Metabolomics

Rheumatoid arthritis (RA) and osteoarthritis (OA) are clinicopathologically different. We aimed to assess the feasibility of metabolomics in differentiating the metabolite profiles of synovial fluid between RA and OA using gas chromatography/time-of-flight mass spectrometry. We first compared the global metabolomic changes in the synovial fluid of 19 patients with RA and OA. Partial least squares-discriminant, hierarchical clustering, and univariate analyses were performed to distinguish metabolites of RA and OA. These findings were then validated using synovial fluid samples from another set of 15 patients with RA and OA. We identified 121 metabolites in the synovial fluid of the first 19 samples. The score plot of PLS-DA showed a clear separation between RA and OA. Twenty-eight crucial metabolites, including hypoxanthine, xanthine, adenosine, citrulline, histidine, and tryptophan, were identified to be capable of distinguishing RA metabolism from that of OA; these were found to be associated with purine and amino acid metabolism. Our results demonstrated that metabolite profiling of synovial fluid could clearly discriminate between RA and OA, suggesting that metabolomics may be a feasible tool to assist in the diagnosis and advance the comprehension of pathological processes for diseases.

Quantitative NMR metabolomics is a powerful tool to have access to valuable information on metabolism. Unfortunately, the quantitative analysis of metabolic samples is often hampered by peak overlap. Two-dimensional (2D) spectroscopy offers a promising alternative and quantitative results can be obtained provided that a calibration approach is employed. However, the duration of 2D NMR experiments is barely compatible with the metabolomic study of a large number of samples. This drawback can be circumvented by relying on “ultrafast” experiments capable of recording 2D spectra in a single-scan. While such experiments are not sensitive enough to match the concentrations of metabolic samples, a compromise can be reached by hybrid strategies capable of recording 2D NMR spectra of extracts in a few minutes only. The purpose of this study is to demonstrate that these multi-scan single-shot experiments can be successfully applied to the absolute quantification of major metabolites in plant extracts. Fast COSY experiments are recorded in 5 min on tomato fruit pericarp extracts at different stages of development. The concentration of eight major metabolites is determined with a trueness better than 10 % and a technical repeatability of a few percent. The experiments performed at two magnetic fields lead to similar quantitative results, in coherence with the metabolism of tomato fruit. The results show that fast 2D NMR methods form a promising tool for fast targeted metabolomics, and open promising perspective towards the automated and high-throughput quantitative analysis of large groups of plant and other samples for metabolomics and for the modelling of metabolism.

A large number of studies have explored the potential biomarkers for detecting liver cirrhosis in an early stage, yet consistent conclusions are still warranted. To conduct a review and a meta-analysis of the existing studies that test the serum level of bile acids in cirrhosis as the potential biomarkers to predict cirrhosis. Six databases had been searched from inception date to April 12, 2021. Screening and selection of the records were based on the inclusion criteria. The risk of bias was assessed with the Newcastle–Ottawa quality assessment scale (NOS). Mean difference (MD) and confidence intervals 95% (95% CI) were calculated by using the random effect model for the concentrations of bile acids in the meta-analysis, and I2 statistic was used to measure studies heterogeneity. This study was registered on PROSPERO. A total of 1583 records were identified and 31 studies with 2679 participants (1263 in the cirrhosis group, 1416 in the healthy control group) were included. The quality of included studies was generally high, with 25 studies (80.6%) rated over 7 stars. A total of 45 bile acids or their ratios in included studies were extracted. 36 increased in the cirrhosis group compared with those of the healthy controls by a qualitative summary, 5 decreased and 4 presented with mixing results. The result of meta-analysis among 12 studies showed that 13 bile acids increased, among which four primary conjugated bile acids showed the most significant elevation in the cirrhosis group: GCDCA (MD = 11.38 μmol/L, 95% CI 8.21–14.55, P < 0.0001), GCA (MD = 5.72 μmol/L, 95% CI 3.47–7.97, P < 0.0001), TCDCA (MD = 3.57 μmol/L, 95% CI 2.64–4.49, P < 0.0001) and TCA (MD = 2.14 μmol/L, 95% CI 1.56–2.72, P < 0.0001). No significant differences were found between the two groups in terms of DCA (MD = − 0.1 μmol/L, 95% CI − 0.18 to − 0.01, P < 0.0001) and LCA (MD = − 0.01 μmol/L, 95% CI − 0.01 to − 0.02, P < 0.0001), UDCA (MD = − 0.14 μmol/L, 95% CI − 0.04 to − 0.32, P < 0.0001), and TLCA (MD = 0 μmol/L, 95% CI 0–0.01, P < 0.0001). Subgroup analysis in patients with hepatitis B cirrhosis showed similar results. Altered serum bile acids profile seems to be associated with cirrhosis. Some specific bile acids (GCA, GCDCA, TCA, and TCDCA) may increase with the development of cirrhosis, which possibly underlay their potential role as predictive biomarkers for cirrhosis. Yet this predictive value still needs further investigation and validation in larger prospective cohort studies.

Few studies have investigated the influence of storage conditions on urine samples and none of them used targeted mass spectrometry (MS). We investigated the stability of metabolite profiles in urine samples under different storage conditions using targeted metabolomics. Pooled, fasting urine samples were collected and stored at −80 °C (biobank standard), −20 °C (freezer), 4 °C (fridge), ~9 °C (cool pack), and ~20 °C (room temperature) for 0, 2, 8 and 24 h. Metabolite concentrations were quantified with MS using the AbsoluteIDQ™ p150 assay. We used the Welch-Satterthwaite-test to compare the concentrations of each metabolite. Mixed effects linear regression was used to assess the influence of the interaction of storage time and temperature. The concentrations of 63 investigated metabolites were stable at −20 and 4 °C for up to 24 h when compared to samples immediately stored at −80 °C. When stored at ~9 °C for 24 h, few amino acids (Arg, Val and Leu/Ile) significantly decreased by 40% in concentration (P < 7.9E−04); for an additional three metabolites (Ser, Met, Hexose H1) when stored at ~20 °C reduced up to 60% in concentrations. The concentrations of four more metabolites (Glu, Phe, Pro, and Thr) were found to be significantly influenced when considering the interaction between exposure time and temperature. Our findings indicate that 78% of quantified metabolites were stable for all examined storage conditions. Particularly, some amino acid concentrations were sensitive to changes after prolonged storage at room temperature. Shipping or storing urine samples on cool packs or at room temperature for more than 8 h and multiple numbers of freeze and thaw cycles should be avoided.

Palmitate, the typical end product released from fatty acid synthase, is of interest to many researchers performing metabolomics. Although palmitate can be readily detected by using mass spectrometry, many metabolomic platforms involve the use of plastic consumables that introduce a competing background signal of palmitate. The goal of this study was to quantify palmitate contamination in metabolomics and isotope tracer studies and to examine the reliability of approaches for reducing error. We measured the quantitative error introduced by palmitate contamination from 4 vendors of plastic consumables used in combination with several different extraction solvents. The background palmitate signal was as much as sixfold higher than the biological palmitate signal from 4 million 3T3-L1 cells. Importantly, the palmitate contamination signal was highly variable between plastic consumables (even within the same lot) and therefore could not be accurately removed by subtracting the background as measured from a blank. In addition to affecting relative and absolute quantitation, the palmitate background signal from disposable plastics also led to the underestimation of labeled palmitate in isotope tracer experiments. When measuring palmitate standard solutions, the best results were obtained when glass vials and glass pipettes were used. However, much of the palmitate background signal could be eliminated by pre-rinsing plastic vials and plastic pipette tips with methanol prior to sample introduction. For isotope tracer studies, error could also be minimized by estimating palmitate enrichment from palmitoylcarnitine, which does not have a competing contamination signal from plastic consumables.

Globally, one of the major causes of cancer related deaths in women is breast cancer. Although metabolic pattern is altered in cancer patients, robust metabolic biomarkers with a potential to improve the screening and disease monitoring are lacking. A complete metabolome profiling of breast cancer patients may lead to the identification of diagnostic/prognostic markers and potential targets. The aim of this study was to analyze the metabolic profile in the serum from 43 breast cancer patients and 13 healthy individuals. We used 1H NMR spectroscopy for the identification and quantification of metabolites. q-RT-PCR was used to examine the relative expression of lncRNAs. Metabolites such as amino acids, lipids, membrane metabolites, lipoproteins, and energy metabolites were observed in the serum from both patients and healthy individuals. Using unsupervised PCA, supervised PLS-DA, supervised OPLS-DA, and random forest classification, we observed that more than 25 metabolites were altered in the breast cancer patients. Metabolites with AUC value > 0.9 were selected for further analysis that revealed significant elevation of lactate, LPR and glycerol, while the level of glucose, succinate, and isobutyrate was reduced in breast cancer patients in comparison to healthy control. The level of these metabolites (except LPR) was altered in advanced-stage breast cancer patients in comparison to early-stage breast cancer patients. The altered metabolites were also associated with over 25 signaling pathways related to metabolism. Further, lncRNAs such as H19, MEG3 and GAS5 were dysregulated in the breast tumor tissue in comparison to normal adjacent tissue. The study provides insights into metabolic alteration in breast cancer patients. It also provides an avenue to examine the association of lncRNAs with metabolic patterns in patients.

In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. However, metabolomics normalization is often considered an imprecise science due to the diverse sources of variation and the availability of a number of alternative strategies that may be implemented. We highlight the need for comparative evaluation of different normalization methods and present software strategies to help ease this task for both data-oriented and biological researchers. We present NormalizeMets—a joint graphical user interface within the familiar Microsoft Excel and freely-available R software for comparative evaluation of different normalization methods. The NormalizeMets R package along with the vignette describing the workflow can be downloaded from https://cran.r-project.org/web/packages/NormalizeMets/ . The Excel Interface and the Excel user guide are available on https://metabolomicstats.github.io/ExNormalizeMets . NormalizeMets allows for comparative evaluation of normalization methods using criteria that depend on the given dataset and the ultimate research question. Hence it guides researchers to assess, select and implement a suitable normalization method using either the familiar Microsoft Excel and/or freely-available R software. In addition, the package can be used for visualisation of metabolomics data using interactive graphical displays and to obtain end statistical results for clustering, classification, biomarker identification adjusting for confounding variables, and correlation analysis. NormalizeMets is designed for comparative evaluation of normalization methods, and can also be used to obtain end statistical results. The use of freely-available R software offers an attractive proposition for programming-oriented researchers, and the Excel interface offers a familiar alternative to most biological researchers. The package handles the data locally in the user’s own computer allowing for reproducible code to be stored locally.

Incidence of esophageal adenocarcinoma (EA), an often fatal cancer, has increased sharply over recent decades. Several important risk factors (reflux, obesity, smoking) have been identified for EA and its precursor, Barrett’s esophagus (BE), but a key challenge remains in identifying individuals at highest risk, since most with reflux do not develop BE, and most with BE do not progress to cancer. Metabolomics represents an emerging approach for identifying novel biomarkers associated with cancer development. We used targeted liquid chromatography-mass spectrometry (LC-MS) to profile 57 metabolites in 322 serum specimens derived from individuals with gastroesophageal reflux disease (GERD), BE, high-grade dysplasia (HGD), or EA, drawn from two well-annotated epidemiologic parent studies. Multiple metabolites differed significantly (P < 0.05) between BE versus GERD (n = 9), and between HGD/EA versus BE (n = 4). Several top candidates (FDR q ≤ 0.15), including urate, homocysteine, and 3-nitrotyrosine, are linked to inflammatory processes, which may contribute to BE/EA pathogenesis. Multivariate modeling achieved moderate discrimination between HGD/EA and BE (AUC = 0.75), with less pronounced separation for BE versus GERD (AUC = 0.64). Serum metabolite differences can be detected between individuals with GERD versus BE, and between those with BE versus HGD/EA, and may help differentiate patients at different stages of progression to EA.

Metabolomics is a multi-discipline approach to systems biology that provides a snapshot of the metabolic status of a cell, tissue, or organism. Metabolomics uses mass spectroscopy (MS) and nuclear magnetic resonance (NMR) to analyze biological samples for low molecular weight metabolites. Normalize urine sample pre-acquisition to perform a targeted quantitative analysis of selected metabolites in rat urine. Urine samples were provided from rats on a control diet (n = 10) and moderate sucrose diet (n = 8) collected in a metabolic cage during an eight hour fast. Urine from each sample was prepared by two different methods. One sample was a non-normalized sample of 1200 µL and the second sample was a variable volume-normalized to the concentration of urobilin in a standard sample of urine. The urobilin concentration in all samples was determined by fluorescence. Ten metabolites for each non-normalized and normalized urine sample were quantified by integration to an internal standard of DSS. Both groups showed an improvement in pH range going from non-normalized to normalized samples. In the group on the control diet, eight metabolites had significant improvement in range, while the remaining two metabolites had insignificant improvement in range comparing the non-normalized sample to the normalized sample. In the group on the moderate sucrose diet all ten metabolites showed significant improvement in range going from non-normalized to normalized samples. These findings describe a pre-acquisition method of urine normalization to adjust for differences in hydration state of each organism. This results in a narrower concentration range in a targeted analysis.

Resting skeletal muscle has a preference for the oxidation of lipids compared to carbohydrates and a shift towards carbohydrate oxidation is observed with increasing exercise. Lactate is not only an end product in skeletal muscle but also an important metabolic intermediate for mitochondrial oxidation. Recent advances in hyperpolarized MRS allow the measurement of substrate metabolism in vivo in real time. The aim of this study was to investigate the use of hyperpolarized 13C lactate as a substrate for metabolic studies in skeletal muscle in vivo. Carbohydrate metabolism in healthy rat skeletal muscle at rest was studied in different nutritional states using hyperpolarized [1-13C]lactate, a substrate that can be injected at physiological concentrations and leaves other oxidative processes undisturbed. 13C label incorporation from lactate into bicarbonate in fed animals was observed within seconds but was absent after an overnight fast, representing inhibition of the metabolic flux through pyruvate dehydrogenase (PDH). A significant decrease in 13C labeling of alanine was observed comparing the fed and fasted group, and was attributed to a change in cellular alanine concentration and not a decrease in enzymatic flux through alanine transaminase. We conclude that hyperpolarized [1-13C]lactate can be used to study carbohydrate oxidation in resting skeletal muscle at physiological levels. The herein proposed method allows probing simultaneously both PDH activity and variations in alanine tissue concentration, which are associated with metabolic dysfunctions. A simple alteration of the nutritional state demonstrated that the observed pyruvate, alanine, and bicarbonate signals are indeed sensitive markers to probe metabolic changes in vivo.