Automated metabolite identification from biological fluid <Superscript>1</Superscript>H NMR spectra

Introduction

Metabolite identification in biological samples using Nuclear Magnetic Resonance (NMR) spectra is a challenging task due to the complexity of the biological matrices.

Objectives

This paper introduces a new, automated computational scheme for the identification of metabolites in 1D ¹H NMR spectra based on the Human Metabolome Database.

Methods

The methodological scheme comprises of the sequential application of preprocessing, data reduction, metabolite screening and combination selection.

Results

The proposed scheme has been tested on the 1D ¹H NMR spectra of: (a) an amino acid mixture, (b) a serum sample spiked with the amino acid mixture, (c) 20 blood serum, (d) 20 human amniotic fluid samples, (e) 160 serum samples from publicly available database. The methodological scheme was compared against widely used software tools, exhibiting good performance in terms of correct assignment of the metabolites.

Conclusions

This new robust scheme accomplishes to automatically identify peak resonances in ¹H-NMR spectra with high accuracy and less human intervention with a wide range of applications in metabolic profiling.

     

Characterization of ankylosing spondylitis and rheumatoid arthritis using <Superscript>1</Superscript>H NMR-based metabolomics of human fecal extracts

Introduction

The differences in fecal metabolome between ankylosing spondylitis (AS)/rheumatoid arthritis (RA) patients and healthy individuals could be the reason for an autoimmune disorder.

Objectives

The study explored the fecal metabolome difference between AS/RA patients and healthy controls to clarify human immune disturbance.

Methods

Fecal samples from 109 individuals (healthy controls 34, AS 40, and RA 35) were analyzed by ¹H NMR spectroscopy. Data were analyzed with principal component analysis (PCA) and orthogonal projection to latent structure discriminant (OPLS-DA) analysis.

Results

Significant differences in the fecal metabolic profiles could distinguish AS/RA patients from healthy controls but could not distinguish between AS and RA patients. The significantly decreased metabolites in AS/RA patients were butyrate, propionate, methionine, and hypoxanthine. Significantly increased metabolites in AS/RA patients were taurine, methanol, fumarate, and tryptophan.

Conclusion

The metabolome variations in feces indicated AS and RA were two homologous diseases that could not be distinguished by ¹H NMR metabolomics.

     

ASICS: an automatic method for identification and quantification of metabolites in complex 1D <Superscript>1</Superscript>H NMR spectra

Introduction

Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.

Objectives

In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.

Methods

A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.

Results

The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.

Conclusion

ASICS is a completely automated procedure to identify and quantify metabolites in ¹H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

     

Modelling the acid/base <Superscript>1</Superscript>H NMR chemical shift limits of metabolites in human urine

Introduction

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.

     

Comprehensive <Superscript>1</Superscript>H NMR metabolic profiling of body fluids for differentiation of meningitis in adults

Introduction

Meningitis, a morbidly infectious central nervous system pathology is accompanied by acute inflammation of the meninges, causing raised intracranial pressure linked with serious neurological sequelae.

Objective

To observe the variation in the metabolic profile, that may occur in serum and urine along with CSF in adults using ¹H NMR spectroscopy, with an attempt of appropriate and timely treatment regimen.

Methods

The ¹H NMR-based metabolomics has been performed in 115 adult subjects for differentiating bacterial meningitis (BM) and tubercular meningitis (TBM).

Results

The discriminant function analysis (DFA) of the three bio-fluids collectively identified 3-hydroxyisovalerate, lactate, glucose, formate, valine, alanine, ketonic bodies, malonate and choline containing compounds (choline and GPC) as significant metabolites among cases versus control group. The differentiation of bacterial meningitis and tuberculous meningitis (BM vs. TBM) can be done on the basis of identification of 3-hydroxyisovalerate, isobutyrate and formate in case of CSF (with a correct classification of 78 %), alanine in serum (correct classification 60 %), valine and acetone in case of urine (correct classification 89.1 %). The NMR spectral bins based orthogonal signal correction principal component analysis score plots of significant metabolites obtained from DFA also provided group classification among cases versus control group in CSF, serum and urine samples. The variable importance in projection scores also identified similar significant metabolites as obtained from DFA, collectively in CSF, serum and urine samples, responsible for differentiation of meningitis.

Conclusion

The CSF contained metabolites which are formed during infection and inflammation, and these were also found in significant quantity in serum and urine samples.

     

<Superscript>1</Superscript>H NMR metabolic profiling of gastric cancer patients with lymph node metastasis

Introduction

Gastric cancer (GC) is a malignant tumor worldwide. As primary pathway for metastasis, the lymphatic system is an important prognostic factor for GC patients. Although the metabolic changes of gastric cancer have been investigated in extensive studies, little effort focused on the metabolic profiling of lymph node metastasis (LNM)-positive or negative GC patients.

Objectives

We performed ¹H NMR spectrum of GC tissue samples with and without LNM to identify novel potential metabolic biomarkers in the process of LNM of GC.

Methods

¹H NMR-based untargeted metabolomics approach combined with multivariate statistical analyses were used to study the metabolic profiling of tissue samples from LNM-positive GC patients (n?=?40), LNM-negative GC patients (n?=?40) and normal controls (n?=?40).

Results

There was a clear separation between GC patients and normal controls, and 33 differential metabolites were identified in the study. Moreover, GC patients were also well-classified according to LNM-positive or negative. Totally eight distinguishing metabolites were selected in the metabolic profiling of GC patients with LNM-positive or negative, suggesting the metabolic dysfunction in the process of LNM. According to further validation and analysis, especially BCAAs metabolism (leucine, isoleucine, valine), GSH and betaine may be as potential factors of diagnose and prognosis of GC patients with or without LNM.

Conclusion

To our knowledge, this is the first metabolomics study focusing on LNM of GC. The identified distinguishing metabolites showed a promising application on clinical diagnose and therapy prediction, and understanding the mechanism underlying the carcinogenesis, invasion and metastasis of GC.

     

Alteration in lipid composition differentiates breast cancer tissues: a <Superscript>1</Superscript>H HRMAS NMR metabolomic study

Introduction

Breast cancer is the most frequent diagnosed cancer among women with a mortality rate of 15% of all cancer related deaths in women. Breast cancer is heterogeneous in nature and produces plethora of metabolites allowing its early detection using molecular diagnostic techniques like magnetic resonance spectroscopy.

Objectives

To evaluate the variation in metabolic profile of breast cancer focusing on lipids as triglycerides (TG) and free fatty acids (FFA) that may alter in malignant breast tissues and lymph nodes from adjacent benign breast tissues by HRMAS ¹H NMR spectroscopy.

Methods

The ¹H NMR spectra recorded on 173 tissue specimens comprising of breast tumor tissues, adjacent tissues, few lymph nodes and overlying skin tissues obtained from 67 patients suffering from breast cancer. Multivariate statistical analysis was employed to identify metabolites acting as major confounders for differentiation of malignancy.

Result

Reduction in lipid content were observed in malignant breast tissues along with a higher fraction of FFA. Four small molecule metabolites e.g., choline containing compounds (Chocc), taurine, glycine, and glutamate were also identified as major confounders. The test set for prediction provided sensitivity and specificity of more than 90% excluding the lymph nodes and skin tissues.

Conclusion

Fatty acids composition in breast cancer using in vivo magnetic resonance spectroscopy (MRS) is gaining its importance in clinical settings (Coum et al. in Magn Reson Mater Phys Biol Med 29:1–4, 2016). The present study may help in future for precise evaluation of lipid classification including small molecules as a source of early diagnosis of invasive ductal carcinoma by employing in vivo magnetic resonance spectroscopic methods.

     

Investigation of species and environmental effects on rhubarb roots metabolome using <Superscript>1</Superscript>H NMR combined with high performance thin layer chromatography

Introduction

The pharmacological activities of medicinal plants are reported to be due to a wide range of metabolites, therein, the concentrations of which are greatly affected by many genetic and/or environmental factors. In this context, a metabolomics approach has been applied to reveal these relationships. The investigation of such complex networks that involve the correlation between multiple biotic and abiotic factors and the metabolome, requires the input of information acquired by more than one analytical platform. Thus, development of new metabolomics techniques or hyphenations is continuously needed.

Objectives

Feasibility of high performance thin-layer chromatography (HPTLC) were investigated as a supplementary tool for medicinal plants metabolomics supporting ¹H nuclear magnetic resonance (¹H NMR) spectroscopy.

Method

The overall metabolic difference of plant material collected from two species (Rheum palmatum and Rheum tanguticum) in different geographical locations and altitudes were analyzed by ¹H NMR- and HPTLC-based metabolic profiling. Both NMR and HPTLC data were submitted to multivariate data analysis including principal component analysis and orthogonal partial least square analysis.

Results

The NMR and HPTLC profiles showed that while chemical variations of rhubarb are in some degree affected by all the factors tested in this study, the most influential factor was altitude of growth. The metabolites responsible for altitude differentiation were chrysophanol, emodin and sennoside A, whereas aloe emodin, catechin, and rhein were the key species-specific markers.

Conclusion

These results demonstrated the potential of HTPLC as a supporting tool for metabolomics due to its high profiling capacity of targeted metabolic groups and preparative capability.

     

A multidimensional <Superscript>1</Superscript>H NMR lipidomics workflow to address chemical food safety issues

Introduction

Although it is still at a very early stage compared to its mass spectrometry (MS) counterpart, proton nuclear magnetic resonance (NMR) lipidomics is worth being investigated as an original and complementary solution for lipidomics. Dedicated sample preparation protocols and adapted data acquisition methods have to be developed to set up an NMR lipidomics workflow; in particular, the considerable overlap observed for lipid signals on 1D spectra may hamper its applicability.

Objectives

The study describes the development of a complete proton NMR lipidomics workflow for application to serum fingerprinting. It includes the assessment of fast 2D NMR strategies, which, besides reducing signal overlap by spreading the signals along a second dimension, offer compatibility with the high-throughput requirements of food quality characterization.

Method

The robustness of the developed sample preparation protocol is assessed in terms of repeatability and ability to provide informative fingerprints; further, different NMR acquisition schemes—including classical 1D, fast 2D based on non-uniform sampling or ultrafast schemes—are evaluated and compared. Finally, as a proof of concept, the developed workflow is applied to characterize lipid profiles disruption in serum from β-agonists diet fed pigs.

Results

Our results show the ability of the workflow to discriminate efficiently sample groups based on their lipidic profile, while using fast 2D NMR methods in an automated acquisition framework.

Conclusion

This work demonstrates the potential of fast multidimensional ¹H NMR—suited with an appropriate sample preparation—for lipidomics fingerprinting as well as its applicability to address chemical food safety issues.

     

Response of Degarelix treatment in human prostate cancer monitored by HR-MAS <Superscript>1</Superscript>H NMR spectroscopy

Introduction

The androgen receptor (AR) is the master regulator of prostate cancer cell metabolism. Degarelix is a novel gonadotrophin-releasing hormone blocker, used to decrease serum androgen levels in order to treat advanced human prostate cancer. Little is known of the rapid metabolic response of the human prostate cancer tissue samples to the decreased androgen levels.

Objectives

To investigate the metabolic responses in benign and cancerous tissue samples from patients after treatment with Degarelix by using HRMAS ¹H NMR spectroscopy.

Methods

Using non-destructive HR-MAS ¹H NMR spectroscopy we analysed the metabolic changes induced by decreased AR signalling in human prostate cancer tissue samples. Absolute concentrations of the metabolites alanine, lactate, glutamine, glutamate, citrate, choline compounds [t-choline = choline + phosphocholine (PC) + glycerophosphocholine (GPC)], creatine compounds [t-creatine = creatine (Cr) + phosphocreatine (PCr)], taurine, myo-inositol and polyamines were measured in benign prostate tissue samples (n = 10), in prostate cancer specimens from untreated patients (n = 7) and prostate cancer specimens from patients treated with Degarelix (n = 6).

Results

Lactate, alanine and t-choline concentrations were significantly elevated in high-grade prostate cancer samples when compared to benign samples in untreated patients. Decreased androgen levels resulted in significant decreases of lactate and t-choline concentrations in human prostate cancer biopsies.

Conclusions

The reduced concentrations of lactate and t-choline metabolites due to Degarelix could in principle be monitored by in vivo ¹H MRS, which suggests that it would be possible to monitor the effects of physical or chemical castration in patients by that non-invasive method.

     

<Superscript>1</Superscript>H NMR based metabolomic approach to monitoring of the head and neck cancer treatment toxicity

Introduction

Anticancer treatment results in temporary or permanent toxicity considered as changes in normal tissues and/or involved regions. The net effect is mirrored in morphological, functional and molecular disturbances—thus in a systemic response of the human body. To date, specific NMR biomarkers of radiation therapy toxicity in head and neck squamous cell carcinoma (HNSCC) patients are scarce or even missing.

Objectives

We aimed to investigate molecular processes reflecting acute radiation sequelae (ARS) in HNSCC patients using NMR-based metabolomics of blood serum.

Methods

45 patients with HNSCC were treated with radiotherapy (RT) or chemoradiotherapy (CHRT). Blood samples were collected within a week after RT/CHRT completion. Patients were divided into two classes (of high and low ARS) on the basis of the highest individual ARS value observed during the treatment. ¹H NMR spectra of serum samples were acquired on a Bruker 400.13 MHz spectrometer at 310 K and analyzed using principal component analysis and orthogonal partial least squares discriminant analysis. Additional statistical analyses were performed on quantified metabolites.

Results

1D projections of the J-resolved NMR spectra seem to be of the great potential in the quest for the HNSCC treatment toxicity biomarker. The metabolic features characteristic for high ARS are the increased signals of N-acetyl-glycoprotein and acetate, as well as decrease of choline and the metabolites involved in energy metabolism: branched chain amino acids (BCAAs), alanine, creatinine and carnitine. Furthermore, we observed significant correlations between N-acetyl-glycoprotein and clinical markers of inflammation as well as acetate and a percentage-weight-loss during the treatment. CRP was also negatively correlated with alanine and BCAAs.

Conclusion

NMR-based metabolomics provides relevant biomarkers of RT/CHRT toxicity (ARS) in HNSCC patients. The results indicate at least three concomitant processes related to high ARS: inflammation, altered energy metabolism and disturbed membrane metabolism, and indicate an exciting potential of J-resolved NMR spectroscopy combined with multivariate projection techniques.

     

Miniaturized <Superscript>1</Superscript>H-NMR method for analyzing limited-quantity samples applied to a mouse model of Leigh disease

Introduction

The analysis of limited-quantity samples remains a challenge associated with mouse models, especially for multi-platform metabolomics studies. Although inherently insensitive, the highly specific characteristics of nuclear magnetic resonance (NMR) spectroscopy make it an advantageous platform for global metabolite profiling, particularly in mitochondrial disease research.

Objectives

Show method equivalency between a well-established standard operating protocol (SOP) and our novel miniaturized ¹H-NMR method.

Method

The miniaturized method was performed in a 2 mm NMR tube on a standard 500 MHz NMR spectrometer with a 5 mm triple-resonance inverse TXI probe at room temperature.

Results

Firstly, using synthetic urine spiked with low (50 µM), medium (250 µM) and high (500 µM) levels (n?=?10) of nine standards, both the SOP and miniaturized method were shown to have acceptable precision (CV?<?15%), relative accuracy (80–120%), and linearity (R²?>?0.95), except for taurine. Furthermore, statistical equivalence was shown using the two one-sided test. Secondly, pooled mouse quadriceps muscle extract was used to further confirm method equivalence (n?=?3), as well as explore the analytical dynamics of this novel approach by analyzing more-concentrated versions of samples (up to 10× concentration) to expand identification of metabolites qualitatively, with quantitative linearity. Lastly, we demonstrate the new technique’s application in a pilot metabolomics study using minute soleus muscle tissue from a mouse model of Leigh syndrome using Ndufs4 KO mice.

Conclusion

We demonstrate method equivalency, supporting our novel miniaturized ¹H-NMR method as a financially feasible alternative to cryoprobe technology—for limited-quantity biological samples in metabolomics studies that requires a volume one-tenth of the SOP.

     

Characterizing alternative feeds for rainbow trout (<Emphasis Type="Italic">O. mykiss</Emphasis>) by <Superscript>1</Superscript>H NMR metabolomics

Introduction

Fish feed formulations are constantly evolving to improve the quality of diets for farmed fish and to ensure the sustainability of the aquaculture sector. Nowadays, insect, microalgae and yeast are feedstuff candidates for new feeds. However, the characterization of aquafeed is still based on proximate and targeted analyses which may not be sufficient to assess feed quality.

Objectives

Our aim was to highlight the soluble compounds that specifically differ between selected plant-based feeds complemented with alternative feedstuffs and discuss their origin and potential for fish nutrition.

Methods

A growth trial was carried out to evaluate growth performances and feed conversion ratios of fish fed plant-based, commercial, insect, spirulina and yeast feeds. ¹H NMR metabolomics profiling of each feed was performed using a CPMG sequence on polar extracts. Spectra were processed, and data were analyzed using multivariate and univariate analyses to compare alternative feeds to a plant-based feed.

Results

Fish fed insect or yeast feed showed the best growth performances associated with the lowest feed conversion ratios compared to plant-based feed. Soluble compound ¹H NMR profiles of insect and spirulina alternative feeds differed significantly from the plant-based one that clustered with yeast feed. In insect and spirulina feeds, specific differences compared to plant-based feed concerned glycerol and 3-hydroxybutyrate, respectively.

Conclusion

This strategy based on compositional differences between plant-based and alternative feeds can be useful for detecting compounds unsuspected until now that could impact fish metabolism.

     

<Superscript>1</Superscript>H NMR-based metabolomics approach reveals metabolic alterations in response to dietary imbalances in <Emphasis Type="Italic">Megalobrama amblycephala</Emphasis>

Introduction

High-fat and high-carbohydrate diets cause a number of metabolic disorders in mammals. However, little is known about metabolomic changes caused by dietary imbalances in fish.

Objectives

The objective of this study was to assess the impacts of high-fat diet (HFD), high-carbohydrate diet (HCD) and high-fat-high-carbohydrate diet (HFHCD) on metabolites in a farmed cyprinid fish Megalobrama amblycephala.

Methods

We have employed the ¹H NMR-based metabolomic approach to measure the concentrations of metabolites in plasma and liver of four different diet groups: HFD, HCD, HFHCD and control. Multivariate statistical analyses were used to determine significantly changed metabolites between all group-pairs.

Results

All three test diets have affected metabolic profiles, phenotypes and clinical chemistry. High-fat diets (HFD, HFHCD) resulted in a higher average weight than HCD, but high-carbohydrate diets (HCD, HFHCD) caused signs of liver damage. HCD has resulted in elevated metabolites in energy pathways, leading to further disturbances in creatine pathway. Excess of carbohydrate and lipid metabolism products in the HFHCD group appears to have caused “congestion” of the TCA cycle, causing a significant decline in the numbers of amino acids entering the cycle, which in turn resulted in elevated levels of seven amino acids in this group. Gut microbiota metabolites (TMA) exhibited a strong positive correlation with the carbohydrate content and a negative correlation with the fat content in diets.

Conclusion

These results provide an important insight into the diet-affected metabolic disorders that often lead to financial losses in the aquaculture of Megalobrama amblycephala.

Graphical Abstract

Open image in new window

     

rDolphin: a GUI R package for proficient automatic profiling of 1D <Superscript>1</Superscript>H-NMR spectra of study datasets

Introduction

Adoption of automatic profiling tools for ¹H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices.

Objectives

To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process.

Methods

rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality.

Results

The information and quality achieved in two public datasets of complex matrices are maximized.

Conclusion

rDolphin is an open-source R package (http://github.com/danielcanueto/rDolphin) able to provide the best balance between accuracy, reproducibility and ease of use.

     

Pathway-specific metabolome analysis with <Superscript>18</Superscript>O<Subscript>2</Subscript>-labeled <Emphasis Type="Italic">Medicago truncatula</Emphasis> via a mass spectrometry-based approach

Introduction

Oxygen from carbon dioxide, water or molecular oxygen, depending on the responsible enzyme, can lead to a large variety of metabolites through chemical modification.

Objectives

Pathway-specific labeling using isotopic molecular oxygen (¹⁸O₂) makes it possible to determine the origin of oxygen atoms in metabolites and the presence of biosynthetic enzymes (e.g., oxygenases). In this study, we established the basis of ¹⁸O₂-metabolome analysis.

Methods

¹⁸O₂ labeled whole Medicago truncatula seedlings were prepared using ¹⁸O₂-air and an economical sealed-glass bottle system. Metabolites were analyzed using high-accuracy and high-resolution mass spectrometry. Identification of the metabolite was confirmed by NMR following UHPLC–solid-phase extraction (SPE).

Results

A total of 511 peaks labeled by ¹⁸O₂ from shoot and 343 peaks from root were annotated by untargeted metabolome analysis. Additionally, we identified a new flavonoid, apigenin 4′-O-[2′-O-coumaroyl-glucuronopyranosyl-(1–2)-O-glucuronopyranoside], that was labeled by ¹⁸O₂. To the best of our knowledge, this is the first report of apigenin 4′-glucuronide in M. truncatula. Using MSⁿ analysis, we estimated that ¹⁸O atoms were specifically incorporated in apigenin, the coumaroyl group, and glucuronic acid. For apigenin, an ¹⁸O atom was incorporated in the 4′-hydroxy group. Thus, non-specific incorporation of an ¹⁸O atom by recycling during one month of labeling is unlikely compared with the more specific oxygenase-catalyzing reaction.

Conclusion

Our finding indicated that ¹⁸O₂ labeling was effective not only for the mining of unknown metabolites which were biosynthesized by oxygenase-related pathway but also for the identification of metabolites whose oxygen atoms were derived from oxygenase activity.

     

A comparison of human serum and plasma metabolites using untargeted <Superscript>1</Superscript>H NMR spectroscopy and UPLC-MS

Introduction

Differences in the metabolite profiles between serum and plasma are incompletely understood.

Objectives

To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes.

Methods

We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG ¹H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis.

Results

Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples.

Conclusions

Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.

     

Measurement of the <Superscript>15</Superscript>N/<Superscript>14</Superscript>N ratio of phenylalanine in fermentation matrix by isotope ratio mass spectrometry

Objectives

To determine the origin of ¹⁵N-labeled phenylalanine in microbial metabolic flux analysis using ¹⁵N as a tracer, a method for measuring phenylalanine δ¹⁵N using HPLC coupled with elemental analysis-isotope ratio mass spectrometry (EA-IRMS) was developed.

Results

The original source of the ¹⁵N-labeled phenylalanine was determined using this new method that consists of three steps: optimization of the HPLC conditions, evaluation of the isotope fractionation effects, and evaluation of the effect of pre-processing on the phenylalanine nitrogen stable isotope. In addition, the use of a ¹⁵N-labeled inorganic nitrogen source, rather than ¹⁵N-labeled amino acids, was explored using this method.

Conclusions

The method described here can also be applied to the analysis of metabolic flux.

     

Automation of chemical assignment for identifying molecular formula of S-containing metabolites by combining metabolomics and chemoinformatics with <Superscript>34</Superscript>S labeling

Introduction

Sulfur-containing metabolites (S-metabolites) in organisms including plants have unique benefits to humans. So far, few analytical methods have explored such metabolites.

Objectives

We aimed to develop an automatic chemically assigning platform by metabolomics and chemoinformatics with ³⁴S labeling to identify the molecular formula of S-metabolites.

Methods

Direct infusion analysis using Fourier transform ion cyclotron resonance-mass spectrometry provided ultra-high-resolution data including clearly separated isotopic ions—¹⁵N, ³⁴S, ¹⁸O, and ¹³C₂—in the flower, silique, leaf, stem, and root of non-labeled and ³⁴S-labeled Arabidopsis thaliana. Chemoinformatic analysis assigned several elemental compositions of S-metabolites to the acquired S-containing monoisotopic ions using mass accuracy and peak resolution in the non-labeled metabolome data. Possible elemental compositions were characterized on the basis of diagnostic scores of the exact mass and isotopic ion pattern, and a database search. By comparing elemental compositions assigned to the ³⁴S-labeled data with those assigned to the non-labeled data, the elemental composition of S-metabolites were determined. The determined elemental compositions were surveyed using the in-house database, which stores molecular formulae downloaded from metabolome databases.

Results

We identified 35 molecular formulae for known S-metabolites and characterized 72 for unknown. Chemoinformatics required around 1.5 min to analyze a pair of the non-labeled and ³⁴S-labeled data of the organ.

Conclusion

In this study, we developed an automation platform for automatically identifying the presence of S-metabolites. We identified the molecular formula of known S-metabolites, which are accessible in free databases, together with that of unknown. This analytical method did not focus on identifying the structure of S-metabolites, but on the automatic identification of their molecular formula.