Serum metabonomics study of pregnant women with gestational diabetes mellitus based on LC-MS

ObjectiveThrough metabolomics method, the objective of the paper is to differentially screen serum metabolites of GDM patients and healthy pregnant women, to explore potential biomarkers of GDM and analyze related pathways, and to explain the potential mechanism and biological significance of GDM.MethodsThe serum samples from 30 GDM patients and 30 healthy pregnant women were selected to conduct non-targeted metabolomics study by liquid chromatography-mass spectrometry. The differential metabolites between the two groups were searched and the metabolic pathway was analyzed by KEGG database.ResultsMultivariate statistical analysis found that serum metabolism in GDM patients was different significantly from healthy pregnant women, 36 differential metabolites and corresponding metabolic pathways were identified in serum, which involved several metabolic ways like, fatty acid metabolism, butyric acid metabolism, bile secretion, and amino acid metabolism.ConclusionThe discovery of these biomarkers provided a new theoretical basis and experimental basis for further study of the early diagnosis and pathogenesis of GDM. At the same time, LC-MS-based serum metabolomics methods also showed great application values in disease diagnosis and mechanism research.     

Plasma Metabolomic Profiling of Patients with Diabetes-Associated Cognitive Decline

Diabetes related cognitive dysfunction (DACD), one of the chronic complications of diabetes, seriously affect the quality of life in patients and increase family burden. Although the initial stage of DACD can lead to metabolic alterations or potential pathological changes, DACD is difficult to diagnose accurately. Moreover, the details of the molecular mechanism of DACD remain somewhat elusive. To understand the pathophysiological changes that underpin the development and progression of DACD, we carried out a global analysis of metabolic alterations in response to DACD. The metabolic alterations associated with DACD were first investigated in humans, using plasma metabonomics based on high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis. The related pathway of each metabolite of interest was searched in database online. The network diagrams were established KEGGSOAP software package. Receiver operating characteristic (ROC) analysis was used to evaluate diagnostic accuracy of metabolites. This is the first report of reliable biomarkers of DACD, which were identified using an integrated strategy. The identified biomarkers give new insights into the pathophysiological changes and molecular mechanisms of DACD. The disorders of sphingolipids metabolism, bile acids metabolism, and uric acid metabolism pathway were found in T2DM and DACD. On the other hand, differentially expressed plasma metabolites offer unique metabolic signatures for T2DM and DACD patients. These are potential biomarkers for disease monitoring and personalized medication complementary to the existing clinical modalities.     

基于UPLC-QTOFMS正、负离子模式对无偿献血者乙型肝炎表面抗原阳性血清代谢组学的研究

目的:基于超高效液相色谱-单四极杆飞行时间质谱(UPLC-QTOFMS)正、负离子模式探讨无偿献血者中乙型肝炎表面抗原阳性和乙型肝炎表面抗原阴性的血清代谢组学的差异,为乙型肝炎的诊断寻找潜在的血清生物标志物。方法:选取2017年10月~2018年1月在青海省血液中心检测的乙型肝炎表面抗原阳性57例(研究组)与同期无偿献血者乙型肝炎表面抗原阴性63例(对照组),利用UPLC-QTOFMS技术建立两组血清代谢指纹图谱,采用主成分分析(PCA)和偏最小二乘法-判别分析(PLS-DA)分析两组间有差异的小分子物质,确定与乙型肝炎相关的生物标志物,并分析相关代谢机制。结果:通过变量重要性投影、质谱鉴定和数据库检索筛选出8个潜在的生物标志物,分别为缬氨酸、胆碱、甘氨鹅去氧胆酸、肉毒碱、高丝氨酸、溶血磷脂酰胆碱、血清溶菌酶和花生四烯酸,涉及胆汁酸代谢、氨基酸代谢、磷脂代谢等。结论:无偿献血者中乙型肝炎表面抗原阳性和乙型肝炎表面抗原阴性的血清代谢物存在显著差异,差异代谢物的发现有助于寻找乙型肝炎的潜在生物标志物,为血液安全提供依据。     

Metabonomic studies on potential plasma biomarkers in rats exposed to ionizing radiation and the protective effects of Hong Shan Capsule

An ultra performance liquid chromatography coupled to mass spectrometry-based metabonomic approach, combined with pattern recognition methods including PCA, PLS-DA, RF and heatmap, has been developed to characterize the global serum metabolic profile associated with ionizing radiation (IR). As the VIP-value threshold cutoff of the metabolites was set to 2, metabolites above this threshold were filtered out as potential target biomarkers. Nineteen distinct potential biomarkers in rat plasma were identified. To further elucidate the pathophysiology of IR, related metabolic pathways have been studied. It was found that IR was closely related to disturbed fatty acid metabolism, taurine and hypotaurine metabolism, sphingolipid metabolism, purine metabolism, pyrimidine metabolism, phospholipid catabolism, tryptophan metabolism, phenylalanine metabolism, and bile acid metabolism. With the presented metabonomic method, we systematically analyzed the protective effects of Traditional Chinese Medicine Hong Shan Capsule (HSC). The results demonstrated that HSC administration could provide satisfactory effects on IR through partially regulating the perturbed metabolic pathways.     

The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes

Understanding the molecular mechanisms underlying complex phenotypes requires systematic analyses of complicated metabolic networks and contributes to improvements in the breeding efficiency of staple cereal crops and diagnostic accuracy for human diseases. Here, we selected rice (Oryza sativa) heterosis as a complex phenotype and investigated the mechanisms of both vegetative and reproductive traits using an untargeted metabolomics strategy. Heterosis-associated analytes were identified, and the overlapping analytes were shown to underlie the association patterns for six agronomic traits. The heterosis-associated analytes of four yield components and plant height collectively contributed to yield heterosis, and the degree of contribution differed among the five traits. We performed dysregulated network analyses of the high- and low-better parent heterosis hybrids and found multiple types of metabolic pathways involved in heterosis. The metabolite levels of the significantly enriched pathways (especially those from amino acid and carbohydrate metabolism) were predictive of yield heterosis (area under the curve = 0.907 with 10 features), and the predictability of these pathway biomarkers was validated with hybrids across environments and populations. Our findings elucidate the metabolomic landscape of rice heterosis and highlight the potential application of pathway biomarkers in achieving accurate predictions of complex phenotypes.

Specific metabolic pathways (especially those from amino acid and carbohydrate metabolism) underlie heterosis of six agronomic traits in rice.     

Predicting metabolic biomarkers of human inborn errors of metabolism

Early diagnosis of inborn errors of metabolism is commonly performed through biofluid metabolomics, which detects specific metabolic biomarkers whose concentration is altered due to genomic mutations. The identification of new biomarkers is of major importance to biomedical research and is usually performed through data mining of metabolomic data. After the recent publication of the genome‐scale network model of human metabolism, we present a novel computational approach for systematically predicting metabolic biomarkers in stochiometric metabolic models. Applying the method to predict biomarkers for disruptions of red‐blood cell metabolism demonstrates a marked correlation with altered metabolic concentrations inferred through kinetic model simulations. Applying the method to the genome‐scale human model reveals a set of 233 metabolites whose concentration is predicted to be either elevated or reduced as a result of 176 possible dysfunctional enzymes. The method's predictions are shown to significantly correlate with known disease biomarkers and to predict many novel potential biomarkers. Using this method to prioritize metabolite measurement experiments to identify new biomarkers can provide an order of a 10‐fold increase in biomarker detection performance.     

Serum metabolomics analysis of patients with polycystic ovary syndrome by mass spectrometry

Polycystic ovary syndrome (PCOS) is a set of symptoms caused by elevated androgens (male hormones) in females. PCOS is the most common endocrine disorder among women between 18 and 44 years. Currently, the pathogenesis of PCOS remains unclear. Liquid chromatography–mass spectrometry (LC/MS)‐based metabolomics is becoming more and more useful for medical research, especially in revealing the mechanism of the disease. The aim of this study was to investigate the difference of serum metabolic profiles in patients with PCOS and healthy control to better understand the mechanism of this disease. Ten patients with PCOS and 10 healthy people were recruited for this study. The serum samples were collected for LC/MS analysis. Multivariate statistical analysis was performed to discover and identify the potential biomarkers. Six biomarkers were found and identified. The biomarkers belonged to different metabolic pathway including lipid metabolism, carnitine metabolism, androgen metabolism, and bile acid metabolism. Those biomarkers also played different roles in disease progression. Metabolomics is a powerful tool used in research of the mechanism involved in this disease to provide useful information for better understanding of PCOS.     

A multivariate screening strategy for investigating metabolic effects of strenuous physical exercise in human serum

A novel hypothesis-free multivariate screening methodology for the study of human exercise metabolism in blood serum is presented. Serum gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) data was processed using hierarchical multivariate curve resolution (H-MCR), and orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to model the systematic variation related to the acute effect of strenuous exercise. Potential metabolic biomarkers were identified using data base comparisons. Extensive validation was carried out including predictive H-MCR, 7-fold full cross-validation, and predictions for the OPLS-DA model, variable permutation for highlighting interesting metabolites, and pairwise t tests for examining the significance of metabolites. The concentration changes of potential biomarkers were verified in the raw GC/TOFMS data. In total, 420 potential metabolites were resolved in the serum samples. On the basis of the relative concentrations of the 420 resolved metabolites, a valid multivariate model for the difference between pre- and post-exercise subjects was obtained. A total of 34 metabolites were highlighted as potential biomarkers, all statistically significant (p < 8.1E-05). As an example, two potential markers were identified as glycerol and asparagine. The concentration changes for these two metabolites were also verified in the raw GC/TOFMS data.The strategy was shown to facilitate interpretation and validation of metabolic interactions in human serum as well as revealing the identity of potential markers for known or novel mechanisms of human exercise physiology. The multivariate way of addressing metabolism studies can help to increase the understanding of the integrative biology behind, as well as unravel new mechanistic explanations in relation to, exercise physiology.     

Serum metabolic profiling of abnormal savda by liquid chromatography/mass spectrometry

Abnormal savda is a special symptom in Uigur medicine. The understanding of its metabolic origins is of great importance for the subsequent treatment. Here, a metabonomic study of this symptom was carried out using LC-MS based human serum metabolic profiling. Orthogonal signal correction partial least-squares discriminant analysis (OSC-PLS-DA) was used for the classification and prediction of abnormal savda. Potential biomarkers from metabonomics were also identified for a metabolic understanding of abnormal savda. As a result, our OSC-PLS-DA model had a satisfactory ability for separation and prediction of abnormal savda. The potential biomarkers including bilirubin, bile acids, tryptophan, phenylalanine and lyso-phosphatidylcholines indicated that abnormal savda could be related to some abnormal metabolisms within the body, including energy metabolism, absorption of nutrition, metabolism of lecithin on cell membrane, etc. To the best of our knowledge, this is the first study of abnormal savda based on serum metabolic profiling. The LC/MS-based metabonomic platform could be a powerful tool for the classification of symptoms and for the development of this traditional medicine into an evidence-based one.     

Effects of the Suxiao Jiuxin pill on acute myocardial infarction assessed by comprehensive metabolomics

BackgroundSJP is the commercial Chinese medicine included in the Chinese Pharmacopoeia, with well-established cardiovascular protective effects in the clinic. However, the mechanisms underlying the protective effects of SJP on cardiovascular disease have not yet been clearly elucidated.AimsTo investigate the underlying protective mechanisms of SJP in an acute myocardial infarction (AMI) rat model using comprehensive metabolomics.Materials and methodsThe rat model of AMI was generated by ligating the left anterior descending coronary artery. After 2 weeks treatment with SJP, the entire metabolic changes in the serum, heart, urine and feces of the rat were profiled by HPLC-QTOF-MS/MS.ResultsThe metabolic profiles in different biological samples (heart, serum, urine and feces) were significantly different among groups, in which a total of 112 metabolites were identified. AMI caused comprehensive metabolic changes in amino acid metabolism, galactose metabolism and fatty acid metabolism, while SJP reversed more than half of the differential metabolic changes, mainly affecting amino acid metabolism and fatty acid metabolism. Correlation analysis found that SJP could significantly alter the metabolic activity of 12 key metabolites, regarded as potential biomarkers of SJP treatment. According to the results of network analysis, 6 biomarkers were considered to be hub metabolites, which means that these metabolites may have a major relationship with the SJP therapeutic effects on AMI.ConclusionThe combined comprehensive metabolomics and network analysis, indicated that the protective effect of SJP on cardiovascular disease was associated with systemic metabolic modulation, in particular regulation of amino acid and fatty acid metabolism.     

Genome-scale modeling of human metabolism – a systems biology approach

Altered metabolism is linked to the appearance of various human diseases and a better understanding of disease-associated metabolic changes may lead to the identification of novel prognostic biomarkers and the development of new therapies. Genome-scale metabolic models (GEMs) have been employed for studying human metabolism in a systematic manner, as well as for understanding complex human diseases. In the past decade, such metabolic models – one of the fundamental aspects of systems biology – have started contributing to the understanding of the mechanistic relationship between genotype and phenotype. In this review, we focus on the construction of the Human Metabolic Reaction database, the generation of healthy cell type- and cancer-specific GEMs using different procedures, and the potential applications of these developments in the study of human metabolism and in the identification of metabolic changes associated with various disorders. We further examine how in silico genome-scale reconstructions can be employed to simulate metabolic flux distributions and how high-throughput omics data can be analyzed in a context-dependent fashion. Insights yielded from this mechanistic modeling approach can be used for identifying new therapeutic agents and drug targets as well as for the discovery of novel biomarkers. Finally, recent advancements in genome-scale modeling and the future challenge of developing a model of whole-body metabolism are presented. The emergent contribution of GEMs to personalized and translational medicine is also discussed.     

Exploiting tumor metabolism for non-invasive imaging of the therapeutic activity of molecularly targeted anticancer agents

Rational drug discovery and development requires biomarkers to inform on target modulation and treatment efficacy. Many aspects of metabolism are altered in cancer, compared to normal tissues, and are often regulated by oncogene activation. Non-invasive imaging of spatio-temporal effects of molecularly targeted anticancer agents on tumor metabolism has considerable potential in the development and use of personalized molecular medicine approaches to cancer treatment. Here we describe how non-invasive monitoring of metabolism, using primarily magnetic resonance spectroscopy (MRS), can be used to follow treatment with novel molecularly targeted anticancer agents. We discuss how the regulation of metabolic pathways by oncogenic signaling can affect MRS-detectable metabolic signals together with the physiological factors that can influence the measured changes. Finally, the translation of these metabolic measurements from pre-clinical models to patients will be discussed.     

Metabolic signatures of esophageal cancer: NMR-based metabolomics and UHPLC-based focused metabolomics of blood serum

Focused metabolic profiling is a powerful tool for the determination of biomarkers. Here, a more global proton nuclear magnetic resonance (¹H NMR)-based metabolomic approach coupled with a relative simple ultra high performance liquid chromatography (UHPLC)-based focused metabolomic approach was developed and compared to characterize the systemic metabolic disturbances underlying esophageal cancer (EC) and identify possible early biomarkers for clinical prognosis. Serum metabolic profiling of patients with EC (n = 25) and healthy controls (n = 25) was performed by using both ¹H NMR and UHPLC, and metabolite identification was achieved by multivariate statistical analysis. Using orthogonal projection to least squares discriminant analysis (OPLS-DA), we could distinguish EC patients from healthy controls. The predictive power of the model derived from the UHPLC-based focused metabolomics performed better in both sensitivity and specificity than the results from the NMR-based metabolomics, suggesting that the focused metabolomic technique may be of advantage in the future for the determination of biomarkers. Moreover, focused metabolic profiling is highly simple, accurate and specific, and should prove equally valuable in metabolomic research applications. A total of nineteen significantly altered metabolites were identified as the potential disease associated biomarkers. Significant changes in lipid metabolism, amino acid metabolism, glycolysis, ketogenesis, tricarboxylic acid (TCA) cycle and energy metabolism were observed in EC patients compared with the healthy controls. These results demonstrated that metabolic profiling of serum could be useful as a screening tool for early EC diagnosis and prognosis, and might enhance our understanding of the mechanisms involved in the tumor progression.     

Sex differences in global metabolomic profiles of COVID-19 patients

Coronavirus disease (COVID-19), caused by SARS-CoV-2, leads to symptoms ranging from asymptomatic disease to death. Although males are more susceptible to severe symptoms and higher mortality due to COVID-19, patient sex has rarely been examined. Sex-associated metabolic changes may implicate novel biomarkers and therapeutic targets to treat COVID-19. Here, using serum samples, we performed global metabolomic analyses of uninfected and SARS-CoV-2-positive male and female patients with severe COVID-19. Key metabolic pathways that demonstrated robust sex differences in COVID-19 groups, but not in controls, involved lipid metabolism, pentose pathway, bile acid metabolism, and microbiome-related metabolism of aromatic amino acids, including tryptophan and tyrosine. Unsupervised statistical analysis showed a profound sexual dimorphism in correlations between patient-specific clinical parameters and their global metabolic profiles. Identification of sex-specific metabolic changes in severe COVID-19 patients is an important knowledge source for researchers striving for development of potential sex-associated biomarkers and druggable targets for COVID-19 patients.Subject terms: Metabolomics, Immunological disorders     

A Comparative Metabolomic Evaluation of Behcet’s Disease with Arthritis and Seronegative Arthritis Using Synovial Fluid

Behcet’s disease (BD) with arthritis is often confused with seronegative arthritis (SNA) because of shared clinical symptoms and the lack of definitive biomarkers for BD. To investigate possible metabolic patterns and potential biomarkers of BD with arthritis, metabolomic profiling of synovial fluid (SF) from 6 patients with BD with arthritis and 18 patients with SNA was performed using gas chromatography/time-of-flight mass spectrometry in conjunction with univariate and multivariate statistical analyses. A total of 123 metabolites were identified from samples. Orthogonal partial least square-discriminant analysis showed clear discrimination between BD with arthritis and SNA. A set of 11 metabolites were identified as potential biomarkers for BD using variable importance for projection values and the Wilcoxon-Mann-Whitney test. Compared with SNA, BD with arthritis exhibited relatively high levels of glutamate, valine, citramalate, leucine, methionine sulfoxide, glycerate, phosphate, lysine, isoleucine, urea, and citrulline. There were two markers identified, elevated methionine sulfoxide and citrulline, that were associated with increased oxidative stress, providing a potential link to BD-associated neutrophil hyperactivity. Glutamate, citramalate, and valine were selected and validated as putative biomarkers for BD with arthritis (sensitivity, 100%; specificity, 61.1%). This is the first report to present potential biomarkers from SF for discriminating BD with arthritis from SNA. The metabolomics of SF may be helpful in searching for potential biomarkers and elucidating the clinicopathogenesis of BD with arthritis.     

1H NMR Based Serum Metabolic Profiles Associated with Pathological Progression of Pancreatic Islet β Cell Tumor in Rip1-Tag2 Mice

Pancreatic islet β cell tumor is the most common islet cell tumor. A well-characterized tumor progression in Rip1-Tag2 mice undergoes five stages, involving normal, hyperplasia, angiogenic islets, tumorigenesis and invasive carcinoma. ¹H NMR based metabonomics was applied to identify potential biomarkers for monitoring pancreatic islet β cell tumor progression in Rip1-Tag2 mice. Multivariate analysis results showed the serum metabonome at hyperplasia stage shared the similar characteristics with the ones at normal stage as a result of slight proliferation of pancreatic islet β cells. At angiogenic islets stage, the up-regulated glycolysis, disturbed choline and phospholipid metabolism composed the metabolic signature. In addition to the changes mentioned above, several metabolites were identified as early biomarkers for tumorigenesis, including increased methionine, citrate and choline, and reduced acetate, taurine and glucose, which suggested the activated energy and amino acid metabolism. All the changes were aggravated at invasive carcinoma stage, coupled with notable changes in alanine, glutamate and glycine. Moreover, the distinct metabolic phenotype was found associated with the implanting of SV40 large T antigen in Rip1-Tag2 mice. The combined metabolic and multivariate statistics approach provides a robust method for screening the biomarkers of disease progression and examining the association between gene and metabolism.     

Hydrophilic interaction and reversed-phase ultraperformance liquid chromatography TOF-MS for serum metabonomic analysis of myocardial infarction in rats and its applications

An ultra performance liquid chromatography coupled to mass spectrometry-based metabonomic approach, which utilizes both reversed-performance (RP) chromatography and hydrophilic interaction chromatography (HILIC) separations, has been developed to characterize the global serum metabolic profile associated with myocardial infarction (MI). The HILIC was found necessary for a comprehensive serum metabonomic profiling, providing complementary information to RP chromatography. By combining with partial least squares discriminant analysis, 21 potential biomarkers in rat serum were identified. To further elucidate the pathophysiology of MI, related metabolic pathways have been studied. It was found that MI was closely related to disturbed sphingolipid metabolism, phospholipid catabolism, fatty acid transportation and metabolism, tryptophan metabolism, branched-chain amino acids metabolism, phenylalanine metabolism, and arginine and proline metabolism. With the presented metabonomic method, we systematically analyzed the therapeutic effects of Traditional Chinese Medicine Sini decoction (SND). The results demonstrated that SND administration could provide satisfactory effects on MI through partially regulating the perturbed metabolic pathways.     

Metabolic characterization of hepatitis B virus-related liver cirrhosis using NMR-based serum metabolomics

Introduction

Liver cirrhosis (LC) is an advanced liver disease that can develop into hepatocellular carcinoma. Hepatitis B virus (HBV) infection is one of the main causes of LC. Therefore, there is an urgent need for developing a new method to monitor the progression of HBV-related LC (HBV-LC).

Objectives

In this study, we attempted to examine serum metabolic changes in healthy individuals as well as patients with HBV and HBV-LC. Furthermore, potential metabolite biomarkers were identified to evaluate patients progressed from health to HBV-LC.

Methods

Metabolic profiles in the serum of healthy individuals as well as patients with HBV and HBV-LC were detected using an NMR-based metabolomic approach. Univariate and multivariate analyses were conducted to analyze serum metabolic changes during HBV-LC progression. Moreover, potential metabolite biomarkers were explored by receiver operating characteristic curve analysis.

Results

Serum metabolic changes were closely associated with the progression of HBV-LC, mainly involving energy metabolism, protein metabolism, lipid metabolism and microbial metabolism. Serum histidine was identified as a potential biomarker for HBV patients. Acetate, formate, pyruvate and glutamine in the serum were identified as a potential biomarker panel for patients progressed from HBV to HBV-LC. In addition, phenylalanine, unsaturated lipid, n-acetylglycoprotein and acetone in the serum could be considered as a potential common biomarkers panel for these patients.

Conclusion

NMR-based serum metabolomic approach could be a promising tool to monitor the progression of liver disease. Different metabolites may reflect different stages of liver disease.

     

Global Metabolomic Profiling of Acute Myocarditis Caused by Trypanosoma cruzi Infection

Chagas disease is caused by Trypanosoma cruzi infection, being cardiomyopathy the more frequent manifestation. New chemotherapeutic drugs are needed but there are no good biomarkers for monitoring treatment efficacy. There is growing evidence linking immune response and metabolism in inflammatory processes and specifically in Chagas disease. Thus, some metabolites are able to enhance and/or inhibit the immune response. Metabolite levels found in the host during an ongoing infection could provide valuable information on the pathogenesis and/or identify deregulated metabolic pathway that can be potential candidates for treatment and being potential specific biomarkers of the disease. To gain more insight into those aspects in Chagas disease, we performed an unprecedented metabolomic analysis in heart and plasma of mice infected with T. cruzi. Many metabolic pathways were profoundly affected by T. cruzi infection, such as glucose uptake, sorbitol pathway, fatty acid and phospholipid synthesis that were increased in heart tissue but decreased in plasma. Tricarboxylic acid cycle was decreased in heart tissue and plasma whereas reactive oxygen species production and uric acid formation were also deeply increased in infected hearts suggesting a stressful condition in the heart. While specific metabolites allantoin, kynurenine and p-cresol sulfate, resulting from nucleotide, tryptophan and phenylalanine/tyrosine metabolism, respectively, were increased in heart tissue and also in plasma. These results provide new valuable information on the pathogenesis of acute Chagas disease, unravel several new metabolic pathways susceptible of clinical management and identify metabolites useful as potential specific biomarkers for monitoring treatment and clinical severity in patients.