Potential drug targets on insomnia and intervention effects of Jujuboside A through metabolic pathway analysis as revealed by UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition approach

Potential metabolites from the metabolic pathways could be therapeutic targets and useful for the discovery of broad spectrum drugs. UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition methods including PCA, PLS-DA, OPLS-DA and Heatmap were integrated to examine the global metabolic signature of insomnia and intervention effects of Jujuboside A (JuA). Six unique pathways of the insomnia were identified using Ingenuity Pathway Analysis (IPA) software. The VIP-value threshold cutoff of the metabolites was set to 10, above this threshold, were filtered out as potential target biomarkers. Sixteen distinct metabolites were identified from these pathways, and 6 of them can be considered for rational drug design. It was further experimental validation that the changes in metabolic profiling were restored to their baseline values after JuA treatment according to the multivariate data analysis. Potential metabolite network of the insomnia was preliminarily predicted JuA-target interaction networks, and could be further explored for in silico docking studies with suitable drugs. Thus, our method is an efficient procedure for drug target identification through metabolic analysis. It can guide testable predictions, provide insights into drug action mechanisms and enable us to increase research productivity toward metabolomic drug discovery.     

Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery

Background

Previous metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers.

Objective

The aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures.

Methods

Thirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography–mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers.

Results

Global metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, l-beta-aspartyl-l-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964.

Conclusion

Plasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.

     

Proton NMR-Based Metabolite Analyses of Archived Serial Paired Serum and Urine Samples from Myeloma Patients at Different Stages of Disease Activity Identifies Acetylcarnitine as a Novel Marker of Active Disease

Background

Biomarker identification is becoming increasingly important for the development of personalized or stratified therapies. Metabolomics yields biomarkers indicative of phenotype that can be used to characterize transitions between health and disease, disease progression and therapeutic responses. The desire to reproducibly detect ever greater numbers of metabolites at ever diminishing levels has naturally nurtured advances in best practice for sample procurement, storage and analysis. Reciprocally, since many of the available extensive clinical archives were established prior to the metabolomics era and were not processed in such an ‘ideal’ fashion, considerable scepticism has arisen as to their value for metabolomic analysis. Here we have challenged that paradigm.

Methods

We performed proton nuclear magnetic resonance spectroscopy-based metabolomics on blood serum and urine samples from 32 patients representative of a total cohort of 1970 multiple myeloma patients entered into the United Kingdom Medical Research Council Myeloma IX trial.

Findings

Using serial paired blood and urine samples we detected metabolite profiles that associated with diagnosis, post-treatment remission and disease progression. These studies identified carnitine and acetylcarnitine as novel potential biomarkers of active disease both at diagnosis and relapse and as a mediator of disease associated pathologies.

Conclusions

These findings show that samples conventionally processed and archived can provide useful metabolomic information that has important implications for understanding the biology of myeloma, discovering new therapies and identifying biomarkers potentially useful in deciding the choice and application of therapy.     

Ingenuity pathways analysis of urine metabolomics phenotypes toxicity of Chuanwu in Wistar rats by UPLC-Q-TOF-HDMS coupled with pattern recognition methods

Chuanwu (CW), a valuable traditional Chinese medicine (TCM), is the mother root of Aconitum carmichaelii Debx. The cause of CW-induced toxicity is still under ongoing research, although this is limited by the lack of sensitive and reliable biomarkers. Ingenuity pathway analysis (IPA) was performed to analyzing global metabolomics in order to characterize the phenotypically biochemical perturbations and potential mechanisms of the CW-induced toxicity. CW was administered to Wistar rats (0.027 g/200 g and 0.108 g/200 g bw, oral) for 6 months and urine samples were collected. The urinary metabolomics was performed by UPLC-Q-TOF-HDMS, and the mass spectra signals of the detected metabolites were systematically deconvoluted and analyzed by pattern recognition methods (PCA, PLS-DA, and OPLS-DA), revealing a time- and dose-dependency of the biochemical perturbations induced by CW toxicity. As a result, several metabolites responsible for pentose and glucuronate interconversions, alanine, aspartate and glutamate metabolism, starch and sucrose metabolism, amino sugar and nucleotide sugar metabolism, purine metabolism, tryptophan metabolism, taurine and hypotaurine metabolism, fructose and mannose metabolism, fatty acid metabolism were characterized, and it was confirmed that biochemical perturbations can be foreseen from these biomarkers. The urinary metabolomics based IPA with pattern recognition methods also revealed that CW produced serious heart and liver toxicity, consistent with clinical biochemistry and histopathology. Significant changes of 17 metabolites were identified and validated as phenotypic biomarkers of CW toxicity. Overall, our work demonstrated the metabolomics has brought enormous opportunities for improved detection of toxicity and biomarker discovery, highlighting the powerful predictive potential of the IPA to study of drug toxicity.     

Metabolomic characterization of hypertension and dyslipidemia

Background

Hypertension and dyslipidemia are two main risk factors for cardiovascular diseases (CVD). Moreover, their coexistence predisposes individuals to a considerably increased risk of CVD. However, the regulatory mechanisms involved in hypertension and dyslipidemia as well as their interactions are incompletely understood.

Objectives

The aims of our study were to identify metabolic biomarkers and pathways for hypertension and dyslipidemia, and compare the metabolic patterns between hypertension and dyslipidemia.

Methods

In this study, we performed metabolomic investigations into hypertension and dyslipidemia based on a “healthy” UK population. Metabolomic data from the Husermet project were acquired by gas chromatography–mass spectrometry and ultra-performance liquid chromatography–mass spectrometry. Both univariate and multivariate statistical methods were used to facilitate biomarker selection and pathway analysis.

Results

Serum metabolic signatures between individuals with and without hypertension or dyslipidemia exhibited considerable differences. Using rigorous selection criteria, 26 and 46 metabolites were identified as potential biomarkers of hypertension and dyslipidemia respectively. These metabolites, mainly involved in fatty acid metabolism, glycerophospholipid metabolism, alanine, aspartate and glutamate metabolism, are implicated in insulin resistance, vascular remodeling, macrophage activation and oxidised LDL formation. Remarkably, hypertension and dyslipidemia exhibit both common and distinct metabolic patterns, revealing their independent and synergetic biological implications.

Conclusion

This study identified valuable biomarkers and pathways for hypertension and dyslipidemia, and revealed common and different metabolic patterns between hypertension and dyslipidemia. The information provided in this study could shed new light on the pathologic mechanisms and offer potential intervention targets for hypertension and dyslipidemia as well as their related diseases.

     

Integrative Metabolic Signatures for Hepatic Radiation Injury

Background

Radiation-induced liver disease (RILD) is a dose-limiting factor in curative radiation therapy (RT) for liver cancers, making early detection of radiation-associated liver injury absolutely essential for medical intervention. A metabolomic approach was used to determine metabolic signatures that could serve as biomarkers for early detection of RILD in mice.

Methods

Anesthetized C57BL/6 mice received 0, 10 or 50 Gy Whole Liver Irradiation (WLI) and were contrasted to mice, which received 10 Gy whole body irradiation (WBI). Liver and plasma samples were collected at 24 hours after irradiation. The samples were processed using Gas Chromatography/Mass Spectrometry and Liquid Chromatography/Mass Spectrometry.

Results

Twenty four hours after WLI, 407 metabolites were detected in liver samples while 347 metabolites were detected in plasma. Plasma metabolites associated with 50 Gy WLI included several amino acids, purine and pyrimidine metabolites, microbial metabolites, and most prominently bradykinin and 3-indoxyl-sulfate. Liver metabolites associated with 50 Gy WLI included pentose phosphate, purine, and pyrimidine metabolites in liver. Plasma biomarkers in common between WLI and WBI were enriched in microbial metabolites such as 3 indoxyl sulfate, indole-3-lactic acid, phenyllactic acid, pipecolic acid, hippuric acid, and markers of DNA damage such as 2-deoxyuridine. Metabolites associated with tryptophan and indoles may reflect radiation-induced gut microbiome effects. Predominant liver biomarkers in common between WBI and WLI were amino acids, sugars, TCA metabolites (fumarate), fatty acids (lineolate, n-hexadecanoic acid) and DNA damage markers (uridine).

Conclusions

We identified a set of metabolomic markers that may prove useful as plasma biomarkers of RILD and WBI. Pathway analysis also suggested that the unique metabolic changes observed after liver irradiation was an integrative response of the intestine, liver and kidney.     

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.     

Targeted metabolomic profiling of low and high grade serous epithelial ovarian cancer tissues: a pilot study

Introduction

Epithelial ovarian cancer (EOC) remains the leading cause of death from gynecologic malignancies and has an alarming global fatality rate. Besides the differences in underlying pathogenesis, distinguishing between high grade (HG) and low grade (LG) EOC is imperative for the prediction of disease progression and responsiveness to chemotherapy.

Objectives

The aim of this study was to investigate, the tissue metabolome associated with HG and LG serous epithelial ovarian cancer.

Methods

A combination of one dimensional proton nuclear magnetic resonance (1D H NMR) spectroscopy and targeted mass spectrometry (MS) was employed to profile the tissue metabolome of HG, LG serous EOCs, and controls.

Results

Using partial least squares-discriminant analysis, we observed significant separation between all groups (p?<?0.05) following cross validation. We identified which metabolites were significantly perturbed in each EOC grade as compared with controls and report the biochemical pathways which were perturbed due to the disease. Among these metabolic pathways, ascorbate and aldarate metabolism was identified, for the first time, as being significantly altered in both LG and HG serous cancers. Further, we have identified potential biomarkers of EOC and generated predictive algorithms with AUC (CI)?=?0.940 and 0.929 for HG and LG, respectively.

Conclusion

These previously unreported biochemical changes provide a framework for future metabolomic studies for the development of EOC biomarkers. Finally, pharmacologic targeting of the key metabolic pathways identified herein could lead to novel and effective treatments of EOC.

     

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.     

Metabolomic analysis of biochemical changes in the plasma and urine of first-episode neuroleptic-naïve schizophrenia patients after treatment with risperidone

Early findings propose that impaired neurotransmission in the brain plays a key role in the pathophysiology of schizophrenia. Recent advances in understanding its multiple etiologies and pathogenetic mechanisms provide more speculative hypotheses focused on even broader somatic systems. Using a targeted tandem mass spectrometry (MS/MS)-based metabolomic platform, we compared metabolic signatures consisting of monoamine and amino acid neurotransmitter (NT) metabolites in plasma/urine simultaneously between first-episode neuroleptic-na?ve schizophrenia patients (FENNS) and healthy controls before and after a 6-week risperidone monotherapy, which suggest that the patient NT profiles are restoring during treatment. To detect and identify potential biomarkers associated with schizophrenia and risperidone treatment, we also performed a combined ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) and (1)H nuclear magnetic resonance (NMR)-based metabolomic profiling of the same samples, indicating a further deviation of the patients' global metabolic profile from that of controls. The NTs and their metabolites together with the 32 identified biomarkers underpin that metabolic pathways including NT metabolism, amino acid metabolism, glucose metabolism, lipid metabolism, energy metabolism, antioxidant defense system, bowel microflora and endocrine system are disturbed in FENNS. Among them, pregnanediol, citrate and α-ketoglutarate (α-KG) were significantly associated with symptomatology of schizophrenia after Bonferroni correction and may be useful biomarkers for monitoring therapeutic efficacy. These findings promise to yield valuable insights into the pathophysiology of schizophrenia and may advance the approach to treatment, diagnosis and disease prevention of schizophrenia and related syndromes.     

Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease

Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value≥0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms.     

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     

Investigation of altered urinary metabolomic profiles of invasive ductal carcinoma of breast using targeted and untargeted approaches

Introduction

Invasive ductal carcinoma (IDC) is a type of breast cancer, usually detected in advanced stages due to its asymptomatic nature which ultimately leads to low survival rate. Identification of urinary metabolic adaptations induced by IDC to understand the disease pathophysiology and monitor therapy response would be a helpful approach in clinical settings. Moreover, its non-invasive and cost effective strategy better suited to minimize apprehension among high risk population.

Objective

This study aims toward investigating the urinary metabolic alterations of IDC by targeted (LC-MRM/MS) and untargeted (GC–MS) approaches for the better understanding of the disease pathophysiology and monitoring therapy response.

Methods

Urinary metabolic alterations of IDC subjects (63) and control subjects (63) were explored by targeted (LC-MRM/MS) and untargeted (GC–MS) approaches. IDC specific urinary metabolomics signature was extracted by applying both univariate and multivariate statistical tools.

Results

Statistical analysis identified 39 urinary metabolites with the highest contribution to metabolomic alterations specific to IDC. Out of which, 19 metabolites were identified from targeted LC-MRM/MS analysis, while 20 were identified from the untargeted GC–MS analysis. Receiver operator characteristic (ROC) curve analysis evidenced 6 most discriminatory metabolites from each type of approach that could differentiate between IDC subjects and controls with higher sensitivity and specificity. Furthermore, metabolic pathway analysis depicted several dysregulated pathways in IDC including sugar, amino acid, nucleotide metabolism, TCA cycle etc.

Conclusions

Overall, this study provides valuable inputs regarding altered urinary metabolites which improved our knowledge on urinary metabolomic alterations induced by IDC. Moreover, this study identified several dysregulated metabolic pathways which offer further insight into the disease pathophysiology.

     

Comprehensive plasma metabolomic and lipidomic analyses reveal potential biomarkers for heart failure

Heart failure is a syndrome with symptoms or signs caused by cardiac dysfunction. In clinic, four stages (A, B, C, and D) were used to describe heart failure progression. This study was aimed to explore plasma metabolomic and lipidomic profiles in different HF stages to identify potential biomarkers. Metabolomics and lipidomics were performed using plasma of heart failure patients at stages A (n?=?49), B (n?=?61), and C+D (n?=?26). Analysis of Variance (ANOVA) was used for screening dysregulated molecules. Bioinformatics was used to retrieve perturbed metabolic pathways. Univariate and multivariate receiver operating characteristic curve (ROC) analyses were used for potential biomarker screening. Stage A showed significant difference to other stages, and 142 dysregulated lipids and 134 dysregulated metabolites were found belonging to several metabolic pathways. Several marker panels were proposed for the diagnosis of heart failure stage A versus stage B-D. Several molecules, including lysophosphatidylcholine 18:2, cholesteryl ester 18:1, alanine, choline, and Fructose, were found correlated with B-type natriuretic peptide or left ventricular ejection fractions. In summary, using untargeted metabolomic and lipidomic profiling, several dysregulated small molecules were successfully identified between HF stages A and B-D. These molecules would provide valuable information for further pathological researches and biomarker development.

     

NMR-based metabolomics studies of human prostate cancer tissue

Introduction

Prostate cancer (PCa) is one of the most prevalent cancers in men worldwide. Serum prostate-specific antigen (PSA) remains the most used biomarker in the detection and management of patients with PCa, in spite of the problems related with its low specificity, false positive rate and overdiagnosis. Furthermore, PSA is unable to discriminate indolent from aggressive PCa, which can lead to overtreatment. Early diagnosed and treated PCa can have a good prognosis and is potentially curable. Therefore, the discovery of new biomarkers able to detect clinically significant aggressive PCa is urgently needed.

Methods

This revision was based on an electronic literature search, using Pubmed, with Nuclear Magnetic Resonance (NMR), tissue and prostate cancer as keywords. All metabolomic studies performed in PCa tissues by NMR spectroscopy, from 2007 until March 2018, were included in this review.

Results

In the context of cancer, metabolomics allows the analysis of the entire metabolic profile of cancer cells. Several metabolic alterations occur in cancer cells to sustain their abnormal rates of proliferation. NMR proved to be a suitable methodology for the evaluation of these metabolic alterations in PCa tissues, allowing to unveil alterations in citrate, spermine, choline, choline-related compounds, lactate, alanine and glutamate.

Conclusion

The study of the metabolic alterations associated with PCa progression, accomplished by the analysis of PCa tissue by NMR, offers a promising approach for elucidating biochemical pathways affected by PCa and also for discovering new clinical biomarkers. The main metabolomic alterations associated with PCa development and promising biomarker metabolites for diagnosis of PCa were outlined.

     

Plasma metabolomics reveals a potential panel of biomarkers for early diagnosis in acute coronary syndrome

Discovery of new biomarkers is critical for early diagnosis of acute coronary syndrome (ACS). Recent advances in metabolomic technologies have drastically enhanced the possibility of improving the knowledge of its physiopathology through the identification of the altered metabolic pathways. In this study, analyses of peripheral plasma from non-ST segment elevation ACS patients and healthy controls by gas chromatography–mass spectrometry (GC–MC) permitted the identification of 15 metabolites with statistical differences (p < 0.05) between experimental groups. Additionally, validation by GC–MC and liquid chromatography–MC permitted us to identify a potential panel of biomarkers formed by 5-OH-tryptophan, 2-OH-butyric acid and 3-OH-butyric acid. This panel of biomarkers reflects the oxidative stress and the hypoxic state that suffers the myocardial cells and consequently constitutes a metabolomic signature of the atherogenesis process that could be used for early diagnosis of ACS.     

Metabolomic Analysis Reveals Metabolic Disturbance in the Cortex and Hippocampus of Subchronic MK-801 Treated Rats

Background

Although a number of proteins and genes relevant to schizophrenia have been identified in recent years, few are known about the exact metabolic pathway involved in this disease. Our previous proteomic study has revealed the energy metabolism abnormality in subchronic MK-801 treated rat, a well-established animal model for schizophrenia. This prompted us to further investigate metabolite levels in the same rat model to better delineate the metabolism dysfunctions and provide insights into the pathology of schizophrenia.

Methods

Metabolomics, a high-throughput investigatory strategy developed in recent years, can offer comprehensive metabolite-level insights that complement protein and genetic findings. In this study, we employed a nondestructive metabolomic approach (1H-MAS-NMR) to investigate the metabolic traits in cortex and hippocampus of MK-801 treated rats. Multivariate statistics and ingenuity pathways analyses (IPA) were applied in data processing. The result was further integrated with our previous proteomic findings by IPA analysis to obtain a systematic view on our observations.

Results

Clear distinctions between the MK-801 treated group and the control group in both cortex and hippocampus were found by OPLS-DA models (with R²X = 0.441, Q²Y = 0.413 and R²X = 0.698, Q²Y = 0.677, respectively). The change of a series of metabolites accounted for the separation, such as glutamate, glutamine, citrate and succinate. Most of these metabolites fell in a pathway characterized by down-regulated glutamate synthesis and disturbed Krebs cycle. IPA analysis further confirmed the involvement of energy metabolism abnormality induced by MK-801 treatment.

Conclusions

Our metabolomics findings reveal systematic changes in pathways of glutamate metabolism and Krebs cycle in the MK-801 treated rats’ cortex and hippocampus, which confirmed and improved our previous proteomic observation and served as a valuable reference to the etiology research of schizophrenia.     

A Metabolomic Study of Rats with Doxorubicin-Induced Cardiomyopathy and Shengmai Injection Treatment

Doxorubicin-induced cardiomyopathy (DOX-CM) is a severe complication of doxorubicin (DOX) chemotherapy. Characterized by cumulative and irreversible myocardial damage, its pathogenesis has not been fully elucidated. Shengmai Injection (SMI), a Traditional Chinese Medicine, may alleviate myocardial injury and improve heart function in the setting of DOX-CM. As a result of its multi-component and multi-target nature and comprehensive regulation, the pharmacological mechanisms underlying SMI’s effects remain obscure. The emerging field of metabolomics provides a potential approach with which to explore the pathogenesis of DOX-CM and the benefits of SMI treatment. DOX-CM was induced in rats via intraperitoneal injections of DOX. Cardiac metabolic profiling was performed via gas chromatography/mass spectrometry and ultra-performance liquid chromatography/tandem mass spectrometry. A bioinformatics analysis was conducted via Ingenuity Pathway Analysis (IPA). Eight weeks following DOX treatment, significant cardiac remodeling, dysfunction and metabolic perturbations were observed in the rats with DOX-CM. The metabolic disturbances primarily involved lipids, amino acids, vitamins and energy metabolism, and may have been indicative of both an energy metabolism disorder and oxidative stress secondary to DOX chemotherapy. However, SMI improved cardiac structure and function, as well as the metabolism of the rats with DOX-CM. The metabolic alterations induced via SMI, including the promotion of glycogenolysis, glycolysis, amino acid utilization and antioxidation, suggested that SMI exerts cardioprotective effects by improving energy metabolism and attenuating oxidative stress. Moreover, the IPA revealed that important signaling molecules and enzymes interacted with the altered metabolites. These findings have provided us with new insights into the pathogenesis of DOX-CM and the effects of SMI, and suggest that the combination of metabolomic analysis and IPA may represent a promising tool with which to explore and better understand both heart disease and TCM therapy.     

Metabolic Profiles are Principally Different between Cancers of the Liver,Pancreas and Breast

Molecular profiling of primary tumors may facilitate the classification of patients with cancer into more homogenous biological groups to aid clinical management. Metabolomic profiling has been shown to be a powerful tool in characterizing the biological mechanisms underlying a disease but has not been evaluated for its ability to classify cancers by their tissue of origin. Thus, we assessed metabolomic profiling as a novel tool for multiclass cancer characterization. Global metabolic profiling was employed to identify metabolites in paired tumor and non-tumor liver (n=60), breast (n=130) and pancreatic (n=76) tissue specimens. Unsupervised principal component analysis showed that metabolites are principally unique to each tissue and cancer type. Such a difference can also be observed even among early stage cancers, suggesting a significant and unique alteration of global metabolic pathways associated with each cancer type. Our global high-throughput metabolomic profiling study shows that specific biochemical alterations distinguish liver, pancreatic and breast cancer and could be applied as cancer classification tools to differentiate tumors based on tissue of origin.