Metabolite profiling of human plasma by different extraction methods through gas chromatography–mass spectrometry—An objective comparison

Association of lipid metabolism-related metabolites with overweight/obesity based on the FTO rs1421085

Globally, obesity is a severe health issue. A more precise and practical approach is required to enhance clinical care and drug development. The FTO (fat mass and obesity-associated) gene variant rs1421085 is strongly associated with an increased susceptibility to obesity in numerous populations; however, the precise mechanism behind this association concerning metabolomics is still not understood. This study aims to examine the association between metabolites and obesity-related anthropometric traits based on the variant FTO rs1421085. This study was based on a case-control design involving a total of 542 participants including overweight/obese cases and healthy controls. The blood samples were collected from all the participants. The isolated serum samples were subjected to untargeted metabolomics using GC-MS. The isolated DNA samples were genotyped for the FTO rs1421085 variant. Initially, a total of 42 metabolites were identified on GC-MS, which were subjected to further association analyses. The study observed a significant association of two metabolites, glycerol and 2,3-dihydroxypropyl stearate with FTO gene variant rs1421085 and obesity-related anthropometric traits including % BF, WHtR, WC, and HC. The CT genotype of FTO rs1421085 may greatly increase the risk of overweight/obesity by changing the lipid metabolism-related metabolites. Therefore, this study highlights the significance of biochemical networks in the progression of obesity in carriers of the FTO rs1421085 risk genotype.

Metabolomics Study of Serum Samples of β-YAC Transgenic Mice Treated with Tenofovir Disoproxil Fumarate

β-thalassemia is one of the most common monogenic disorders and a life-threatening health issue in children. A cost-effective and safe therapeutic approach to treat this disease is to reactivate the γ-globin gene for fetal hemoglobin (HbF) production that has been silenced during infancy. Hydroxyurea (HU) is the only FDA approved HbF inducer. However, its cytotoxicity and inability to respond significantly in all patients pose a need for an HbF inducer with better efficacy. The study describes the serum metabolic alteration in β-YAC transgenic mice treated with Tenofovir disoproxil fumarate (TDF) (n = 5), a newly identified HbF inducer, and compared to the mice groups treated with HU (n = 5) and untreated control (n = 5) using gas chromatography-mass spectrometry. Various univariate and multivariate statistical analyses were performed to identify discriminant metabolites that altered the biological pathways encompassing galactose metabolism, lactose degradation, and inositol. Furthermore, the decreased concentrations of L-fucose and geraniol in TDF-treated mice help in recovering towards normal, decreasing oxidative stress even much better than the HU-treated mice. The proposed study suggested that TDF can reduce the deficiency of blood required for β-thalassemia and can be used for the preclinical study at phase I/II for fetal hemoglobin production.

Optimization of a GC-MS method for the profiling of microbiota-dependent metabolites in blood samples: An application to type 2 diabetes and prediabetes

Changes in serum or plasma metabolome may reflect gut microbiota dysbiosis, which is also known to occur in patients with prediabetes and type 2 diabetes (T2DM). Thus, developing a robust method for the analysis of microbiota-dependent metabolites (MDMs) is an important issue. Gas chromatography with mass spectrometry (GC–MS) is a powerful approach enabling detection of a wide range of MDMs in biofluid samples with good repeatability and reproducibility, but requires selection of a suitable solvents and conditions. For this reason, we conducted for the first time the study in which, we demonstrated an optimisation of samples preparation steps for the measurement of 75 MDMs in two matrices. Different solvents or mixtures of solvents for MDMs extraction, various concentrations and volumes of derivatizing reagents as well as temperature programs at methoxymation and silylation step, were tested. The stability, repeatability and reproducibility of the 75 MDMs measurement were assessed by determining the relative standard deviation (RSD). Finally, we used the developed method to analyse serum samples from 18 prediabetic (PreDiab group) and 24 T2DM patients (T2DM group) from our 1000PLUS cohort. The study groups were homogeneous and did not differ in age and body mass index. To select statistically significant metabolites, T2DM vs. PreDiab comparison was performed using multivariate statistics. Our experiment revealed changes in 18 MDMs belonging to different classes of compounds, and seven of them, based on the SVM classification model, were selected as a panel of potential biomarkers, able to distinguish between patients with T2DM and prediabetes.

AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications

Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.

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.

Association of metabolites with obesity based on two gene variants, MC4R rs17782313 and BDNF rs6265

Previous genome-wide association analyses for obesity related genes demonstrated the association of BDNF gene variant rs6265 and MC4R gene variant rs17782313 with body mass index (BMI). However, the associated metabolite pathways are still behind the curtain. The aim of the current study is to investigate the associations of metabolic changes in obesity with MC4R gene variant rs17782313 and BDNF variant rs6265. Gas chromatography-mass spectrometry based untargeted metabolomics approach was used and 42 identified serum metabolites were selected for statistical analyses. Significant association of seven metabolites with MC4R gene variant rs17782313 based on obesity and thirty metabolites with obesity dependent BDNF variant rs6265 using additive model (adjusted p < 0.05) was observed. This study highlights the importance of alteration of fatty acid biosynthesis, probably due to high consumption of fats may cause to develop obesity. But obesity is a complex disorder and the full clarification of this complex machinery is still distant. To understand the obesity in a better way, more studies are required to identify remaining metabolites and also mechanism of these metabolic entities.

A comparative metabolomic study on desi and kabuli chickpea (Cicer arietinum L.) genotypes under rainfed and irrigated field conditions

Chickpea is considered among the most important leguminous crops in the world. However, in recent years drought conditions and/or limited availability of water have significantly reduced the production of chickpea. The current study was aimed to understand the legume stress response at the metabolic level for the determination of chickpea genotypes which can resist yield losses and could be cultivated with limited water availability. Here, we have analyzed two genotypes of chickpea, desi and kabuli under rainfed condition using a GC–MS based untargeted metabolomics approach. Results revealed significant differences in several metabolite features including oxalic acid, threonic acid, inositol, maltose and l-proline between studied groups. Accumulation of plant osmoprotectants such as l-proline, sugars and sugar alcohols was higher in desi genotype than kabuli genotype of chickpea when grown under the rainfed condition. Metabolic pathway analysis suggests that the inositol phosphate metabolism was involved in plant defense mechanisms against the limited water availability.

Metabolomics of red-light-induced stomatal opening in Arabidopsis thaliana: Coupling with abscisic acid and jasmonic acid metabolism

Environmental stimuli-triggered stomatal movement is a key physiological process that regulates CO₂ uptake and water loss in plants. Stomata are defined by pairs of guard cells that perceive and transduce external signals, leading to cellular volume changes and consequent stomatal aperture change. Within the visible light spectrum, red light induces stomatal opening in intact leaves. However, there has been debate regarding the extent to which red-light-induced stomatal opening arises from direct guard cell sensing of red light versus indirect responses as a result of red light influences on mesophyll photosynthesis. Here we identify conditions that result in red-light-stimulated stomatal opening in isolated epidermal peels and enlargement of protoplasts, firmly establishing a direct guard cell response to red light. We then employ metabolomics workflows utilizing gas chromatography mass spectrometry and liquid chromatography mass spectrometry for metabolite profiling and identification of Arabidopsis guard cell metabolic signatures in response to red light in the absence of the mesophyll. We quantified 223 metabolites in Arabidopsis guard cells, with 104 found to be red light responsive. These red-light-modulated metabolites participate in the tricarboxylic acid cycle, carbon balance, phytohormone biosynthesis and redox homeostasis. We next analyzed selected Arabidopsis mutants, and discovered that stomatal opening response to red light is correlated with a decrease in guard cell abscisic acid content and an increase in jasmonic acid content. The red-light-modulated guard cell metabolome reported here provides fundamental information concerning autonomous red light signaling pathways in guard cells.

Untargeted metabolomic analysis of coronary artery disease patients with diastolic dysfunction show disturbed oxidative pathway

INTRODUCTION

Left ventricular diastolic dysfunction (LVDD) is common in patients with coronary artery disease (CAD) with prevalence estimates of 34% and constitutes a predictor of all-cause mortality. Although diastolic dysfunction is induced by myocardial ischemia and has been shown to alter the clinical course, the role of coronary artery disease in the diastolic dysfunction and its progression into heart failure has not been completely elucidated.

OBJECTIVE

The present study was conducted to identify possible metabolites in coronary artery disease patients that are differentially regulated in patients with diastolic dysfunction.

METHODS

The serum of CAD (n = 75) patients and young healthy volunteers (n = 43) were analysed by using gas chromatography mass spectrometry (GC-MS) technique. Pre-processing of data results in 1547 features; among them 1064 features were annotated using NIST library.

RESULTS AND CONCLUSION

Fifteen metabolites were found to be statistically different between cases and control. Variation in metabolites were identified and correlated with several clinically important echocardiography parameters i.e. LVDD grades, ejection fraction (EF) and E/e' values. The results suggested that metabolic products of fatty acid oxidation and glucose oxidation pathways such as oleic acid, stearic acid, palmitic acid, linoleic acid, galactose, pyruvic and lactic acids are predominantly up regulated in patients with coronary artery disease and severity of diastolic dysfunction appears to be linked to increase in fatty acid oxidation and inflammation. The metabolic fingerprints of these patients give us an insight into the pathophysiological mechanism of diastolic dysfunction in coronary artery disease patients although it did not identify validated novel markers.

An In-Vitro Study for Early Detection and to Distinguish Breast and Lung Malignancies Using the Pcb Technology Based Nanodosimeter

Since the early detection of cancer increases the chance of successful treatment, the present study focused to confirm the suitability of an indigenously fabricated multilayer PCB technology based 3D positive ion detector to detect breast and lung malignancy at an early stage. The 3D positive ion detector is a type of gas filled radiation detector works under the principle of ion induced ionization using an exempted micro curie activity source. Earlier studies report that malignant cells can be detected by analyzing the Volatile Organic Compounds (VOCs) exhaled by those cells that serve as eminent biomarkers for malignant detection. Based on this, the present study analyzed the signals produced in the detector by VOCs exhaled from 140 biopsy tissue samples that include tissue of normal and all stages of breast and lung malignancy. To strengthen the present data, the normal and advanced breast and lung malignant tissues were also analyzed using the Gas Chromatography- Mass Spectrometry (GC-MS). From this study, it is confirmed that the present 3D positive ion detector can be used to detect both breast and lung malignancy and also to distinguish them based on the variation in four basic physical parameters of the output pulse such as frequency, amplitude, rise time and fall time and four derived parameters of the pulse such as FWHM, area of the pulse, ionization cluster size, and ion drift time.

A sensitive GC/MS detection method for analyzing microbial metabolites short chain fatty acids in fecal and serum samples

Gut microbiota and their major metabolites, short-chain fatty acids (SCFAs), are recognized as important players in gut homeostasis and metabolic disease occurance. A convenient and sensitive detection method is needed to profile SCFAs in limited and complex biological samples. The gas chromatography/mass spectrometry (GC/MS) is the most common method for SCFAs profiling in biological samples. Trimethylsilyl (TMS) derivatization reagents such as N, O-bis(trimethyl-silyl)-trifluoroacetamide (BSTFA) are commonly used in GC/MS analysis to improve sensitivity and accuracy, but they were barely used in SCFA analysis due to their sensitivity to moisture and the volatility of SCFAs. Here, we developed a rapid, convenient and reliable method for SCFAs profiling in small amounts of fecal and serum samples by GC/MS using BSTFA in combination with sodium sulfate dehydration pretreatment. SCFAs were extracted with anhydrous ether from acidified fecal water extract or serum samples, followed by dehydration with sodium sulfate and BSTFA derivatization at a reduced temperature. Select ion monitoring mode was used for highly sensitive quantification of SCFAs by GC/MS. The derivation with BSTFA at 37 °C or 22 °C showed an excellent linearity (R² > 0.999), good recoveries (81.27-128.42%), high repeatability (RSD < 2%) and low limit of detections (LODs) of different SCFAs ranging from 0.064 to 0.067 µM. All major SCFAs including acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid and valeric acid were identified and quantified accurately in fecal and serum samples. In conclusions, a reliable, convenient and sensitive method wasdeveloped for the measurement of SCFA and other volatile compounds in small biological samples using sodium sulfate dehydration pretreatment and BSTFA derivatization-based GC/MS analyses.

Hydroxyurea Treated β-Thalassemia Children Demonstrate a Shift in Metabolism Towards Healthy Pattern

Augmentation of fetal hemoglobin (HbF) production has been an enduring therapeutic objective in β-thalassemia patients for which hydroxyurea (HU) has largely been the drug of choice and the most cost-effective approach. A serum metabolomics study on 40 patients with β-thalassemia prior to and after administration of HU was done along with healthy controls. Treated patients were divided further into non-responders (NR), partial (PR) and good (GR) per their response. 25 metabolites that were altered before HU therapy at p ≤ 0.05 and fold change >2.0 in β-thalassemia patients; started reverting towards healthy group after HU treatment. A prediction model based on another set of 70 HU treated patients showed a good separation of GR from untreated β-thalassemia patients with an overall accuracy of 76.37%. Metabolic pathway analysis revealed that various important pathways that were disturbed in β-thalassemia were reverted after treatment with HU and among them linoleic acid pathway was most impactfully improved in HU treated patients which is a precursor of important signaling molecules. In conclusion, this study indicates that HU is a good treatment option for β-thalassemia patients because in addition to reducing blood transfusion burden it also ameliorates disease complications by shifting body metabolism towards normal.

Metabolic Signatures in Response to Abscisic Acid (ABA) Treatment in Brassica napus Guard Cells Revealed by Metabolomics

Drought can severely damage crops, resulting in major yield losses. During drought, vascular land plants conserve water via stomatal closure. Each stomate is bordered by a pair of guard cells that shrink in response to drought and the associated hormone abscisic acid (ABA). The activation of complex intracellular signaling networks underlies these responses. Therefore, analysis of guard cell metabolites is fundamental for elucidation of guard cell signaling pathways. Brassica napus is an important oilseed crop for human consumption and biodiesel production. Here, non-targeted metabolomics utilizing gas chromatography mass spectrometry (GC-MS/MS) and liquid chromatography mass spectrometry (LC-MS/MS) were employed for the first time to identify metabolic signatures in response to ABA in B. napus guard cell protoplasts. Metabolome profiling identified 390 distinct metabolites in B. napus guard cells, falling into diverse classes. Of these, 77 metabolites, comprising both primary and secondary metabolites were found to be significantly ABA responsive, including carbohydrates, fatty acids, glucosinolates, and flavonoids. Selected secondary metabolites, sinigrin, quercetin, campesterol, and sitosterol, were confirmed to regulate stomatal closure in Arabidopsis thaliana, B. napus or both species. Information derived from metabolite datasets can provide a blueprint for improvement of water use efficiency and drought tolerance in crops.

Serum Metabolomic Profiles for Breast Cancer Diagnosis, Grading and Staging by Gas Chromatography-Mass Spectrometry

Detection of metabolic signature for breast cancer (BC) has the potential to improve patient prognosis. This study identified potentially significant metabolites differentiating between breast cancer patients and healthy controls to help in diagnosis, grading, staging and determination of neoadjuvant status. Serum was collected from 152 pre-operative breast cancer (BC) patients and 155 healthy controls in this case-controlled study. Gas chromatography-mass spectrometry (GC-MS) was used to obtain metabolic profiles followed by chemometric analysis with the identification of significantly differentiated metabolites including 7 for diagnosis, 18 for grading, 23 for staging, 15 for stage III subcategory and 10 for neoadjuvant status (p-value < 0.05). Partial Least Square Discriminant Analysis (PLS-DA) model revealed a distinct separation between healthy controls and BC patients with a sensitivity of 96% and specificity of 100% on external validation. Models for grading, staging and neoadjuvant status were built with Decision Tree Algorithm with predictive accuracy of 71.5%, 71.3% and 79.8% respectively. Pathway analysis revealed increased glycolysis, lipogenesis, and production of volatile organic metabolites indicating the metabolic alterations in breast cancer.

β-Thalassemia Patients Revealed a Significant Change of Untargeted Metabolites in Comparison to Healthy Individuals

β-Thalassemia is one of the most prevalent forms of congenital blood disorders characterized by reduced hemoglobin levels with severe complications, affecting all dimensions of life. The mechanisms underlying the phenotypic heterogeneity of β-thalassemia are still poorly understood. We aimed to work over metabolite biomarkers to improve mechanistic understanding of phenotypic heterogeneity and hence better management of disorder at different levels. Untargeted serum metabolites were analyzed after protein precipitation and SPE (solid phase extraction) from 100 β-thalassemia patients and 61 healthy controls using GC-MS. 40 metabolites were identified having a significance difference between these two groups at probability of 0.05 and fold change >1.5. Out of these 40 metabolites, 17 were up-regulated while 23 were down-regulated. PCA and PLS-DA model was also created that revealed a fine separation with a sensitivity of 70% and specificity of 100% on external validation of samples. Metabolic pathway analysis revealed alteration in multiple pathways including glycolysis, pyruvate, propanoate, glycerophospholipid, galactose, fatty acid, starch and sucrose metabolism along with fatty acid elongation in mitochondria, glycerolipid, glyoxylate and dicarboxylate metabolism pointing towards the shift of metabolism in β-thalassemia patients in comparison to healthy individuals.

SERUM metabolomics of acute lymphoblastic leukaemia and acute myeloid leukaemia for probing biomarker molecules

Acute leukaemia (AL) is a critical neoplasm of white blood cells. Diagnosing AL requires bone marrow puncture procedure, which many patients do not consent to for it is invasive. Hence sensitive and specific early diagnostic biomarkers are essential for non-invasive diagnosis, new therapeutics and improving the disease prognosis. To differentiate the metabolic alterations associated with acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML), we investigated serum of ALL and AML patients in comparison with two controls using gas chromatography coupled with triple quadrupole tandem mass spectrometry and multivariate statistical analysis. Twenty seven out of 1425 metabolites were found differentiative among ALL, AML, aplastic anaemia (APA) patients and healthy control using p-value ≤ 0.001. ALL is the most dissimilar group from other three groups as in hierarchical clustering showed 72.1% dissimilarity. Model generation using PLSDA gave an overall accuracy of 91.9%. This study helps in metabolic fingerprinting of control and disease serum at high significance levels and could be used for early diagnosing of AL. Based on pathways analysis, fatty acid metabolism is deregulated in patients with AL and may represent an underlying metabolic pathway associated with disease progression. Copyright © 2016 John Wiley & Sons, Ltd.

Determination of carbohydrates in tobacco by pressurized liquid extraction combined with a novel ultrasound-assisted dispersive liquid-liquid microextraction method

A novel derivatization-ultrasonic assisted-dispersive liquid-liquid microextraction (UA-DLLME) method for the simultaneous determination of 11 main carbohydrates in tobacco has been developed. The combined method involves pressurized liquid extraction (PLE), derivatization, and UA-DLLME, followed by the analysis of the main carbohydrates with a gas chromatography-flame ionization detector (GC-FID). First, the PLE conditions were optimized using a univariate approach. Then, the derivatization methods were properly compared and optimized. The aldononitrile acetate method combined with the O-methoxyoxime-trimethylsilyl method was used for derivatization. Finally, the critical variables affecting the UA-DLLME extraction efficiency were searched using fractional factorial design (FFD) and further optimized using Doehlert design (DD) of the response surface methodology. The optimum conditions were found to be 44 μL for CHCl3, 2.3 mL for H2O, 11% w/v for NaCl, 5 min for the extraction time and 5 min for the centrifugation time. Under the optimized experimental conditions, the detection limit of the method (LODs) and linear correlation coefficient were found to be in the range of 0.06-0.90 μg mL(-1) and 0.9987-0.9999. The proposed method was successfully employed to analyze three flue-cured tobacco cultivars, among which the main carbohydrate concentrations were found to be very different.

Application of metabolite profiling tools and time-of-flight mass spectrometry in the identification of transformation products of iopromide and iopamidol during advanced oxidation

The efficiency of wastewater treatment systems in removing pharmaceuticals is often assessed on the basis of the decrease in the concentration of the parent compound. However, what is perceived as "removal" during treatment may not necessarily mean mineralization of the pharmaceutical compound but simply conversion into different transformation products (TPs). Using liquid chromatography coupled to a quadrupole time-of-flight mass spectrometer (LC-QToF-MS), we demonstrated conversion of iopromide in wastewater to at least 14 TPs after an advanced oxidation process (AOP) using UV (fluence = 1500 mJ/cm(2)) and H2O2 (10 mg/L). Due to the complexity of the wastewater matrix, the initial experiments were performed using a high concentration (10 mg/L) of iopromide in order to facilitate the identification of TPs. Despite the high concentration of iopromide used, cursory inspection of UV and mass spectra only revealed four TPs in the chromatograms of the post-AOP samples. However, the use of METLIN database and statistics-based profiling tools commonly used in metabolomics proved effective in discriminating between background signals and TPs derived from iopromide. High-resolution mass data allowed one to predict molecular formulas of putative TPs with errors below 5 ppm relative to the observed m/z. Tandem mass spectrometry (MS/MS) data and isotope pattern comparisons provided necessary information that allowed one to elucidate the structure of iopromide TPs. The presence of the proposed iopromide TPs was determined in unspiked wastewater from a municipal wastewater treatment plant, but no iopromide and TPs were detected. Using analogous structural modifications and oxidation that results from the AOP treatment of iopromide, the potential TPs of iopamidol (a structurally similar compound to iopromide) were predicted. The same mass fragmentation pattern observed in iopromide TPs was applied to the predicted iopamidol TPs. LC-QToF-MS revealed the presence of two iopamidol TPs in unspiked AOP-treated wastewater.

Plasma metabolite profiling and chemometric analyses of lung cancer along with three controls through gas chromatography-mass spectrometry

Lung cancer has been the most common death causing cancer in the world for several decades. This study is focused on the metabolite profiling of plasma from lung cancer (LC) patients with three control groups including healthy non-smoker (NS), smokers (S) and chronic obstructive pulmonary disease patients (COPD) samples using gas chromatography-mass spectrometry (GC-MS) in order to identify the comparative and distinguishing metabolite pattern for lung cancer. Metabolites obtained were identified through National Institute of Standards and Technology (NIST) mass spectral (Wiley registry) and Fiehn Retention Time Lock (RTL) libraries. Mass Profiler Professional (MPP) Software was used for the alignment and for all the statistical analysis. 32 out of 1,877 aligned metabolites were significantly distinguished among three controls and lung cancer using p-value ≤ 0.001. Partial Least Square Discriminant Analysis (PLSDA) model was generated using statistically significant metabolites which on external validation provide high sensitivity (100%) and specificity (78.6%). Elevated level of fatty acids, glucose and acids were observed in lung cancer in comparison with control groups apparently due to enhanced glycolysis, gluconeogenesis, lipogenesis and acidosis, indicating the metabolic signature for lung cancer.