Evaluation of Cocaine Effect on Endogenous Metabolites of HepG2 Cells Using Targeted Metabolomics

Cocaine toxicity has been a subject of study because cocaine is one of the most common and potent drugs of abuse. In the current study the effect of cocaine on human liver cancer cell line (HepG2) was assessed. Cocaine toxicity (IC50) on HepG2 cells was experimentally calculated using an XTT assay at 2.428 mM. The metabolic profile of HepG2 cells was further evaluated to investigate the cytotoxic activity of cocaine at 2 mM at three different time points. Cell medium and intracellular material samples were analyzed with a validated HILIC-MS/MS method for targeted metabolomics on an ACQUITY Amide column in gradient mode with detection on a triple quadrupole mass spectrometer in multiple reaction monitoring. About 106 hydrophilic metabolites from different metabolic pathways were monitored. Multivariate analysis clearly separated the studied groups (cocaine-treated and control samples) and revealed potential biomarkers in the extracellular and intracellular samples. A predominant effect of cocaine administration on alanine, aspartate, and glutamate metabolic pathway was observed. Moreover, taurine and hypotaurine metabolism were found to be affected in cocaine-treated cells. Targeted metabolomics managed to reveal metabolic changes upon cocaine administration, however deciphering the exact cocaine cytotoxic mechanism is still challenging.     

Comparison of Widely Targeted Metabolomics and Untargeted Metabolomics of Wild Ophiocordyceps sinensis

The authors of this paper conducted a comparative metabolomic analysis of Ophiocordyceps sinensis (OS), providing the metabolic profiles of the stroma (OSBSz) and sclerotia (OSBSh) of OS by widely targeted metabolomics and untargeted metabolomics. The results showed that 778 and 1449 metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. The metabolites in OSBSz and OSBSh are significantly differentiated; 71 and 96 differentially expressed metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. This suggests that these 71 metabolites (riboflavine, tripdiolide, bromocriptine, lumichrome, tetrahymanol, citrostadienol, etc.) and 96 metabolites (sancycline, vignatic acid B, pirbuterol, rubrophen, epalrestat, etc.) are potential biomarkers. 4-Hydroxybenzaldehyde, arginine, and lumichrome were common differentially expressed metabolites. Using the widely targeted metabolomics approach, the key pathways identified that are involved in creating the differentiation between OSBSz and OSBSh may be nicotinate and nicotinamide metabolism, thiamine metabolism, riboflavin metabolism, glycine, serine, and threonine metabolism, and arginine biosynthesis. The differentially expressed metabolites identified using the untargeted metabolomics approach were mainly involved in arginine biosynthesis, terpenoid backbone biosynthesis, porphyrin and chlorophyll metabolism, and cysteine and methionine metabolism. The purpose of this research was to provide support for the assessment of the differences between the stroma and sclerotia, to furnish a material basis for the evaluation of the physical effects of OS, and to provide a reference for the selection of detection methods for the metabolomics of OS.     

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70–80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.     

基于质谱分析的代谢组学研究进展

质谱分析技术是代谢组学研究的重要技术之一。该文通过近5年来的文献分析,对基于质谱分析的代谢组学研究方法的新进展,包括样品前处理方法、分析检测方法、数据处理方法等,以及近年来代谢组学在疾病诊断、药物研发、营养学、毒理学、运动医学等领域的应用进展,进行了较全面的综述,并对未来的发展趋势进行了展望。     

基于超高效液相色谱-质谱的胶质瘤患者血浆代谢组学研究

采用超高效液相色谱-四极杆-静电场轨道阱质谱(UHPLC-Q-OrbitrapHRMS)技术对胶质瘤患者和正常对照人群的血浆进行代谢轮廓分析,筛选胶质瘤代谢标志物,为其发病机制阐明和临床早期诊断提供科学依据。通过对UHPLC-Q-OrbitrapHRMS采集得到的谱图进行峰识别、峰匹配和去噪等处理后,应用主成分和正交偏最小二乘-判别分析法对代谢组学数据进行统计分析,筛选VIP1.0及P0.05的差异代谢物,并进一步对其诊断能力进行评价。结果显示,胶质瘤患者的血浆代谢轮廓发生明显变化,发现并鉴定得到10个差异代谢物,其中亮氨酸、缬氨酸、色氨酸、胆碱和牛磺酸在胶质瘤患者血浆中含量降低,组氨酸、柠檬酸、乳酸、肌酸和丙酮酸含量升高,与正常对照组比较具有显著性差异(P0.05),提示氨基酸和能量等代谢异常可能对胶质瘤的发生发展具有重要影响。此外,各差异性代谢物对胶质瘤均显示出较好的诊断能力(AUC0.8),可作为潜在诊断标志物。     

Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry

Diagnosis of human bladder cancer in untreated tissue sections is achieved by using imaging data from desorption electrospray ionization mass spectrometry (DESI-MS) combined with multivariate statistical analysis. We use the distinctive DESI-MS glycerophospholipid (GP) mass spectral profiles to visually characterize and formally classify twenty pairs (40 tissue samples) of human cancerous and adjacent normal bladder tissue samples. The individual ion images derived from the acquired profiles correlate with standard histological hematoxylin and eosin (H&E)-stained serial sections. The profiles allow us to classify the disease status of the tissue samples with high accuracy as judged by reference histological data. To achieve this, the data from the twenty pairs were divided into a training set and a validation set. Spectra from the tumor and normal regions of each of the tissue sections in the training set were used for orthogonal projection to latent structures (O-PLS) treated partial least-square discriminate analysis (PLS-DA). This predictive model was then validated by using the validation set and showed a 5% error rate for classification and a misclassification rate of 12%. It was also used to create synthetic images of the tissue sections showing pixel-by-pixel disease classification of the tissue and these data agreed well with the independent classification that uses histological data by a certified pathologist. This represents the first application of multivariate statistical methods for classification by ambient ionization although these methods have been applied previously to other MS imaging methods. The results are encouraging in terms of the development of a method that could be utilized in a clinical setting through visualization and diagnosis of intact tissue.     

Changes in Triterpenes in Alismatis rhizoma after Processing Based on Targeted Metabolomics Using UHPLC-QTOF-MS/MS

Alismatis rhizoma (AR) has been used as an herbal medicine in China for over a thousand years. Crude AR, salt-processed AR (SAR), and bran-processed AR (BAR) are recorded in the Pharmacopoeia of the People′s Republic of China. However, the differences of chemical composition between crude AR and its processing products remain limited. In this study, triterpenes were identified from crude AR, SAR, and BAR by ultra-high performance liquid chromatography coupled with quadrupole time-of-flight-mass spectrometer (UHPLC-QTOF-MS/MS). Subsequently, the differences of triterpenes between the crude AR and processed ARs were compared via a targeted metabolomics approach. Finally, a total of 114 triterpenes were identified, of which 83, 100, and 103 triterpenes were found in crude AR, SAR, and BAR, respectively. After salt-processing, there were 17 triterpenes newly generated, 7 triterpenes with trends of increasing, and 37 triterpenes decreased. Meanwhile, 56 triterpenes including 21 newly generated and 35 with significant increases were observed in BAR. This study could be benefit to investigate the processing mechanism of AR, as well as support their clinical applications.     

Application of the Metabolomics Approach in Food Authentication

The authentication of food products is essential for food quality and safety. Authenticity assessments are important to ensure that the ingredients or contents of food products are legitimate and safe to consume. The metabolomics approach is an essential technique that can be utilized for authentication purposes. This study aimed to summarize food authentication through the metabolomics approach, to study the existing analytical methods, instruments, and statistical methods applied in food authentication, and to review some selected food commodities authenticated using metabolomics-based methods. Various databases, including Google Scholar, PubMed, Scopus, etc., were used to obtain previous research works relevant to the objectives. The review highlights the role of the metabolomics approach in food authenticity. The approach is technically implemented to ensure consumer protection through the strict inspection and enforcement of food labeling. Studies have shown that the study of metabolomics can ultimately detect adulterant(s) or ingredients that are added deliberately, thus compromising the authenticity or quality of food products. Overall, this review will provide information on the usefulness of metabolomics and the techniques associated with it in successful food authentication processes, which is currently a gap in research that can be further explored and improved.     

HR-MAS NMR Applications in Plant Metabolomics

Metabolomics is used to reduce the complexity of plants and to understand the underlying pathways of the plant phenotype. The metabolic profile of plants can be obtained by mass spectrometry or liquid-state NMR. The extraction of metabolites from the sample is necessary for both techniques to obtain the metabolic profile. This extraction step can be eliminated by making use of high-resolution magic angle spinning (HR-MAS) NMR. In this review, an HR-MAS NMR-based workflow is described in more detail, including used pulse sequences in metabolomics. The pre-processing steps of one-dimensional HR-MAS NMR spectra are presented, including spectral alignment, baseline correction, bucketing, normalisation and scaling procedures. We also highlight some of the models which can be used to perform multivariate analysis on the HR-MAS NMR spectra. Finally, applications of HR-MAS NMR in plant metabolomics are described and show that HR-MAS NMR is a powerful tool for plant metabolomics studies.     

Metabolomics of lung cancer: Analytical platforms and their applications

Lung cancer is the leading type of cancer worldwide in terms of the number of new cases and is responsible for the largest number of deaths due to poor prognosis and difficult early detection. Due to its ability to detect numerous small molecular metabolites simultaneously, metabolomics has been widely used for the assessment of global metabolic changes in a living organism to discover candidate biomarkers for cancer diagnosis, investigate the development of cancer, and provide insights into the underlying pathophysiology. This review will mainly describe recent developments in lung cancer metabolomics in terms of early‐stage detection, biomarker discovery and mechanism exploration by using nuclear magnetic resonance, liquid chromatography–mass spectrometry, gas chromatography–mass spectrometry, and capillary electrophoresis–mass spectrometry in the last 10 years. The sample collection and metabolite extraction methods are also summarized.     

NMF-Based Approach for Missing Values Imputation of Mass Spectrometry Metabolomics Data

In mass spectrometry (MS)-based metabolomics, missing values (NAs) may be due to different causes, including sample heterogeneity, ion suppression, spectral overlap, inappropriate data processing, and instrumental errors. Although a number of methodologies have been applied to handle NAs, NA imputation remains a challenging problem. Here, we propose a non-negative matrix factorization (NMF)-based method for NA imputation in MS-based metabolomics data, which makes use of both global and local information of the data. The proposed method was compared with three commonly used methods: k-nearest neighbors (kNN), random forest (RF), and outlier-robust (ORI) missing values imputation. These methods were evaluated from the perspectives of accuracy of imputation, retrieval of data structures, and rank of imputation superiority. The experimental results showed that the NMF-based method is well-adapted to various cases of data missingness and the presence of outliers in MS-based metabolic profiles. It outperformed kNN and ORI and showed results comparable with the RF method. Furthermore, the NMF method is more robust and less susceptible to outliers as compared with the RF method. The proposed NMF-based scheme may serve as an alternative NA imputation method which may facilitate biological interpretations of metabolomics data.     

Enantioselectivity Effects in Clinical Metabolomics and Lipidomics

Metabolomics and lipidomics have demonstrated increasing importance in underlying biochemical mechanisms involved in the pathogenesis of diseases to identify novel drug targets and/or biomarkers for establishing therapeutic approaches for human health. Particularly, bioactive metabolites and lipids have biological activity and have been implicated in various biological processes in physiological conditions. Thus, comprehensive metabolites, and lipids profiling are required to obtain further advances in understanding pathophysiological changes that occur in cells and tissues. Chirality is one of the most important phenomena in living organisms and has attracted long-term interest in medical and natural science. Enantioselective separation plays a pivotal role in understanding the distribution and physiological function of a diversity of chiral bioactive molecules. In this context, it has been the goal of method development for targeted and untargeted metabolomics and lipidomic assays. Herein we will highlight the benefits and challenges involved in these stereoselective analyses for clinical samples.     

基于液相色谱-质谱技术的肾脏代谢组学分析方法研究

建立了一种基于高效液相色谱-超高分辨质谱技术的肾脏代谢组学分析方法。肾脏组织于液氮中研磨成粉后,采用甲基叔丁基醚/甲醇/水溶剂体系进行提取,分别得到强极性部分(下层)和弱极性部分(上层)提取物,依次采用HILIC亲水色谱柱和反相C₁₈色谱柱进行梯度洗脱分离后,进行高分辨质谱分析。采用电喷雾离子源(ESI),正、负离子模式检测,扫描方式为全扫描,扫描范围为100～1 000 Da,质量分辨率为120 000。结果表明,该方法灵敏度高,专属性强,稳定性良好,可同时获取肾脏组织中的强极性和弱极性代谢物信息,可为肾脏疾病和药物肾毒性生物标志物的发现提供一种新的方法。     

基于液相色谱-质谱联用技术的代谢组学分析方法研究进展

代谢组学是研究小分子代谢物的有用工具,能够直接反映生命体终端和表型信息,在精准医学和转化医学中发挥着重要作用。色谱-质谱联用技术具有灵敏度高、选择性好、动态范围宽、信息丰富等优点,已成为代谢组学研究的主要技术平台。代谢组学分析方法的创新与进展是代谢组学在各领域广泛应用的重要前提。该文综述了近5年来基于液相色谱-质谱联用技术的代谢组学分析方法取得的成果,并对目前存在的问题及发展前景给予展望。综述引用文献81篇。     

基于HPLC/Q-TOF MS的4种农药联合暴露人群的代谢组学研究

采用基于高效液相色谱-飞行时间质谱联用(HPLC-TOF MS)的代谢组学方法,研究了啶虫脒、高效氯氟氰菊酯、联苯菊酯、甲氨基阿维菌素苯甲酸盐4种农药联合暴露所致的施药人群尿液中内源性代谢物的变化。采集30位农民喷洒4种复合农药前和喷洒农药期1,3,5,7 d的尿液进行检测。提取正常尿液中常见代谢物并通过质控样品评价手段进行分析,结果表明该方法具有良好的稳定性和精密度,可用于尿液中代谢物分析。多变量分析结果表明,暴露人群施药前后尿液的代谢物含量存在较大差异。对选取的36个差异离子进行鉴定,确定了8个生物标志物的结构。结果显示联合暴露组人群尿液中多巴胺、5-羟色胺、酪氨酸、色氨酸、牛磺酸和马尿酸的含量显著下降;犬尿素和肌酸的含量显著上升。4种农药联合暴露导致接触人群尿液中色氨酸代谢途径的中间产物含量降低,肝代谢和能量代谢相关的代谢物蓄积,可能与神经系统和肝脏功能的受损有关。     

Plasma metabolomic profile and potential biomarkers for missed abortion

A missed abortion (MA) is an in utero death of the embryo or fetus before the 20th week of gestation with retained products of conception, and this condition is currently common in China. In order to discover novel biomarkers for MA, ultrahigh performance liquid chromatography was applied to study plasma metabolite profiles for 33 patients with MA and 29 control subjects. Thirty‐seven differential plasma metabolites were found to discriminate between the two groups in the initial cohort (15 subjects with MA and 15 healthy controls). The feasibility of using these potential biomarkers to predict MA was further evaluated in the validation cohort (18 subjects with MA and 14 healthy controls) and 15 had an area under the receiver operating characteristic curve of >0.80, making them satisfactory. Tryptophan metabolism and sphingolipid metabolism were identified as important potential target pathways for MA using metabolic pathway impact analysis. Furthermore, three of the 15 satisfactory metabolites (glyceric acid, indole and sphingosine) were combined to establish a predictive model with 100% sensitivity and 100% specificity in the validation cohort. Taken together, these results suggest that MA results in significant disturbance of metabolism and those various novel biomarkers have satisfactory diagnostic and predictive power for MA.     

Biomarker Discovery of Acute Coronary Syndrome Using Proteomic Approach

Acute coronary syndrome (ACS) is a condition in which the coronary artery supplying blood to the heart is infarcted via formation of a plaque and thrombus, resulting in abnormal blood supply and high mortality and morbidity. Therefore, the prompt and efficient diagnosis of ACS and the need for new ACS diagnostic biomarkers are important. In this study, we aimed to identify new ACS diagnostic biomarkers with high sensitivity and specificity using a proteomic approach. A discovery set with samples from 20 patients with ACS and 20 healthy controls was analyzed using mass spectrometry. Among the proteins identified, those showing a significant difference between each group were selected. Functional analysis of these proteins was conducted to confirm their association with functions in the diseased state. To determine ACS diagnostic biomarkers, standard peptides of the selected protein candidates from the discovery set were quantified, and these protein candidates were validated in a validation set consisting of the sera of 50 patients with ACS and 50 healthy controls. We showed that hemopexin, leucine-rich α-2-glycoprotein, and vitronectin levels were upregulated, whereas fibronectin level was downregulated, in patients with ACS. Thus, the use of these biomarkers may increase the accuracy of ACS diagnosis.