Metabolomics approach based on utra-performance liquid chromatography coupled to mass spectrometry with chemometrics methods for high-throughput analysis of metabolite biomarkers to explore the abnormal metabolic pathways associated with myocardial dysfunction

Ultra-performance liquid chromatography/mass spectrometry-based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh-pressure liquid chromatography with mass spectrometry-based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide-induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high-resolution mass spectrometry-based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease.     

Urine biomarkers discovery by metabolomics and machine learning for Parkinson's disease diagnoses

Parkinson’s disease(PD) is a complex neurological disorder that typically worsens with age. A wide range of pathologies makes PD a very heterogeneous condition, and there are currently no reliable diagnostic tests for this disease. The application of metabolomics to the study of PD has the potential to identify disease biomarkers through the systematic evaluation of metabolites. In this study, urine metabolic profiles of 215 urine samples from 104 PD patients and 111 healthy individuals were ass...     

Recent advances in metabolomics in neurological disease, and future perspectives

Discovery of clinically relevant biomarkers for diseases has revealed metabolomics has potential advantages that classical diagnostic approaches do not. The great asset of metabolomics is that it enables assessment of global metabolic profiles of biofluids and discovery of biomarkers distinguishing disease status, with the possibility of enhancing clinical diagnostics. Most current clinical chemistry tests rely on old technology, and are neither sensitive nor specific for a particular disease. Clinical diagnosis of major neurological disorders, for example Alzheimer’s disease and Parkinson’s disease, on the basis of current clinical criteria is unsatisfactory. Emerging metabolomics is a powerful technique for discovering novel biomarkers and biochemical pathways to improve diagnosis, and for determination of prognosis and therapy. Identifying multiple novel biomarkers for neurological diseases has been greatly enhanced with recent advances in metabolomics that are more accurate than routine clinical practice. Cerebrospinal fluid (CSF), which is known to be a rich source of small-molecule biomarkers for neurological and neurodegenerative diseases, and is in close contact with diseased areas in neurological disorders, could potentially be used for disease diagnosis. Metabolomics will drive CSF analysis, facilitate and improve the development of disease treatment, and result in great benefits to public health in the long-term. This review covers different aspects of CSF metabolomics and discusses their significance in the postgenomic era, emphasizing the potential importance of endogenous small-molecule metabolites in this emerging field.     

18F-FDG PET/CT显像联合肿瘤标志物检查对女性不明原因腹腔积液的诊断价值

本文探讨了18 F-脱氧葡萄糖(18 F-FDG)正电子发射计算机断层显像(PET/CT)联合肿瘤标志物检查对女性不明原因腹腔积液的鉴别诊断价值。选取95例不明原因腹腔积液女性患者的18 F-FDG PET/CT影像资料及肿瘤标志物资料进行回顾性分析,并与病理诊断结果进行对照。结果显示:95例患者中,经病理证实恶性腹腔积液74例,良性腹腔积液21例。PET/CT对良恶性腹腔积液的诊断灵敏度为98.65%,特异度为19.05%,准确度为81.05%。恶性腹腔积液的SUV max与良性腹腔积液比较,差异无统计学意义(P>0.05)。将患者按病因进一步细分后发现,卵巢癌组的SUV max明显高于其他良性原因组(P<0.05),但与结核组和其他恶性肿瘤组比较,差异无统计学意义(P>0.05)。肿瘤标志物中以CA125检测效率最高,其对卵巢癌及结核所致腹腔积液的诊断灵敏度为98.2%,特异度为7.7%,准确度为81.4%。PET/CT和CA125联合诊断对卵巢癌及结核所致腹腔积液的灵敏度、特异度及准确度与单一PET/CT或CA125诊断比较差异均无统计学意义(P>0.05)。本实验表明18 F-FDG PET/CT诊断女性不明原因腹腔积液的良恶性有较高的灵敏度和准确度,但特异性较差,联合肿瘤标志物检查并不能提高对卵巢癌及结核所致腹腔积液的诊断效能。     

SMI技术测定血流分布模式、消融灶体积与甲状腺结节良恶性诊断及患者疗效预测的关系研究

分析SMI（微血管成像技术）测定血流分布模式、消融灶体积与甲状腺结节良恶性诊断及患者疗效预测的关系。将我院2017年1月～2019年2月期间收治的60例甲状腺结节患者依照患者术后病理检测结果分为良性组及恶性组;采用微波消融术对患者进行治疗,并在治疗前后采用SMI及CEUS（超声造影）对患者病情进行评估。结果显示,采用SMI对患者进行评估的敏感度、特异度、准确度及AUC,均明显高于CEUS,且差异存统计学意义（P<0.05）;采用SMI扫描检查的病灶未完全消融率、参与微血管率均明显高于CEUS检查,且差异存统计学意义（P<0.05）,采用SMI扫描检查的消融灶体积、消融灶长及消融灶宽均明显高于CEUS扫描检查,且差异存统计学意义（P<0.05）。证明采用SMI技术对甲状腺结节良恶性进行诊断具有较高的应用价值,并可有效用于评估临床疗效。     

High-Throughput Metabolomics Integrated Network Pharmacology Reveals the Underlying Mechanism of Paeoniae Radix Alba Treating Rheumatoid Arthritis

Objective: The mechanism of action and potential targets of Paeoniae Radix Alba (Baishao, B) in the treatment of adjuvant-induced arthritis (AIA) rats are explained using metabolomics and network pharmacology techniques, and the research evidence for the development of anti-rheumatoid arthritis (RA) drugs is enriched. Methods: The rats were injected with Freund’s complete adjuvant (CFA) to induce arthritis. We then measured the general physical characteristics, examined their X-rays and histopathology to evaluate the pathological condition of the inflammation models, and conducted metabolomics studies on the change in urine metabolism caused by CFA. The lyophilized powder of B at a dose of 2.16 g/kg was orally administered to the rats continuously for 28 days, and the therapeutic effect was evaluated. Network pharmacology prediction shows that B contains the target action of the ingredient, and the simulation of the target molecular docking, in combination with the metabolomics analysis results, shows that B has a potential role in the treatment of AIA rats. Results: B can reduce the paw swelling and pathological changes in rats caused by CFA, reverse the levels of 12 urine biomarkers, and regulate histidine metabolism, phenylalanine metabolism, arginine, proline metabolism, pyrimidine metabolism, etc. The prediction of the active ingredient target in B indicates that it may act as an inflammatory signaling pathway in anti-RA, among them being paeoniflorin, palbinone, beta-sitosterol, kaempferol, and catechin, which are the significant active ingredients. Conclusion: The metabolomics results revealed the markers and metabolic mechanisms of urinary metabolic disorders in rats with AIA, demonstrated the efficacy of the therapeutic effect of B, and identified the key ingredients in B, providing theoretical support for the subsequent development and utilization of B.     

Accelerated,untargeted metabolomics analysis of cutaneous T‐cell lymphoma reveals metabolic shifts in plasma and tumor adjacent skins of xenograft mice

Cutaneous T‐cell lymphoma (CTCL) is a heterogeneous group of skin‐homing T‐cell neoplasms. Clinical management is stage based but diagnosis and prognosis could be extremely challenging. The presented study aims to explore the metabolic profiling of CTCL by an accelerated untargeted metabolomics data analysis tool “Mummichog” to facilitate the discoveries of potential biomarkers for clinical early stage diagnosis, prognosis, and treatments in CTCL. Ultra high‐performance liquid chromatography–quadrupole time‐of‐flight–based untargeted metabolomics were conducted on the skin and plasma of CTCL mice. It showed that the metabolism of skin changed greatly versus control samples in the development of CTCL. Increased l ‐glutamate and decreased adenosine monophosphate were the most essential metabolic features of CTCL tumor and tumor adjacent skins. Unique metabolism changes in tumor adjacent non‐involved skin tissues (ANIT) occurred in the progress of carcinogenesis, including upregulated cytidine‐5′‐triphosphate, aberrant biosynthesis of prostaglandins, pyrimidine, mevalonate pathway, and tryptophan degradation. Sharply elevated 5‐phospho‐α‐d ‐ribose 1‐diphosphate (PRPP) marked the final state of tumor in CTCL. In the plasma, systematic shifts in corticosterone, sphingolipid, and ceramide metabolism were found. These uncovered aberrant metabolites and metabolic pathways suggested that the metabolic reprogramming of PRPP in tumor tissues may cause the disturbance of cytidine and uridine metabolic homeostasis in ANIT. Accumulative cytidine‐5′‐triphosphate in ANIT may exert positive feedback on the PRPP level and leads to CTCL further development. In addition, the accelerated data analysis tool “Mummichog” showed good practicability and can be widely used in high‐resolution liquid chromatography mass spectrometry–based untargeted metabolomics.     

Rapid diagnosis of papillary thyroid carcinoma with machine learning and probe electrospray ionization mass spectrometry

Frozen section examination could provide pathological diagnosis for surgery of thyroid nodules, which is time-consuming, skill- and experience-dependent. This study developed a rapid classification method for thyroid nodules and machine learning. Total 69 tissues were collected including 43 nodules and 26 nodule-adjacent tissues. Intraoperative frozen section was first performed to give accurate diagnosis, and the rest frozen specimen were pretreated for probe electrospray ionization mass measurement. By multivariate analysis of mass scan data, a series compounds were found downregulated in the extraction solution of papillary thyroid carcinoma (PTC), but some were found upregulated by mass spectrometry imaging. m/z 758.5713 ([PC[34:2] + H]⁺), m/z 772.5845 ([PC[32:0] + K]⁺), and m/z 786.6037 ([PC[36:2] + H]⁺) were firstly identified as potential biomarkers for nodular goiter (NG). Machine learning was employed by means of support vector machine (SVM) and random forest (RF) algorithms. For classification of PTC from NG, SVM and RF algorithms exhibited the same performance and the concordance was 94.2% and 94.4% between prediction and pathological diagnosis with positive and negative mass dataset, respectively. For the classification of PTC from PTC adjacent tissues, SVM was better than RF and the concordance was 93.8% and 83.3% with positive and negative mass dataset, respectively. With the identified compounds as training features, the sensitivity and specificity are 87.5% and 88.9% for the test set. The developed method could also correctly predict the malignancy of one medullary thyroid carcinoma and one adenomatous goiter (benign). The diagnosis time is about 10 min for one specimen, and it is very promising for the intraoperative diagnosis of papillary thyroid carcinoma.     

Expanded metabolomics approach to profiling endogenous carbohydrates in the serum of ovarian cancer patients

We applied hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry to the quantitative analysis of serum from 58 women, including ovarian cancer patients, ovarian benign tumor patients, and healthy controls. All of these ovarian cancer and ovarian benign tumor patients have elevated cancer antigen 125, which makes them clinically difficult to differentiate the malignant from the benign. All of the 16 endogenous carbohydrates were quantitatively detected in the human sera, of which, eight endogenous carbohydrates were significantly different (P‐value < 0.05) between the ovarian cancer and healthy control. According to the receiver operating characteristic curve analysis, arabitol was the most potentially specific biomarker for discriminating ovarian cancer from healthy control, having an area under the curve of 0.911. A panel of metabolite markers composed of maltose, maltotriose, raffinose, and mannitol was selected, which was able to discriminate the ovarian cancer from the benign ovarian tumor counterparts, with an area under concentration‐time curve value of 0.832. Endogenous carbohydrates in the expanded metabolomics approach after the global metabolic profiling are characterized and are potential biomarkers for the early diagnosis of ovarian cancer.     

On-tissue chemical derivatization enables spatiotemporal heterogeneity visualization of oxylipins in esophageal cancer xenograft via ambient mass spectrometry imaging

On-tissue chemical derivatization (OTCD) effectively enhances ionization efficiency of low abundant and poorly ionized functional molecules to improve detection sensitivity and coverage of mass spectrometry imaging (MSI). Combination OTCD and MSI provides a novel strategy for visualizing previously undisclosed metabolic heterogeneity in tumor. Herein, we present a method to visualize heterogeneous metabolism of oxylipins within tumor by coupling OTCD with airflow-assisted desorption electrospray ionization (AFADESI)-MSI. Taking Girard's P as a derivatization reagent, easily ionized hydrazide and quaternary amine groups were introduced into the structure of carbonyl metabolites via condensation reaction. Oxylipins, including 127 fatty aldehydes (FALs) and 71 oxo fatty acids (FAs), were detected and imaged in esophageal cancer xenograft with AFADESI-MSI after OTCD. Then t-distributed stochastic neighbor embedding and random forest were exploited to precisely locate the distribution of oxylipins in heterogeneous tumor tissue. With this method, we surprisingly found almost all FALs and oxo FAs significantly accumulated in the core region of tumor, and exhibited a gradual increase trend in tumor over time. These results reveal spatiotemporal heterogeneity of oxylipins in tumor progression, highlighting the value of OTCD combined with MSI to gain deeper insights into understanding tumor metabolism.     

Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery

Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single “super”-list reflective of the overall preference or importance within the population. This “super”-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable.     

Metabolomics: a revolution for novel cancer marker identification

The repertoire of small-molecular-weight substances present in cells, tissue and body fluids are known as the metabolites. The global analysis of metabolites, such as by high-resolution 1H nuclear magnetic resonance spectroscopy and mass spectrometry, is integral to the rapidly expanding field of metabolomics, which is making progress in various diseases. In the area of cancer and metabolic phenotype, the integrated analysis of metabolites may provide a powerful platform for detecting changes related to cancer diagnosis and discovering novel biomarkers. In this review, metabolomics including the technologies in metabolomics research and extracting information from metabolomics datasets are described. Then we discuss the challenges and opportunities in metabolomics for finding metabolic processes in cancer and discovering novel cancer biomarkers. Finally, we assess the clinical applicability of metabolomics.     

高频超声联合彩色多普勒超声对甲状腺良/恶性结节的诊断价值

本文探讨了高频超声联合彩色多普勒超声对甲状腺结节良/恶性的诊断价值.选择疑似甲状腺结节患者82例作为研究对象,本研究比较了高频超声和彩色多普勒超声与术后病理结果的差异;绘制了ROC曲线,分析了高频超声联合彩色多普勒超声对甲状腺结节良/恶性的诊断效能.结果发现,病理学检查与高频超声联合彩色多普勒超声检查相比较,差异不具有...     

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。     

Untargeted metabolomic study on the insomnia effect of Suan‐Zao‐Ren decoction in the rat serum and brain using ultra‐high‐performance liquid chromatography quadrupole time‐of‐flight mass spectrometry combined with data processing analysis

Insomnia is a common clinical disease that can seriously damage the normal lives of sufferers. Suan‐Zao‐Ren decoction has been used to treat insomnia for a long time. However, the underlying molecular mechanism of Suan‐Zao‐Ren decoction is still not clear. In this study, the nontargeted metabolomics based on high‐resolution mass spectrometry and multiple statistical approaches were initially used to investigate the changes of potential serum and brain biomarkers and metabolic pathways in the insomnia model rat. Principal component analysis‐discriminate analysis indicated that the Suan‐Zao‐Ren decoction treatment improved the metabolic phenotype insomnia. Moreover, the heatmap analysis identified the most important biomarkers involved in insomnia. According to the pathway analysis, phenylalanine metabolism, tryptophan metabolism, and so on were recognized as the most affected metabolic pathways associated with insomnia disease. These findings provided a comprehensive understanding of the regulative effects of Suan‐Zao‐Ren decoction on the host metabolic phenotype of the insomnia rats. Our work demonstrated that the metabolomics approach is a promising tool that could help us to conduct the exploration of the therapeutic effects and mechanism of traditional Chinese medicines.     

Urinary metabolomic study of systemic lupus erythematosus based on gas chromatography/mass spectrometry

Systemic lupus erythematosus (SLE) is an autoimmune disease with heterogeneous organ and system manifestations. In this study, urinary metabolic alterations related to SLE were investigated by performing gas chromatography/mass spectrometry (GC/MS) based metabolomics and multivariate statistical analysis. Patients with SLE and healthy controls could be clearly differentiated in view of the metabolic abnormity in urine. Among 70 identified endogenous metabolites, 23 metabolites were dramatically increased in SLE patients, which involved in several key metabolic pathways including energy metabolism, nucleotide metabolism, oxidative stress and gut‐microbiome‐derived metabolism. This noninvasive and GC/MS‐based metabolomic technique is a promising and potent strategy for identifying novel biomarkers and understanding pathogenesis of SLE. Copyright © 2016 John Wiley & Sons, Ltd.     

Microfluidic Screening of Circulating Tumor Biomarkers toward Liquid Biopsy

The development of early and personalized diagnostic protocol with rapid response and high accuracy is considered the most promising avenue to advance point-of-care testing for tumor diagnosis and therapy. Given the growing awareness of the limitations of conventional tissue biopsy for gathering tumor information, considerable interest has recently been aroused in liquid biopsy. Among a myriad of analytical approaches proposed for liquid biopsy, microfluidics-based separation and purification techniques possess merits of high throughput, low samples consumption, high flexibility, low cost, high sensitivity, automation capability and enhanced spatio-temporal control. These characteristics endow microfluidics to serve as an emerging and promising tool in tumor diagnosis and prognosis by identifying specific circulating tumor biomarkers. In this review, we will put our focus on three key categories of circulating tumor biomarkers, namely, circulating tumor cells (CTCs), circulating exosomes, and circulating nucleic acids (cNAs), and discuss the significant roles of microfluidics in the separation and analysis of circulating tumor biomarkers. Recent advances in microfluidic separation and analysis of CTCs, exosomes, and cNAs will be highlighted and tabulated. Finally, the current challenges and future niches of using microfluidic techniques in the separation and analysis of circulating tumor biomarkers will be discussed.     

Characterization of chemical constituents and absorbed components,screening the active components of gelanxinning capsule and an evaluation of therapeutic effects by ultra‐high performance liquid chromatography with quadrupole time of flight mass spectrometry

We revealed the potential biomarker and pathway of gelanxinning capsule on rat model with coronary heart disease, which aims to clarify holistic therapeutic effect and predict quality‐markers of gelanxinning capsule. Ultra‐high performance liquid chromatography coupled with mass spectrometry based on metabolomics technique was used to find the biomarkers and related metabolic pathways of coronary heart disease model, which evaluates the intervention effect of gelanxinning capsule. Using serum pharmacochemistry of traditional Chinese medicine and Pearson correlation analysis, effective ingredients in serum is analyzed to characterize the activity of gelanxinning capsule on coronary heart disease under valid state. A total of 20 biomarkers from coronary heart disease were identified and 12 of them were regulated by gelanxinning capsule treatment, which is mainly involved in sphingolipid metabolism and glycerophospholipid metabolism. With the high sensitivity liquid chromatography coupled with mass spectrometry technology, a total of 46 compounds from gelanxinning capsule were identified in vitro and 25 of them were absorbed in blood. The correlation analysis of serum biomarkers and absorbed components was used to find 11 compounds as quality‐markers to be responsible for the efficacy of gelanxinning capsule. This strategy was successfully applied to screening of potential mechanism and quality‐markers from herbal medicine.      

Urine Metabolic Fingerprints Encode Subtypes of Kidney Diseases

Metabolic fingerprints of biofluids encode diverse diseases and particularly urine detection offers complete non‐invasiveness for diagnostics of the future. Present urine detection affords unsatisfactory performance and requires advanced materials to extract molecular information, due to the limited biomarkers and high sample complexity. Herein, we report plasmonic polymer@Ag for laser desorption/ionization mass spectrometry (LDI‐MS) and sparse‐learning‐based metabolic diagnosis of kidney diseases. Using only 1 μL of urine without enrichment or purification, polymer@Ag afforded urine metabolic fingerprints (UMFs) by LDI‐MS in seconds. Analysis by sparse learning discriminated lupus nephritis from various other non‐lupus nephropathies and controls. We combined UMFs with urine protein levels (UPLs) and constructed a new diagnostic model to characterize subtypes of kidney diseases. Our work guides urine‐based diagnosis and leads to new personalized analytical tools for other diseases.     

基于肝脏代谢组分析研究低聚硒化氨基多糖对黑鲷的免疫调节作用

采用基于液相色谱-飞行时间质谱联用（LC-TOF-MS）技术的代谢组学方法，分析黑鲷肝脏内源性代谢物的变化，研究硒化氨基多糖增强黑鲷的免疫调节机制。采用XCMS^plus软件非靶向分析质谱采集数据，筛选潜在生物标志物，并通过MetaboAnalyst3.0网站分析相关代谢通路。结果表明，饲喂硒化氨基多糖组中的代谢物明显区分于空白组，发现并鉴定了32个有差异的生物标志物。代谢通路分析结果表明，硒化氨基多糖可通过氨基酰基-转运脱氧核糖核苷酸（tRNA）生物合成、氨基酸代谢、核苷酸代谢、氮代谢等代谢通路增强黑鲷自身的免疫机能。该研究为阐明硒化氨基多糖的免疫增强机制提供了科学依据。