Despite the use of buffering agents the 1H NMR spectra of biofluid samples in metabolic profiling investigations typically suffer from extensive peak frequency shifting between spectra. These chemical shift changes are mainly due to differences in pH and divalent metal ion concentrations between the samples. This frequency shifting results in a correspondence problem: it can be hard to register the same peak as belonging to the same molecule across multiple samples. The problem is especially acute for urine, which can have a wide range of ionic concentrations between different samples.
Objectives
To investigate the acid, base and metal ion dependent 1H NMR chemical shift variations and limits of the main metabolites in a complex biological mixture.
Methods
Urine samples from five different individuals were collected and pooled, and pre-treated with Chelex-100 ion exchange resin. Urine samples were either treated with either HCl or NaOH, or were supplemented with various concentrations of CaCl2, MgCl2, NaCl or KCl, and their 1H NMR spectra were acquired.
Results
Nonlinear fitting was used to derive acid dissociation constants and acid and base chemical shift limits for peaks from 33 identified metabolites. Peak pH titration curves for a further 65 unidentified peaks were also obtained for future reference. Furthermore, the peak variations induced by the main metal ions present in urine, Na+, K+, Ca2+ and Mg2+, were also measured.
Conclusion
These data will be a valuable resource for 1H NMR metabolite profiling experiments and for the development of automated metabolite alignment and identification algorithms for 1H NMR spectra.
The term ‘metabolome’ was introduced to the scientific literature in September 1998.
Aim and key scientific concepts of the review
To mark its 18-year-old ‘coming of age’, two of the co-authors of that paper review the genesis of metabolomics, whence it has come and where it may be going.
Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.
Objectives
Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs.
Methods
A literature search of the current relevant primary research was performed.
Results
Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.
Conclusion
Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.
Studies on ragweed and birch pollen extracts suggested that the adenosine content is an important factor in allergic sensitization. However, exposure levels from other pollens and considerations of geographic and seasonal factors have not been evaluated.
Objective
This study compared the metabolite profile of pollen species important for allergic disease, specifically measured the adenosine content, and evaluated exposure to pollen-derived adenosine.
Methods
An NMR metabolomics approach was used to measure metabolite concentrations in 26 pollen extracts. Pollen count data was analyzed from five cities to model exposure.
Results
A principal component analysis of the various metabolites identified by NMR showed that pollen extracts could be differentiated primarily by sugar content: glucose, fructose, sucrose, and myo-inositol. In extracts of 10 mg of pollen/ml, the adenosine was highest for grasses (45 µM) followed by trees (23 µM) and weeds (19 µM). Pollen count data showed that tree pollen was typically 5–10 times the amount of other pollens. At the daily peaks of tree, grass, and weed season the pollen-derived adenosine exposure per day is likely to be only 1.1, 0.11, and 0.12 µg, respectively. Seasonal models of pollen exposure and respiration suggest that it would be a rare event limited to tree pollen season for concentrations of pollen-derived adenosine to approach physiological levels.
Conclusion
Sugar content and other metabolites may be useful in classifying pollens. Unless other factors create localized exposures that are very different from these models, pollen-derived adenosine is unlikely to be a major factor in allergic sensitization.
The chemical sensitivity of urine metabolomics analysis is greatly compromised due to the large amounts of inorganic salts in urine (NaCl, KCl), which are detrimental to analytical instrumentation, e.g. chromatographic columns or mass spectrometers. Traditional desalting approaches applied to urine pretreatment suffer from the chemical losses, which reduce the information depth of analysis.
Objectives
We aimed to test a simple approach for the simultaneous preconcentration and desalting of organic solutes in urine based on the collection of induced bursting bubble aerosols above the surface of urine samples.
Method
Bursting bubbles were generated at ambient conditions by feeding gas through an air diffuser at the bottom of diluted (200 times in ultrapure water) urine solution (50–500 mL). Collected aerosols were analyzed by the direct-infusion electrospray ionization mass spectrometry (ESI–MS).
Results
The simultaneous preconcentration (ca. 6–12 fold) and desalting (ca. six–tenfold) of organic solutes in urine was achieved by the bursting bubble sample pretreatment, which allowed ca. three-times higher number of identified urine metabolites by high-resolution MS analysis. No chemical losses due to bubbling were observed. The increased degree of MS data clustering was demonstrated on the principal component analysis of data sets from the urine of healthy people and from the urine people with renal insufficiency. At least ten times higher sensitivity of trace drug detection in urine was demonstrated for clenbuterol and salbutamol.
Conclusion
Our results indicate the high versatility of bubble bursting as a simple pretreatment approach to enhance the chemical depth and sensitivity of urine analysis. The approach could be attractive for personalized medicine as well as for the diagnostics of renal disorders of different etiology (diabetic nephropathy, chronic renal failure, transplant-associated complications, oncological disorders).
Graphical Abstract
Urine desalting and preconcentration in bursting bubbles.
Dysregulation of acylcarnitines (AcylCNs) and amino acids metabolism have implicated in abnormality of fatty acid oxidation in type 2 diabetes (T2D). However, it is not well known whether altered plasma AcylCN, and amino acid profiles are associated with albuminuria or diabetic nephropathy (DN) in T2D.
Objective
The aim of this study was to elucidate alterations in plasma levels of AcylCNs and amino acids with respect to the T2D patients with various stages of albuminuria.
Methods
We recruited 52 healthy subjects as control, and 156 T2D patients which were divided into 52 normoalbuminuria, 52 microalbuminuria, and 52 macroalbuminuria. Plasma 37 AcylCNs and 12 amino acids were analyzed by tandem mass spectrometry.
Results
We found that T2D with normoalbuminuria and microalbuminuria had lower shot-, medium-, and long-chain AcylCNs, whereas T2D with macroalbuminuria had higher short-and medium-chain AcylCNs and lower long-chain AcylCNs than healthy subjects. Moreover, estimated glomerular filtration rate (eGFR) was a negative, independent and significant predictor of albumin to creatinine ratio (ACR) levels (β = ?0.376, P < 0.001), whereas plasma Low-density lipoprotein cholesterol (LDL-C) was significantly and positively associated with ACR levels (β = 0.169, P = 0.049). Furthermore, multivariate ordinal logistic regression analysis revealed that isobutyrylcarnitine (C4) was a positive, independent, and significant predictor of ACR levels with higher odds of having T2D patients with progression normoalbuminuria to microalbuminuria [OR = 9.93, 95 % CI (3.51–28.05), P < 0.001].
Conclusions
The findings suggest that plasma C4 may serve as a potential biomarker for the early stages of DN.
Metabolomics has become a valuable tool in many research areas. However, generating metabolomics-based biochemical profiles without any related bioactivity is only of indirect value in understanding a biological process. Therefore, metabolomics research could greatly benefit from tools that directly determine the bioactivity of the detected compounds.
Objective
We aimed to combine LC–MS metabolomics with a cell based receptor assay. This combination could increase the understanding of biological processes and may provide novel opportunities for functional metabolomics.
Methods
We developed a flow through biosensor with human cells expressing both the TRPV1, a calcium ion channel which responds to capsaicin, and the fluorescent intracellular calcium ion reporter, YC3.6. We have analysed three contrasting Capsicum varieties. Two were selected with contrasting degrees of spiciness for characterization by HPLC coupled to high mass resolution MS. Subsequently, the biosensor was then used to link individual pepper compounds with TRPV1 activity.
Results
Among the compounds in the crude pepper fruit extracts, we confirmed capsaicin and also identified both nordihydrocapsaicin and dihydrocapsaicin as true agonists of the TRPV1 receptor. Furthermore, the biosensor was able to detect receptor activity in extracts of both Capsicum fruits as well as a commercial product. Sensitivity of the biosensor to this commercial product was similar to the sensory threshold of a human sensory panel.
Conclusion
Our results demonstrate that the TRPV1 biosensor is suitable for detecting bioactive metabolites. Novel opportunities may lie in the development of a continuous functional assay, where the biosensor is directly coupled to the LC–MS.
Although smoking is a major risk factor for pharyngolaryngeal cancer, most smokers do not develop pharyngolaryngeal cancer.
Objectives
In the prospective Korean Cancer Prevention Study-II (KCPS-II), we investigated the application of metabolomics to differentiate smokers with incident pharyngolaryngeal cancer (pharyngolaryngeal cancer group) from smokers who remained free from cancer (controls) during a mean follow-up period of 7 years and aimed to discover valuable early biomarkers of pharyngolaryngeal cancer.
Methods
We used baseline serum samples from 30 smoking men with incident pharyngolaryngeal cancer and 59 age-matched cancer-free smoking men. Metabolic alterations associated with the incidence of pharyngolaryngeal cancer were investigated by performing metabolomics on baseline serum samples using ultra-performance liquid chromatography-linear-trap quadrupole-Orbitrap mass spectrometry.
Results
Compared to the control group, the pharyngolaryngeal cancer group showed significantly higher oxidized LDL levels. Seventeen metabolites were differentially abundant between the two groups. At baseline, compared to controls, smokers with incident pharyngolaryngeal cancer during follow-up showed significantly higher levels of pyroglutamic acid (glutathione metabolism) but lower levels of lysophosphatidylcholines (lysoPCs) C14:0, C15:0, C16:0, C17:0, C18:0, and C20:5; glycerophosphocholine; PC C36:5; lysoPEs C16:0, C20:1, and C22:0 (glycerophospholipid metabolism); SM (d18:0/16:1); and SM (d18:1/18:1) (sphingomyelin metabolism). Furthermore, smokers with incident pharyngolaryngeal cancer showed significantly higher levels of oleamide and lower levels of tryptophan and linoleyl carnitine at baseline than cancer-free smokers.
Conclusion
This prospective study showed the clinical relevance of dysregulated metabolism of glutathione, glycerophospholipids and sphingolipids to the pathogenesis of pharyngolaryngeal cancer among smokers. These data suggest that the dysregulation of these metabolic processes may be a key mechanism underlying pharyngolaryngeal cancer progression and development.
Lactic acid bacteria (LAB) play an important role in the food industry as starter cultures to manufacture fermented food, and as probiotics. In recent years, there has been an increasing interest in using LAB cultures for biopreservation of food products. It is therefore of great interest to study the detailed metabolism of these bacteria.
Objectives
This study aimed at developing an efficient analytical protocol for real-time in vitro NMR measurements of LAB fermentations, from sample preparation, over data acquisition and preprocessing, to the extraction of the kinetic metabolic profiles.
Method
The developed analytical protocol is applied to an experimental design with two LAB strains (Lactobacillus rhamnosus DSM 20021 and Lactobacillus plantarum subsp. plantarum DSM 20174), two initial pH levels (pHi 6.5 and 5.5), two levels of glucose concentration (2.5 and 0.25 g/l), and two batch fermentation replicates.
Results
The design factors proved to be strongly significant and led to interesting biological information. The protocol allowed for detailed real-time kinetic analysis of 11 major metabolites involved in the glycolysis, pyruvate catabolism, amino acid catabolism and cell energy metabolism. New biological knowledge was obtained about the different patterns of glutamine and aspartic acid consumption by the two strains. It was observed that L. plantarum consumes more glutamine at low pH (pH 5.5) whereas the opposite applies to L. rhamnosus. Regarding aspartic acid, both of the strains consume it higher at low pH, and overall L. plantarum consumes it more. L. rhamnosus did not consume aspartic acid at pH 6.5.
Conclusion
The developed analytical protocol for real-time in vitro NMR measurements of bacterial metabolism allows a relatively easy investigation of different fermentation factors such as new strains, new substrates, cohabitations, temperature, and pH and has a great potential in biopreservation studies to discover new efficient bioprotective cultures.
