Metabolite Biomarker Discovery Research Articles

Discovery of metabolite biomarkers for odontogenic keratocysts.

Odontogenic keratocysts (OKCs) are locally aggressive and have a high rate of recurrence, but the pathogenesis of OKCs is not fully understood. We aimed to investigate the serum metabolomic profile of OKCs and discover potential biomarkers. Metabolomic analysis was performed on 42 serum samples from 22 OKC patients and 20 healthy controls (HCs) using gas chromatography‒mass spectrometry to identify dysregulated metabolites in the OKC samples. LASSO regression and receiver operating characteristic (ROC) curve analyses were used to select and validate metabolic biomarkers and develop diagnostic models. A total of 73 metabolites were identified in the serum samples, and 24 metabolites were dysregulated in the OKC samples, of which 4 were upregulated. Finally, a diagnostic panel of 10 metabolites was constructed that accurately diagnosed OKCs (sensitivity of 100%, specificity of 100%, area under the curve of 1.00). This study is the first to investigate the metabolic characteristics and potential metabolic biomarkers in the serum of OKC patients using GC‒MS. Our study provides further evidence to explore the pathogenesis of OKC.

Tumor Tissue-Specific Biomarkers of Colorectal Cancer by Anatomic Location and Stage.

The progress in the discovery and validation of metabolite biomarkers for the detection of colorectal cancer (CRC) has been hampered by the lack of reproducibility between study cohorts. The majority of discovery-phase biomarker studies have used patient blood samples to identify disease-related metabolites, but this pre-validation phase is confounded by non-specific disease influences on the metabolome. We therefore propose that metabolite biomarker discovery would have greater success and higher reproducibility for CRC if the discovery phase was conducted in tumor tissues, to find metabolites that have higher specificity to the metabolic consequences of the disease, that are then validated in blood samples. This would thereby eliminate any non-tumor and/or body response effects to the disease. In this study, we performed comprehensive untargeted metabolomics analyses on normal (adjacent) colon and tumor tissues from CRC patients, revealing tumor tissue-specific biomarkers (n = 39/group). We identified 28 highly discriminatory tumor tissue metabolite biomarkers of CRC by orthogonal partial least-squares discriminant analysis (OPLS-DA) and univariate analyses (VIP > 1.5, p < 0.05). A stepwise selection procedure was used to identify nine metabolites that were the most predictive of CRC with areas under the curve (AUCs) of >0.96, using various models. We further identified five biomarkers that were specific to the anatomic location of tumors in the colon (n = 236). The combination of these five metabolites (S-adenosyl-L-homocysteine, formylmethionine, fucose 1-phosphate, lactate, and phenylalanine) demonstrated high differentiative capability for left- and right-sided colon cancers at stage I by internal cross-validation (AUC = 0.804, 95% confidence interval, CI 0.670–0.940). This study thus revealed nine discriminatory biomarkers of CRC that are now poised for external validation in a future independent cohort of samples. We also discovered a discrete metabolic signature to determine the anatomic location of the tumor at the earliest stage, thus potentially providing clinicians a means to identify individuals that could be triaged for additional screening regimens.

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.

Untargeted metabolomics using liquid chromatography coupled with mass spectrometry for rapid discovery of metabolite biomarkers to reveal therapeutic effects of Psoralea corylifolia seeds against osteoporosis.

Liquid chromatography coupled with mass spectrometry has been used as metabolomics profiling tool to discover and identify the metabolites in metabolic diseases. Osteoporosis (OP) syndrome is a chronic metabolic disease characterized by bone mass reduction and changes in bone microstructure. Psoralea corylifolia Linn. seeds (PCS) have a therapeutic effect on osteoporosis, but their action mechanism and therapeutic target are still unclear. This study aims to explore the metabolic changes of the urine profile in glucocorticoid-induced OP model rats and the therapeutic effect of PCS. High-throughput metabolomics based on the liquid chromatography coupled with mass spectrometry quadrupole time-of-flight mass spectrometry and multivariate data analysis were used to analyze the urine metabolites. The results showed that has an obvious separation between model and control groups. OPLS-DA was used to further analyze and discover substances that contributed to the separation. 42 potential biomarkers and 12 related metabolic pathways were identified in combination with network databases. After the intervention of PCS, 24 biomarkers were significantly regulated, mainly with glycone, serine and threonine metabolism, glutathione metabolism and purine metabolism and other metabolic pathways are related and discovered. This study has proved that PCS has therapeutic effect against OP by regulating that metabolic pathways disturbed in the OP. It provided a basis for the research and future development of new drugs for OP treatment.

Development of a Correlative Strategy To Discover Colorectal Tumor Tissue Derived Metabolite Biomarkers in Plasma Using Untargeted Metabolomics.

The metabolic profiling of biofluids using untargeted metabolomics provides a promising choice to discover metabolite biomarkers for clinical cancer diagnosis. However, metabolite biomarkers discovered in biofluids may not necessarily reflect the pathological status of tumor tissue, which makes these biomarkers difficult to reproduce. In this study, we developed a new analysis strategy by integrating the univariate and multivariate correlation analysis approach to discover tumor tissue derived (TTD) metabolites in plasma samples. Specifically, untargeted metabolomics was first used to profile a set of paired tissue and plasma samples from 34 colorectal cancer (CRC) patients. Next, univariate correlation analysis was used to select correlative metabolite pairs between tissue and plasma, and a random forest regression model was utilized to define 243 TTD metabolites in plasma samples. The TTD metabolites in CRC plasma were demonstrated to accurately reflect the pathological status of tumor tissue and have great potential for metabolite biomarker discovery. Accordingly, we conducted a clinical study using a set of 146 plasma samples from CRC patients and gender-matched polyp controls to discover metabolite biomarkers from TTD metabolites. As a result, eight metabolites were selected as potential biomarkers for CRC diagnosis with high sensitivity and specificity. For CRC patients after surgery, the survival risk score defined by metabolite biomarkers also performed well in predicting overall survival time ( p = 0.022) and progression-free survival time ( p = 0.002). In conclusion, we developed a new analysis strategy which effectively discovers tumor tissue related metabolite biomarkers in plasma for cancer diagnosis and prognosis.

SANIST: optimization of a technology for compound identification based on the European Union directive with applications in forensic, pharmaceutical and food analyses.

Electrospray Ionization and collision induced dissociation tandem mass spectrometry are usually employed to obtain compound identification through a mass spectra match. Different algorithms have been developed for this purpose (for example the nist match algorithm). These approaches compare the tandem mass spectra of the unknown analyte with the tandem mass spectra spectra of known compounds inserted in a database. The compounds are usually identified on the basis of spectral match value associated with a probability of recognition. However, this approach is not usually applied to multiple reaction monitoring transition spectra achieved by means of triple quadrupole apparatus, mainly due to the lack of a transition spectra database. The Surface Activated Chemical Ionization-Electrospray-NIST Bayesian model database search (SANIST) platform has been recently developed for new potential metabolite biomarker discovery, to confirm their identity and to use them for clinical and diagnostic applications. Here, we present an improved version of the SANIST platform that extends its application to forensic, pharmaceutical, and food analysis studies, where the compound identification rules are strict. The European Union (EU) has set directives for compound identification (EU directive 2002/657/EC). We have applied the SANIST method to identification of 11-nor-9-carboxytetrahydro-cannabinol in urine samples (an example of a forensic application), circulating levels of the immunosuppressive drug tacrolimus in blood (an example of a pharmaceutical application) and glyphosate in fruit juice (an example of a food analysis application) that meet the EU directive requirements. Copyright © 2016 John Wiley & Sons, Ltd.

(1)H NMR-based metabolite profiling workflow to reduce inter-sample chemical shift variations in urine samples for improved biomarker discovery.

Metabolite profiling of urine has seen much advancement in recent years, and its analysis by nuclear magnetic resonance (NMR) spectroscopy has become well established. However, the highly variable nature of human urine still requires improved protocols despite some standardization. In particular, diseases such as kidney disease can have a profound effect on the composition of urine and generate a highly diverse sample set for clinical studies. Large variations in pH and the cationic concentration of urine play an important role in creating positional noise within datasets generated from NMR. We demonstrate positional noise to be a confounding variable for multivariate statistical tools such as statistical total correlation spectroscopy (STOCSY), thereby hindering the process of biomarker discovery. We present a two-dimensional buffering system using potassium fluoride (KF) and phosphate buffer to reduce positional noise in metabolomic data generated from urine samples with various levels of proteinuria. KF reduces positional noise in citrate peaks, by decreasing the mean relative standard deviation (RSD) from 0.17 to 0.09. By reducing positional noise with KF, STOCSY analysis of citrate peaks saw significant improvement. We further aligned spectral data using a recursive segment-wise peak alignment (RSPA) method, which leads to further improvement of the positional noise (RSD = 0.06). These results were validated using diverse selection of metabolites which lead to an overall improvement in positional noise using the suggested protocol. In summary, we provide an improved workflow for urine metabolite biomarker discovery to achieve higher data quality for better pathophysiological understanding of human diseases. Graphical abstract Citrate peaks in the range 2.75-2.5ppm from datasets with different sample preparation protocols and with/without in silico alignment. A Citrate peaks with standard phosphate buffering and without in silico alignment. B citrate peaks with standard phosphate buffering and with in silico alignment. C citrate peak with additional potassium fluoride and standard phosphate buffering without in silico alignment. D citrate peaks with additional potassium fluoride and standard phosphate buffering with in silico alignment. Below the respective spectrum are displayed the percent relative standard deviation (RSD) of the respective citrate peaks. This is a measure of the positional noise of peaks within a (1)H NMR analysis. It can be seen that D performs the best in reducing positional noise of citrate peaks. E-H STOCSY analysis of correlating spectral features with the driver peak at 2.675ppm (see red arrow) to identify structural correlations. As a, b, c, and d are known to be structurally correlated, STOCSY analysis should reveal r (2) = 1 if data is perfectly aligned and can therefore be used as a measure of peak alignment. E Strong positional noise does not allow identifying the c and d peaks of the AB system to be correlated. F, G Neither in silico alignment or KF addition alone can completely improve the alignment and therefore increase the correlations. H Highly improved alignment by combining both KF addition and in silico alignment reduces positional noise and elucidates all four citrate peaks to be strongly correlated.

Abstract B52: Targeted LC-MS/MS metabolic profiling for colon cancer progression monitoring

Abstract Introduction: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, and a major cause of human morbidity and mortality. A number of current efforts are focused on earlier detection of colon cancer using a variety of technologies including genomics, proteomics and metabolomics. Research focused on CRC disease status surveillance using metabolomics or other approaches has not been reported; Close monitoring of disease progression (DP) in CRC can be critical for patients' prognosis management and treatment decisions. In this study we investigate a targeted LC-MS/MS approach for serum metabolic profiling to monitor and predict patient disease progression, using a panel of significantly altered metabolites as potential biomarkers. Methods: 59 serum samples from 21 CRC patients were analyzed, including 23 samples from DP patients and 36 from other CRC disease status (e.g., stable disease and complete remission). Chromatographic separations were performed via an Agilent HPLC system installed with two hydrophilic interaction chromatography (HILIC) columns, and then targeted data acquisition was performed in multiple-reaction-monitoring (MRM) mode using an AB Sciex QTrap 5500 mass spectrometer. We monitored 106 and 58 MRM transitions in negative and positive mode, respectively. Univariate and multivariate statistical analyses (such as the Mann- Whitney U-test and PLS-DA) were applied for metabolite biomarker discovery and model development on a selected set of promising biomarker candidates. Monte Carlo cross validation (MCCV) was performed to evaluate model robustness. Results and conclusion: LC-MS/MS targeted analysis provided a robust system for metabolic profiling of CRC patient disease status monitoring using serum samples. Targeted screening of 164 metabolites, representing more than 20 different classes (such as amino acids, carboxylic acids, pyridines, and etc.) and from 25 important metabolic pathways (e.g., TCA cycle, amino acid metabolism, purine and pyrimidine metabolism, and glycolysis, and etc.) was performed using both positive and negative ionization modes. 131 metabolites could be reproducibly detected in the serum samples, with an average CV of 7.1% measured in pooled serum quality control samples. After univariate analysis, 36 metabolites from different classes, such as monosaccharides, amino acids, carboxylic acids and nucleosides, showed a significant statistical difference (p&amp;lt;0.05) between CRC DP compared to other disease status (e.g., stable disease and complete remission), and twelve of these were previously reported in other CRC serum metabolites studies. Highly significant changes (defined as p&amp;lt;0.001) were found in the average levels of seven metabolites, namely fructose, aspartic acid, oxalic acid, lactate, pyruvate, oxaloacetate and orotate. These metabolites are involved in multiple important metabolic pathways, such as carbohydrate metabolism, tricarboxylic acid cycle, glycolosis, amino acid metabolism, and pyrimidine metabolism. A PLS-DA model was built based on the combination of these seven metabolite biomarkers, and excellent performance was obtained with sensitivity of 96%, specificity of 75% and area under the receiver operator curve (AUROC) of 0.92. Superior performance of this metabolite profile was observed as compared to the traditional carcinoembryonic antigen (CEA) marker currently used for CRC monitoring, which had an AUROC of 0.76, sensitivity of 87% and specificity of 55% for detecting DP over other disease status. Further, Monte Carlo cross validation (MCCV) was performed to compare the true sample classifications with randomly permuted sample classes, and to measure model robustness. MCCV also demonstrated the improved sensitivity and robust diagnostic power of this metabolic profiling approach. In conclusion, our targeted LC-MS/MS metabolic profiling of serum provides an improved approach for colon cancer disease progression monitoring. Citation Format: Jiangjiang Zhu, Danijel Djukovic, Lingli Deng, Lingli Deng, Haiwei Gu, Farhan Himmati, Mohammad Abu Zaid, E. Gabriela Chiorean, E. Gabriela Chiorean, Daniel Raftery, Daniel Raftery. Targeted LC-MS/MS metabolic profiling for colon cancer progression monitoring. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr B52.

No more monkeying around: primate malaria model systems are key to understanding Plasmodium vivax liver-stage biology, hypnozoites, and relapses.

Plasmodium vivax is a human malaria parasite responsible for significant morbidity worldwide and potentially death. This parasite possesses formidable liver-stage biology that involves the formation of dormant parasites known as hypnozoites. Hypnozoites are capable of activating weeks, months, or years after a primary blood-stage infection causing relapsing bouts of illness. Elimination of this dormant parasitic reservoir will be critical for global malaria eradication. Although hypnozoites were first discovered in 1982, few advancements have been made to understand their composition and biology. Until recently, in vitro models did not exist to study these forms and studying them from human ex vivo samples was virtually impossible. Today, non-human primate (NHP) models and modern systems biology approaches are poised as tools to enable the in-depth study of P. vivax liver-stage biology, including hypnozoites and relapses. NHP liver-stage model systems for P. vivax and the related simian malaria species P. cynomolgi are discussed along with perspectives regarding metabolite biomarker discovery, putative roles of extracellular vesicles, and relapse immunobiology.

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Objectives Unbiased metabolite biomarker discovery has revealed that combinations of blood-borne metabolites have the potential to predict preeclampsia accurately at ca. 15 weeks of gestation (Kenny et al., 2010). Our aim is to deploy a dedicated translational effort bringing the merits of de-novo biomarker research to health care providers and patients. Methods To exploit the widespread availability of quadrupole mass spectrometers (QqQ-MS) in clinical laboratories world-wide, a platform migration to QqQ-MS was performed. To support the further refinement of the metabolites-based pre-eclampsia prediction algorithm, and the technical and clinical validation of the test, access to appropriate patient samples from prospective cohorts is warranted. A public-private partnership involving supranational government funding, clinicians and dedicated small and medium sized companies collaborated to establish a pregnancy biobank. Results Thus far, a simple metabolite extraction and a targeted LC-QqQ-MS approach using stable isotope labelled metabolites for relative quantification has been developed. The (semi-)quantitative analysis of circa 40 metabolites with very disparate physicochemical characteristics is achievable in a single 10 min run. The company is engaged in 2 dedicated public-private initiatives, i.e. SCreening fOr Pregnancy Endpoints (SCOPE) (North et al., 2011) and IMproved Pregnancy Outcomes by Early Detection (IMPROvED) (Navaratnam et al., 2013). These collaborations are instrumental for the further clinical and technical validation of the test under development. Conclusions Development of a dedicated translational effort to further exploit newly discovered biomarkers relevant to the prediction of preeclampsia has already resulted in an analytical method ready for testing in clinically relevant patient cohorts. Public–private biobanking efforts have proved to be a cost-efficient means enabling both additional basic biomarker research and progression of biomarkers to market. Disclosures: L. Bond: employee Metabolomic Diagnostics. C. Nolan: employee Metabolomic Diagnostics. K. Hyland: employee Metabolomic Diagnostics. C. Lenaerts: None. P.N. Baker: Commercial Interest: Metabolomic Diagnostics. L.C. Kenny: Commercial Interest: Metabolomic Diagnostics. R. Tuytten: employee Metabolomic Diagnostics.

MetaboNexus: an interactive platform for integrated metabolomics analysis

MetaboNexus is an interactive metabolomics data analysis platform that integrates pre-processing of raw peak data with in-depth statistical analysis and metabolite identity search. It is designed to work as a desktop application hence uploading large files to web servers is not required. This could speed up the data analysis process because server queries or queues are avoided, while ensuring security of confidential clinical data on a local computer. With MetaboNexus, users can progressively start from data pre-processing, multi- and univariate analysis to metabolite identity search of significant molecular features, thereby seamlessly integrating critical steps for metabolite biomarker discovery. Data exploration can be first performed using principal components analysis, while prediction and variable importance can be calculated using partial least squares-discriminant analysis and Random Forest. After identifying putative features from multi- and univariate analyses (e.g. t test, ANOVA, Mann–Whitney U test and Kruskal–Wallis test), users can seamlessly determine the molecular identity of these putative features. To assist users in data interpretation, MetaboNexus also automatically generates graphical outputs, such as score plots, diagnostic plots, boxplots, receiver operating characteristic plots and heatmaps. The metabolite search function will match the mass spectrometric peak data to three major metabolite repositories, namely HMDB, MassBank and METLIN, using a comprehensive range of molecular adducts. Biological pathways can also be searched within MetaboNexus. MetaboNexus is available with installation guide and tutorial at http://www.sph.nus.edu.sg/index.php/research-services/research-centres/ceohr/metabonexus, and is meant for the Windows Operating System, XP and onwards (preferably on 64-bit). In summary, MetaboNexus is a desktop-based platform that seamlessly integrates the entire data analytical workflow and further provides the putative identities of mass spectrometric data peaks by matching them to databases.

Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach

BackgroundMetabolism is a vital cellular process, and its malfunction can be a major contributor to many human diseases. Metabolites can serve as a metabolic disease biomarker. An detection of such biomarkers plays a significant role in the study of biochemical reaction and signaling networks. Early research mainly focused on the analysis of the metabolic networks. The issue of integrating metabolite networks with other available biological data to reveal the mechanics of disease-metabolite associations is an important and interesting challenge.ResultsIn this article, we propose two new approaches for the identification of metabolic biomarkers with the incorporation of disease specific gene expression data and the genome-scale human metabolic network. The first approach is to compare the flux interval between the normal and disease sample so as to identify reaction biomarkers. The second one is based on the Reaction-Reaction Network (RRN) to reveal the significant reactions. These two approaches utilize reaction flux obtained by a Linear Programming (LP) based method that can contribute to the discovery of potential novel biomarkers.ConclusionsBiomarker identification is an important issue in studying biochemical reactions and signaling networks. Two efficient and effective computational methods are proposed for the identification of biomarkers in this article. Furthermore, the biomarkers found by our proposed methods are shown to be significant determinants for diabetes.