported. In present study, the analgesic effect of peptides was determined and evaluated by in-vivo models. To characterize and quantitatively analyze these peptides, a reliable and efficient method combining size exclusion chromatography and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with amino acid analysis was developed. The peptides presented as a series of analogs with similar molecular weights mostly ranging from 2 to 8 kDa. The amino acid analysis by gas chromatography mass spectrometry (GC-MS) was developed to determine both free and combined amino acids (FAA and CAA) in cinobufacini injection. This method achieved good linearity (R(2) , 0.9909-0.9999) and low limit of detection and quantification. FAA and CAA samples were efficiently analyzed by modified Phenomenex EZ: faast procedure. For the sample analysis, the method showed good repeatability (relative standard deviation, RSD ≤ 10%). For most FAA and CAA the mean recoveries were >80% with RSD <10%. The GC-MS based method is useful for quality assurance of both FAA and CAA in cinobufacini injection.

Plasma metabolic profiling of normal and dysmenorrhea syndrome rats and the effects of Xiang-Fu-Si-Wu Decoction intervention

CONTEXT

Primary dysmenorrhea (PDM), a common, clinically heterogeneous endocrine disorder affecting young women, is associated with endocrinopathy and metabolic abnormalities. The Xiang-Fu-Si-Wu Decoction (XFSWD) is a traditional Chinese medicine preparation used to treat PDM.

OBJECTIVE

In the current study, a plasma metabonomics method based on the ultra-high-performance liquid chromatography-quantitative time-of-flight-mass spectrometry (UHPLC-Q-TOF-MS) system was employed to examine the mechanism of XFSWD action in PDM.

MATERIALS AND METHODS

Estradiol benzoate (0.01 g/kg/d) and oxytocin (5 mL/kg) were used to create the dysmenorrhea rat model. Based on the chromatographic data of plasma samples at different time-points following oral administration of XFSWD mixed in water (37.8 g crude herbs/kg) on day 7, partial least square (PLS) and discriminate analysis (DA) were applied to visualize group differentiation and marker selection.

RESULTS

Systemic changes occurring in PDM reflect alterations in not only uterus function but also whole-body metabolism. The XFSWD was effective as a therapeutic agent for PDM by reflect metabolic pathway. Prostaglandins and lysophospholipids were identified as two marker types for oxytocin-induced dysmenorrhea syndrome, including LysoPC(18:4), LysoPE(22:2/0:0), LysoPC(17:0), PGJ₂, 11-deoxy-11-methylene-PGD₂, 15-deoxy-δ-12,14-PGJ₂, LysoPC(20:3), etc. Specifically, the concentrations of prostaglandins compounds (PGJ₂, 11-deoxy-11-methylene-PGD₂, 15-deoxy-δ-12,14-PGJ₂) were increased while those of lysophospholipid compounds [lysoPC(18:4), LysoPE(22:2/0:0), LysoPC(17:0)] were decreased to a significant extent (p < 0.05) in dysmenorrheal rats. Upon treatment with the XFSWD at 12 h, the concentrations of lysophospholipids showed no significant differences (P > 0.05) between the model and normal groups. The lysophospholipid levels were restored. Lysophospholipids were the key factors in phospholipid metabolism. Thus, disruption of phospholipids metabolism appears critical for the development of dysmenorrhea. The XFSWD exerted its effects by interfering with the sphingolipid metabolic pathway.

DISCUSSION AND CONCLUSIONS

The metabonomics method presents a promising tool to treat PDM in animal models, and may be applicable for clinical treatment of the human disease in the future.

A targeted metabolomic protocol for short-chain fatty acids and branched-chain amino acids

Research in obesity and metabolic disorders that involve intestinal microbiota demands reliable methods for the precise measurement of the short-chain fatty acids (SCFAs) and branched-chain amino acids (BCAAs) concentration. Here, we report a rapid method of simultaneously determining SCFAs and BCAAs in biological samples using propyl chloroformate (PCF) derivatization followed by gas chromatography mass spectrometry (GC-MS) analysis. A one-step derivatization using 100 µL of PCF in a reaction system of water, propanol, and pyridine (v/v/v = 8:3:2) at pH 8 provided the optimal derivatization efficiency. The best extraction efficiency of the derivatized products was achieved by a two-step extraction with hexane. The method exhibited good derivatization efficiency and recovery for a wide range of concentrations with a low limit of detection for each compound. The relative standard deviations (RSDs) of all targeted compounds showed good intra- and inter-day (within 7 days) precision (< 10%), and good stability (< 20%) within 4 days at room temperature (23-25 °C), or 7 days when stored at -20 °C. We applied our method to measure SCFA and BCAA levels in fecal samples from rats administrated with different diet. Both univariate and multivariate statistics analysis of the concentrations of these target metabolites could differentiate three groups with ethanol intervention and different oils in diet. This method was also successfully employed to determine SCFA and BCAA in the feces, plasma and urine from normal humans, providing important baseline information of the concentrations of these metabolites. This novel metabolic profile study has great potential for translational research.

Rapid and simultaneous determination of twenty amino acids in complex biological and food samples by solid-phase microextraction and gas chromatography-mass spectrometry with the aid of experimental design after ethyl chloroformate derivatization

Amino acids play a vital role as intermediates in many important metabolic pathways such as the biosynthesis of nucleotides, vitamins and secondary metabolites. A sensitive and rapid analytical method has been proposed for the first time for the simultaneous determination of twenty amino acids using solid-phase microextraction (SPME). The protein samples were hydrolyzed by 6M HCl under microwave radiation for 120 min. Then the amino acids were derivatized by ethyl chloroformate (ECF) and the ethoxy carbonyl ethyl esters of amino acids formed were extracted using SPME by direct immersion. Finally the extracted analytes on the SPME fiber were desorbed at 260°C and analyzed by gas chromatography-mass spectrometer (GC-MS) in electron ionization mode. Factors which affect the SPME efficiency were screened by Plackett-Burmann design; most significant factors were optimized with response surface methodology. The optimum conditions for SPME are as follows: pH of 1.7, ionic strength of 733 mg, extraction time of 30 min and fiber of divinyl benzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS). The recovery of all the amino acids was found to be in the range of 89.17-100.98%. The limit of detection (LOD) of all derivatized amino acids in urine, hair and soybean was found to be in the range of 0.20-7.52 μg L(-1), 0.21-8.40 μg L(-1) and 0.18-5.62 μg L(-1), respectively. Finally, the proposed technique was successfully applied for the determination of amino acids in complex biological (hair, urine) and food samples (soybean). The method can find wide applications in the routine analysis of amino acids in any biological as well as food samples.

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography-mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography-MS (GC-MS) and ultraperformance liquid chromatography-MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control-based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

A novel GC-MS method in urinary estrogen analysis from postmenopausal women with osteoporosis

Estrogen metabolites play important roles in the development of female-related disorders and homeostasis of the bone. To improve detectability, a validated gas chromatography-mass spectrometry (GC-MS) method was conducted with two-phase extractive ethoxycarbonlyation (EOC) and subsequent pentafluoropropionyl (PFP) derivatization was introduced. The resulting samples were separated through a high-temperature MXT-1 column within an 8 min run and were detected in the selected ion monitoring (SIM) mode. The optimized analytical conditions led to good separation with a symmetric peak shape for 19 estrogens as their EOC-PFP derivatives. The limit of quantification (LOQ) was from 0.02 to ∼0.1 ng/ml for most estrogens analyzed, except for 2-hydroxyestriol (0.5 ng/ml). The devised method was found to be linear (r² > 0.995) in the range from the LOQ to 40 ng/ml, whereas the precision (% CV) and accuracy (% bias) ranged from 1.4 to 10.5% and from 91.4 to 108.5%, respectively. The good sensitivity and selectivity of this method even allowed quantification of the estrogen metabolites in urine samples obtained from the postmenopausal female patients with osteoporosis. The present technique can be useful for clinical diagnosis as well as to better understand the pathogenesis of estrogen-related disorders in low-level quantification.

Update of uremic toxin research by mass spectrometry

Mass spectrometry (MS) has been successfully applied for the identification and quantification of uremic toxins and uremia-associated modified proteins. This review focuses on the recent progress in the MS analysis of uremic toxins. Uremic toxins include low-molecular weight solutes, protein-bound low-molecular weight solutes, and middle molecules (peptides and proteins). Based on MS analysis of these uremic toxins, the pathogenesis of the uremic symptoms will be elucidated to prevent and manage the symptoms. Notably, protein-bound uremic toxins such as indoxyl sulfate, p-cresyl sulfate, and 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid have emerged as important targets of therapeutic removal. Hemodialysis even with a high-flux membrane cannot efficiently remove the protein-bound uremic toxins because of their high albumin-binding property. The accumulation of these protein-bound uremic toxins in the blood of dialysis patients might play an important role in the development of uremic complications such as cardiovascular disease. Indoxyl sulfate is the most promising protein-bound uremic toxin as a biomarker of progress in chronic kidney disease. Novel dialysis techniques or membranes should be developed to efficiently remove these protein-bound uremic toxins for the prevention and management of uremic complications.

Sarcosine as a marker in prostate cancer progression: a rapid and simple method for its quantification in human urine by solid-phase microextraction-gas chromatography-triple quadrupole mass spectrometry

Sarcosine is an amino acid derivative of N-methylglycine and is involved in the amino acid metabolism and methylation processes that are enriched during prostate cancer progression. It could also serve as a new target to be measured during therapeutic interventions and help in the identification of aggressive tumors for radical treatment. In this study, we present a new urine test that can help early diagnosis of prostate cancer. The method for the quantification of sarcosine in urine consists of a solid-phase microextraction (SPME) step followed by gas chromatography-triple quadrupole mass spectrometry analysis. We used a preliminary derivatization step with ethyl chloroformate/ethanol and the corresponding ester was then extracted by SPME in immersion mode. Several fibers were evaluated and the optimization of the parameters affecting the SPME process was carried out using an experimental design. The optimal values were 20 min extraction time, 10% NaCl, and 270°C using a divinylbenzene/Carboxen/polydimethylsiloxane fiber. The triple quadrupole analyzer acquired data in selected reaction monitoring mode, allowing us to obtain reconstructed chromatograms with well-defined chromatographic peaks. The accuracy and precision of this method were evaluated at concentrations of 70, 250, and 800 ng/ml and were found to be acceptable. Very satisfactory values (0.10 and 0.16 ng/ml, respectively) were also achieved for the limit of detection and the limit of quantification. The proposed protocol represents a rapid, simple, selective, and sensitive tool to quantify sarcosine in urine samples for prostate cancer diagnosis and for a screening test.

An ultrasonication-assisted extraction and derivatization protocol for GC/TOFMS-based metabolite profiling

Conventional chemical derivatization of metabolites in biological specimens is time-consuming, which limits the throughput and efficiency of metabolite profiling using a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) platform. We report an ultrasonication-assisted protocol which reduces the derivatization time from hours to about 30 min and significantly enhances the derivatization efficiency prior to a GC/TOFMS analysis. The protocol was evaluated using 40 compounds representing different classes of human metabolites, and demonstrated good analytical precision and accuracy. In comparison with the conventional method, the new protocol was able to increase the intensity of most of the identified peaks (71.0%) in the GC/TOFMS chromatograms of human serum samples. The detected compounds with increased intensity include most amino acids, keto-containing organic acids, carbonyl-containing carbohydrates, and unsaturated fatty acids. We applied this protocol in a metabolomic study of human serum samples obtained from 34 patients diagnosed with hypertension and 29 age- and gender-matched healthy subjects. Metabolite markers associated with hypertension, including glucosamine, D-sorbitol, 1-stearoylglycerol, and homocysteine, were identified and validated by statistical methods and use of reference standards. Our work highlights the potential of this novel approach for the large-scale metabolite profiling of samples generated from plant, animal, and clinical and epidemiological studies.

Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy

The study of biological systems in a holistic manner (systems biology) is increasingly being viewed as a necessity to provide qualitative and quantitative descriptions of the emergent properties of the complete system. Systems biology performs studies focussed on the complex interactions of system components; emphasising the whole system rather than the individual parts. Many perturbations to mammalian systems (diet, disease, drugs) are multi-factorial and the study of small parts of the system is insufficient to understand the complete phenotypic changes induced. Metabolomics is one functional level tool being employed to investigate the complex interactions of metabolites with other metabolites (metabolism) but also the regulatory role metabolites provide through interaction with genes, transcripts and proteins (e.g. allosteric regulation). Technological developments are the driving force behind advances in scientific knowledge. Recent advances in the two analytical platforms of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have driven forward the discipline of metabolomics. In this critical review, an introduction to metabolites, metabolomes, metabolomics and the role of MS and NMR spectroscopy will be provided. The applications of metabolomics in mammalian systems biology for the study of the health-disease continuum, drug efficacy and toxicity and dietary effects on mammalian health will be reviewed. The current limitations and future goals of metabolomics in systems biology will also be discussed (374 references).

Chemometrics in metabolomics--a review in human disease diagnosis

Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.

Metabolite analysis of human fecal water by gas chromatography/mass spectrometry with ethyl chloroformate derivatization

Fecal water is a complex mixture of various metabolites with a wide range of physicochemical properties and boiling points. The analytical method developed here provides a qualitative and quantitative gas chromatography/mass spectrometry (GC/MS) analysis, with high sensitivity and efficiency, coupled with derivatization of ethyl chloroformate in aqueous medium. The water/ethanol/pyridine ratio was optimized to 12:6:1, and a two-step derivatization with an initial pH regulation of 0.1M sodium bicarbonate was developed. The deionized water exhibited better extraction efficiency for fecal water compounds than did acidified and alkalized water. Furthermore, more amino acids were extracted from frozen fecal samples than from fresh samples based on multivariate statistical analysis and univariate statistical validation on GC/MS data. Method validation by 34 reference standards and fecal water samples showed a correlation coefficient higher than 0.99 for each of the standards, and the limit of detection (LOD) was from 10 to 500pg on-column for most of the standards. The analytical equipment exhibited excellent repeatability, with the relative standard deviation (RSD) lower than 4% for standards and lower than 7% for fecal water. The derivatization method also demonstrated good repeatability, with the RSD lower than 6.4% for standards (except 3,4-dihydroxyphenylacetic acid) and lower than 10% for fecal water (except dicarboxylic acids). The qualitative means by searching the electron impact (EI) mass spectral database, chemical ionization (CI) mass spectra validation, and reference standards comparison totally identified and structurally confirmed 73 compounds, and the fecal water compounds of healthy humans were also quantified. This protocol shows a promising application in metabolome analysis based on human fecal water samples.