Metabolomics

Interpretation and analysis of NMR-based metabolic profiling studies is limited by substantially incomplete commercial and academic databases. Statistical significance tests, including p-values, VIP scores, AUC values and FC values, can be largely inconsistent. Data normalization prior to statistical analysis can cause erroneous outcomes. The objectives were (1) to quantitatively assess consistency among p-values, VIP scores, AUC values and FC values in representative NMR-based metabolic profiling datasets, (2) to assess how data normalization can impact statistical significance outcomes, (3) to determine resonance peak assignment completion potential using commonly used databases and (4) to analyze intersection and uniqueness of metabolite space in these databases. P-values, VIP scores, AUC values and FC values, and their dependence on data normalization, were determined in orthotopic mouse model of pancreatic cancer and two human pancreatic cancer cell lines. Completeness of resonance assignments were evaluated using Chenomx, the human metabolite database (HMDB) and the COLMAR database. The intersection and uniqueness of the databases was quantified. P-values and AUC values were strongly correlated compared to VIP or FC values. Distributions of statistically significant bins depended strongly on whether or not datasets were normalized. 40–45% of peaks had either no or ambiguous database matches. 9–22% of metabolites were unique to each database. Lack of consistency in statistical analyses of metabolomics data can lead to misleading or inconsistent interpretation. Data normalization can have large effects on statistical analysis and should be justified. About 40% of peak assignments remain ambiguous or impossible with current databases. 1D and 2D databases should be made consistent to maximize metabolite assignment confidence and validation.

Excess visceral and liver fat are known risk factors for cardiometabolic disorders. Metabolomics might allow for easier quantification of these ectopic fat depots, instead of using invasive and costly tools such as MRI or approximations such as waist circumference. We explored the potential use of plasma metabolites as biomarkers of visceral adipose tissue (VAT) and hepatic triglyceride content (HTGC). We performed a cross-sectional analysis of a subset of the Netherlands Epidemiology of Obesity study. Plasma metabolite profiles were determined using the Biocrates AbsoluteIDQ p150 kit in 176 individuals with normal fasting plasma glucose. VAT was assessed with magnetic resonance imaging and HTGC with proton-MR spectroscopy. We used linear regression to investigate the associations of 190 metabolite variables with VAT and HTGC. After adjustment for age, sex, total body fat, currently used approximations of visceral and liver fat, and multiple testing, three metabolite ratios were associated with VAT. The strongest association was the lysophosphatidylcholines to total phosphatidylcholines (PCs) ratio [− 14.1 (95% CI − 21.7; − 6.6) cm2 VAT per SD of metabolite concentration]. Four individual metabolites were associated with HTGC, especially the diacyl PCs of which C32:1 was the strongest at a 1.31 (95% CI 1.14; 1.51) fold increased HTGC per SD of metabolite concentration. Metabolomics may be a useful tool to identify biomarkers of visceral fat and liver fat content that have added diagnostic value over current approximations. Replication studies are required to validate the diagnostic value of these metabolites.

Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites. To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome. Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

Idiopathic acquired aplastic anemia (AA) is a bone marrow failure disorder where aberrant T-cell functions lead to depletion of hematopoietic stem and progenitor cells in the bone marrow (BM) microenvironment. T-cells undergo metabolic rewiring, which regulates their proliferation and differentiation. Therefore, studying metabolic variation in AA patients may aid us with a better understanding of the T-cell regulatory pathways governed by metabolites and their pathological engagement in the disease. To identify the differential metabolites in BM plasma of AA patients, AA follow-up (AAF) in comparison to normal controls (NC) and to identify potential disease biomarker(s). The study used 1D 1H NMR Carr–Purcell–Meiboom–Gill (CPMG) spectra to identify the metabolites present in the BM plasma samples of AA (n = 40), AAF (n = 16), and NC (n = 20). Metabolic differences between the groups and predictive biomarkers were identified by using multivariate analysis and receiver operating characteristic (ROC) module of Metaboanalyst V5.0 tool, respectively. The AA and AAF samples were well discriminated from NC group as per Principal Component analysis (PCA). Further, we found significant alteration in the levels of 17 metabolites in AA involved in amino-acid (Leucine, serine, threonine, phenylalanine, lysine, histidine, valine, tyrosine, and proline), carbohydrate (Glucose, lactate and mannose), fatty acid (Acetate, glycerol myo-inositol and citrate), and purine metabolism (hypoxanthine) in comparison to NC. Additionally, biomarker analysis predicted Hypoxanthine and Acetate can be used as a potential biomarker. The study highlights the significant metabolic alterations in the BM plasma of AA patients which may have implication in the disease pathobiology.

We have recently shown that fluorescence spectroscopy of plasma samples has promising abilities regarding early detection of colorectal cancer. In the present paper, these results were further developed by combining fluorescence with the biomarkers, CEA and TIMP-1 and traditional metabolomic measurements in the form of 1H NMR spectroscopy. The results indicate that using an extensive profile established by combining such measurements together with the biomarkers is better than using single markers.

The serum polar lipid metabolic changes for two common cage-cultured fishes, yellow coraker Pseudosciaena crocea and Japanese seabass Lateolabrax japonicus, after tropical storm attack have been studied by ultra-performance liquid chromatography—quadrupole-time of flight mass spectrometry (UPLC-qTOF-MS). The full scan mass spectrometry combined with principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) indicated that yellow croaker underwent significant chemico-physiological changes during the recovery process, whereas Japanese seabass did not show such noticeable time-dependent consistent metabolites change patterns. Further identification of the metabolite biomarkers showed the increase of phosphatidylcholine with high unsaturated fatty acid and lysophospholipids, and the decrease of phosphatidylcholine with saturated fatty acids and plasmologens, which indicated the need of energy supplement and successive stressful inflammation. The increase of taurocholic acid and decrease of cortol could be regarded as the physiological alleviation measure during the recovery period. This is the first metabolomic study to tackle the fish physiological response for the complex environmental changes, and demonstrated that lipidomics is an effective analytical tool for predicting the stress resistance of fish to ultra uncontrolled environmental stress.

To generate biomarkers of target engagement or predictive response for multi-target drugs is challenging. One such compound is the multi-AGC kinase inhibitor AT13148. Metabolic signatures of selective signal transduction inhibitors identified in preclinical models have previously been confirmed in early clinical studies. This study explores whether metabolic signatures could be used as biomarkers for the multi-AGC kinase inhibitor AT13148. To identify metabolomic changes of biomarkers of multi-AGC kinase inhibitor AT13148 in cells, xenograft / mouse models and in patients in a Phase I clinical study. HILIC LC–MS/MS methods and Biocrates AbsoluteIDQ™ p180 kit were used for targeted metabolomics; followed by multivariate data analysis in SIMCA and statistical analysis in Graphpad. Metaboanalyst and String were used for network analysis. BT474 and PC3 cells treated with AT13148 affected metabolites which are in a gene protein metabolite network associated with Nitric oxide synthases (NOS). In mice bearing the human tumour xenografts BT474 and PC3, AT13148 treatment did not produce a common robust tumour specific metabolite change. However, AT13148 treatment of non-tumour bearing mice revealed 45 metabolites that were different from non-treated mice. These changes were also observed in patients at doses where biomarker modulation was observed. Further network analysis of these metabolites indicated enrichment for genes associated with the NOS pathway. The impact of AT13148 on the metabolite changes and the involvement of NOS-AT13148- Asymmetric dimethylarginine (ADMA) interaction were consistent with hypotension observed in patients in higher dose cohorts (160-300 mg). AT13148 affects metabolites associated with NOS in cells, mice and patients which is consistent with the clinical dose-limiting hypotension.

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.