Performance Assessment in Fingerprinting and Multi Component Quantitative NMR Analyses

A versatile economic strategy by HPLC-CAD for quantification of structurally diverse markers in quality control of Shengmai Formula from raw materials to preparations

BACKGROUND

Shengmai Formula (SMF), a classic formula in treating Qi-Yin deficiency, is composed of Ginseng Radix et Rhizoma Rubra (GRR), Ophiopogon Radix (OR), and Schisandra chinensis Fructus (SC), and has been developed into various dosage forms including Shengmai Yin Oral Liquid (SMY), Shengmai Capsules (SMC), and Shengmai Injection (SMI). The pharmacological effects of compound Chinese medicine are attributed to the integration of multiple components. Yet the quality criteria of SMF are limited to monitoring schisandrol A or ginsenosides Rg1 and Re, but none for OR. Since the complexity of raw materials and preparations, establishing a economical and unified method for SMF is challenging. It is urgent to simultaneously quantify multiple components with different structures using a universal method for quality control of SMF. Charged aerosol detector (CAD) overcame the above shortcomings owing to its characteristics of high responsiveness, nondiscrimination, and low cost.

PURPOSE

We aimed to establish a versatile analysis strategy using HPLC-CAD for simultaneously quantifying the structurally diverse markers in quality control of SMF from raw materials to preparations.

METHOD

By optimizing the column, mobile phase, column temperature, flow rate, and CAD parameters, a HPLC-CAD method that integrated multi-component characterization, authenticity identification, transfer information of raw materials and quantitative determination of Shengmai preparations was established.

RESULTS

In total 50 components from SMF were characterized (28 in GRR, 13 in SC, and 9 in OR). The differences in raw materials between species of SC and Schisandrae sphenantherae Fructus (SS), processing methods of Ginseng Radix (GR) and GRR, and locations of OR from Sichuan (ORS) and Zhejiang (ORZ) were compared. Fourteen components in 19 batches of SMY, SMC and SMI from different manufacturers were quantified, including 11 ginsenosides and 3 lignans. The multivariate statistical analysis results further suggested that Rb1, Rg1 and Ro were the main differences among Shengmai preparations.

CONCLUSION

The established versatile analysis strategy based on HPLC-CAD was proven sensitive, simple, convenient, overcoming the discriminatory effect of UV detector, revealing the composition and transfer information of SMF and applicable for authentication of the ingredient herbs and improving the quality of Shengmai preparations.

Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas

Soft tissue sarcomas (STSs) are rare malignant tumors that are difficult to prognosticate using currently available instruments. Omics sciences could provide more accurate and individualized survival predictions for patients with metastatic STS. In this pilot, hypothesis-generating study, we integrated clinicopathological variables with proton nuclear magnetic resonance (1H NMR) plasma metabolomic and lipoproteomic profiles, capturing both tumor and host characteristics, to identify novel prognostic biomarkers of 2-year survival. Forty-five metastatic STS (mSTS) patients with prevalent leiomyosarcoma and liposarcoma histotypes receiving trabectedin treatment were enrolled. A score combining acetate, triglycerides low-density lipoprotein (LDL)-2, and red blood cell count was developed, and it predicts 2-year survival with optimal results in the present cohort (84.4% sensitivity, 84.6% specificity). This score is statistically significant and independent of other prognostic factors such as age, sex, tumor grading, tumor histotype, frailty status, and therapy administered. A nomogram based on these 3 biomarkers has been developed to inform the clinical use of the present findings.

Antarctic Soil Metabolomics: A Pilot Study

In Antarctica, ice-free areas can be found along the coast, on mountain peaks, and in the McMurdo Dry Valleys, where microorganisms well-adapted to harsh conditions can survive and reproduce. Metabolic analyses can shed light on the survival mechanisms of Antarctic soil communities from both coastal sites, under different plant coverage stages, and inner sites where slow-growing or dormant microorganisms, low water availability, salt accumulation, and a limited number of primary producers make metabolomic profiling difficult. Here, we report, for the first time, an efficient protocol for the extraction and the metabolic profiling of Antarctic soils based on the combination of NMR spectroscopy and mass spectrometry (MS). This approach was set up on samples harvested along different localities of Victoria Land, in continental Antarctica, devoid of or covered by differently developed biological crusts. NMR allowed for the identification of thirty metabolites (mainly sugars, amino acids, and organic acids) and the quantification of just over twenty of them. UPLC-MS analysis identified more than twenty other metabolites, in particular flavonoids, medium- and long-chain fatty acids, benzoic acid derivatives, anthracenes, and quinones. Our results highlighted the complementarity of the two analytical techniques. Moreover, we demonstrated that their combined use represents the "gold standard" for the qualitative and quantitative analysis of little-explored samples, such as those collected from Antarctic soils.

Non-Targeted NMR Method to Assess the Authenticity of Saffron and Trace the Agronomic Practices Applied for Its Production

The development of analytical methods aimed at tracing agri-food products and assessing their authenticity is essential to protect food commercial value and human health. An NMR-based non-targeted method is applied here to establish the authenticity of saffron samples. Specifically, 40 authentic saffron samples were compared with 18 samples intentionally adulterated by using turmeric and safflower at three different concentration levels, i.e., 5, 10, and 20 wt%. Statistical processing of NMR data furnished useful information about the main biomarkers contained in aqueous and dimethyl sulfoxide extracts, which are indicative of the presence of adulterants within the analyzed matrix. Furthermore, a discrimination model was developed capable of revealing the type of agronomic practice adopted during the production of this precious spice, distinguishing between organic and conventional cultivation. The main objective of this work was to provide the scientific community involved in the quality control of agri-food products with an analytical methodology able to extract useful information quickly and reliably for traceability and authenticity purposes. The proposed methodology turned out to be sensitive to minor variations in the metabolic composition of saffron that occur in the presence of the two adulterants studied. Both adulterants can be detected in aqueous extracts at a concentration of 5 wt%. A lower limit of detection was observed for safflower contained in organic extracts in which case the lowest detectable concentration was 20%.

A Real-Life Reproducibility Assessment for NMR Metabolomics

Nuclear magnetic resonance (NMR) metabolomics is currently popular enough to attract both specialized and non-specialized NMR groups involving both analytical trained personnel and newcomers, including undergraduate students. Recent interlaboratory studies performed by established NMR metabolomics groups demonstrated high reproducibility of the state-of-the-art NMR equipment and SOPs. There is, however, no assessment of NMR reproducibility when mixing both analytical experts and newcomers. An interlaboratory assessment of NMR quantitation reproducibility was performed using two NMR instruments belonging to different laboratories and involving several operators with different backgrounds and metabolomics expertise for the purpose of assessing the limiting factors for data reproducibility in a multipurpose NMR environment. The variability induced by the operator, automatic pipettes, NMR tubes and NMR instruments was evaluated in order to assess the limiting factors for quantitation reproducibility. The results estimated the expected reproducibility data in a real-life multipurpose NMR laboratory to a maximum 4% variability, demonstrating that the current NMR equipment and SOPs may compensate some of the operator-induced variability.

Development of a Quantitative NMR Method for Direct and Simultaneous Determination of Four Flavonoids in Scutellaria baicalensis georgi Extracts

Introduction:

Scutellaria Baicalensis Georgi (SBG), a Traditional Chinese Medicine (TCM) used for the treatment of antiviral therapy, contains many flavonoids.

Materials and Methods:

A simple, fast and accurate 1H quantitative nuclear magnetic resonance (1HqNMR) method was established for simultaneously determination of four flavonoids (Baicalin, Baicalein, Wogonin and Wogonoside) in SBG extract by using 3,4,5-trichloropyridine as an internal standard (IS). All the NMR determination work was performed at 308 K on an NMR sample tube inserted with a co-axial tube. The NMR sample tube was added with the sample (SBG extract and IS), the co-axial insert tube was added with D2O. Quantification of four flavonoids was carried out by calculating the relative peak area ratio of the selected proton signals from the target compounds and the IS.

Results:

The validated 1H-qNMR method was successfully used to determine the four flavonoids in 9 batches of SBG from different origins and the results were in good accordance with those obtained from HPLC.

Conclusion:

The established 1H-qNMR method could be used as a powerful tool for the quality control of SBG from different sources and had potential in the quantification of main components from other TCMs.

1H-NMR-Based Analysis for Exploring Knee Synovial Fluid Metabolite Changes after Local Cryotherapy in Knee Arthritis Patients

Rehabilitation using cryotherapy has widely been used in inflammatory diseases to relieve pain and decrease the disease activity. The aim of this study was to explore the metabolite changes in inflammatory knee-joint synovial fluids following local cryotherapy treatment (ice or cold CO2). We used proton nuclear magnetic resonance (1H NMR) spectroscopy to assess the metabolite patterns in synovial fluid (SF) in patients with knee arthritis (n = 46) before (D0) and after (D1, 24 h later) two applications of local cryotherapy. Spectra from aqueous samples and organic extracts were obtained with an 11.75 Tesla spectrometer. The metabolite concentrations within the SF were compared between D1 and D0 using multiple comparisons with the application of a false discovery rate (FDR) adjusted at 10% for each metabolite. A total of 32 metabolites/chemical structures were identified including amino acids, organic acids, fatty acids or sugars. Pyruvate, alanine, citrate, threonine was significantly higher at D1 vs D0 (p < 0.05). Tyrosine concentration significantly decreases after cryotherapy application (p < 0.001). We did not observe any effect of gender and cooling technique on metabolite concentrations between D0 and D1 (p > 0.05). The present study provides new insight into a short-term effect of cold stimulus in synovial fluid from patients with knee arthritis. Our observations suggest that the increased level of metabolites involved in energy metabolism may explain the underlying molecular pathways that mediate the antioxidant and anti-inflammatory capacities of cryotherapy.

Potential of 2D qNMR spectroscopy for distinguishing chicken breeds based on the metabolic differences

Two-dimensional quantitative NMR spectroscopy (2D qNMR) was set up and multivariate analyses were performed on metabolites obtained from breast meat extracts of broilers and four native chicken (KNC) strains. It can accurately identify more metabolites than 1D ¹H NMR via separation of peak overlap by dimensional expansion with good linearity, but has a problem of numerical quantification; Complementation of 1D and 2D qNMR is necessary. Among breeds, KNC-D had higher amounts of free amino acids, sugars, and bioactive compounds than others. Noticeable differences between KNCs and broilers were observed; KNCs contained higher amounts of inosine 5'-monophosphate, α-glucose, anserine, and lactic acid, and lower amounts of free amino acids and their derivatives. The 2D qNMR combined with multivariate analyses distinguished the breast meat of KNCs from broilers but showed similarities among KNCs. Also, 2D qNMR may provide fast metabolomics information compared to conventional analysis.

Collaborative Study to Validate Purity Determination by 1H Quantitative NMR Spectroscopy by Using Internal Calibration Methodology

NMR spectroscopy has recently been utilized to determine the absolute amounts of organic molecules with metrological traceability since signal intensity is directly proportional to the number of each nucleus in a molecule. The NMR methodology that uses hydrogen nucleus (¹H) to quantify chemicals is called quantitative ¹H-NMR (¹H qNMR). The quantitative method using ¹H qNMR for determining the purity or content of chemicals has been adopted into some compendial guidelines and official standards. However, there are still few reports in the literature regarding validation of ¹H qNMR methodology. Here, we coordinated an international collaborative study to validate a ¹H qNMR based on the use of an internal calibration methodology. Thirteen laboratories participated in this study, and the purities of three samples were individually measured using ¹H qNMR method. The three samples were all certified via conventional primary methods of measurement, such as butyl p-hydroxybenzoate Japanese Pharmacopeia (JP) reference standard certified by mass balance; benzoic acid certified reference material (CRM) certified by coulometric titration; fludioxonil CRM certified by a combination of freezing point depression method and ¹H qNMR. For each sample, ¹H qNMR experiments were optimized before quantitative analysis. The results showed that the measured values of each sample were equivalent to the corresponding reference labeled value. Furthermore, assessment of these ¹H qNMR data using the normalized error, E_n-value, concluded that statistically ¹H qNMR has the competence to obtain the same quantification performance and accuracy as the conventional primary methods of measurement.

A community-built calibration system: The case study of quantification of metabolites in grape juice by qNMR spectroscopy

Nuclear Magnetic Resonance (NMR) is an analytical technique extensively used in almost every chemical laboratory for structural identification. This technique provides statistically equivalent signals in spite of using spectrometer with different hardware features and is successfully used for the traceability and quantification of analytes in food samples. Nevertheless, to date only a few internationally agreed guidelines have been reported on the use of NMR for quantitative analysis. The main goal of the present study is to provide a methodological pipeline to assess the reproducibility of NMR data produced for a given matrix by spectrometers from different manufacturers, with different magnetic field strengths, age and hardware configurations. The results have been analyzed through a sequence of chemometric tests to generate a community-built calibration system which was used to verify the performance of the spectrometers and the reproducibility of the predicted sample concentrations.

Purity determination of a new antifungal drug candidate using quantitative 1 H NMR spectroscopy: Method validation and comparison of calibration approaches

Quantitative nuclear magnetic resonance (qNMR) is an analytical technique that offers numerous advantages in pharmaceutical applications including minimum sample preparation and rapid data collection times with no need for response factor corrections, being a powerful tool for assaying drug content in both drug discovery and early drug development. In the present work, we have applied qNMR, using both the internal standard and the electronic reference to access in vivo concentrations 2 calibration methods, to assess the purity of RI76, a novel antifungal drug candidate. NMR acquisition and processing parameters were optimized in order to obtain spectra with intense, well-resolved signals of completely relaxed nuclei. The analytical method was validated following current guidelines, demonstrating selectivity, linearity, accuracy, precision, and robustness. The calibration approaches were statistically compared, and no significant difference was observed when comparing the obtained results and their dispersion in terms of relative standard deviation. The proposed qNMR method may, therefore, be used for both qualitative and quantitative assessments of RI76 in early drug development and for characterization of this compound.

Quantitative assessment of the anticoagulant in plasma units collected by plasmapheresis

BACKGROUND

To date, the quantification of the anticoagulant (ACD-A) in plasma units has been based on theoretical calculations. An accurate quantification could help minimize the risks associated with plasmapheresis, given that the total ACD-A used during the procedure is distributed between the donor and the plasma unit. Our aim was to experimentally quantify the volume of ACD-A in units collected by plasmapheresis.

STUDY DESIGN AND METHODS

We used proton nuclear magnetic resonance spectroscopy to measure the ACD-A volume in 295 plasma units collected by the Azienda USL-IRCCS of Reggio Emilia, Italy. We analyzed the determinants of the differences between estimated and measured ACD-A through multivariate regression models.

RESULTS

The experimentally measured ACD-A in plasma units was variable, with 45% of the samples showing a discrepancy of more than 15 mL compared to the manufacturer's estimate. ACD-A was underestimated for higher density of the units (p < 0.0005); a weak association was also observed with triglycerides (underestimated for higher levels, p = 0.015) and sex (overestimated in females, p = 0.008), but our model explained only 35% of the individual variability.

CONCLUSION

The manufacturer's algorithms do not accurately estimate the ACD-A in units collected by plasmapheresis. Donor-related characteristics may affect ACD-A distribution between donor and plasma unit, thereby explaining the discrepancies between estimate and measurement. Errors in the estimate of the ACD-A actually received by donors could hamper studies on dose-response relationship between anticoagulant and adverse reactions. Our work should stimulate research on tailored procedures aimed at minimizing the anticoagulant received by donors and increasing plasmapheresis safety.

Optimization of 1D 1H Quantitative NMR (Nuclear Magnetic Resonance) Conditions for Polar Metabolites in Meat

The objective of this study was to establish an optimized 1D ¹H quantitative nuclear magnetic resonance (qNMR) analytical method for analyzing polar metabolites in meat. Three extraction solutions [0.6 M perchloric acid, 10 mM phosphate buffer, water/methanol (1:1)], three reconstitution buffers [20 mM 3-morpholinopropane-1-sulfonic acid, 2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid, phosphate buffer], and two pulse programs (zg30, noesypr1d) were evaluated. Extraction with 0.6 M perchloric acid and 20 mM phosphate resulted in a stable baseline and no additional overlap for quantifying polar metabolites in chicken breast. In qNMR analysis, zg30 pulse program (without water-suppression) showed smaller relative standard deviation (RSD) and faster running time than noesypr1d (water-suppression). High-performance liquid chromatography was compared with qNMR analyses to validate accuracy. The zg30 pulse program showed good accuracy and lower RSD. The optimized qNMR method was able to apply for beef and pork samples. Thus, an optimized 1D ¹H qNMR method for meat metabolomics was established.

NMR-based metabolomics identifies patients at high risk of death within two years after acute myocardial infarction in the AMI-Florence II cohort

BACKGROUND

Risk stratification and management of acute myocardial infarction patients continue to be challenging despite considerable efforts made in the last decades by many clinicians and researchers. The aim of this study was to investigate the metabolomic fingerprint of acute myocardial infarction using nuclear magnetic resonance spectroscopy on patient serum samples and to evaluate the possible role of metabolomics in the prognostic stratification of acute myocardial infarction patients.

METHODS

In total, 978 acute myocardial infarction patients were enrolled in this study; of these, 146 died and 832 survived during 2 years of follow-up after the acute myocardial infarction. Serum samples were analyzed via high-resolution ¹H-nuclear magnetic resonance spectroscopy and the spectra were used to characterize the metabolic fingerprint of patients. Multivariate statistics were used to create a prognostic model for the prediction of death within 2 years after the cardiovascular event.

RESULTS

In the training set, metabolomics showed significant differential clustering of the two outcomes cohorts. A prognostic risk model predicted death with 76.9% sensitivity, 79.5% specificity, and 78.2% accuracy, and an area under the receiver operating characteristics curve of 0.859. These results were reproduced in the validation set, obtaining 72.6% sensitivity, 72.6% specificity, and 72.6% accuracy. Cox models were used to compare the known prognostic factors (for example, Global Registry of Acute Coronary Events score, age, sex, Killip class) with the metabolomic random forest risk score. In the univariate analysis, many prognostic factors were statistically associated with the outcomes; among them, the random forest score calculated from the nuclear magnetic resonance data showed a statistically relevant hazard ratio of 6.45 (p = 2.16×10^-16). Moreover, in the multivariate regression only age, dyslipidemia, previous cerebrovascular disease, Killip class, and random forest score remained statistically significant, demonstrating their independence from the other variables.

CONCLUSIONS

For the first time, metabolomic profiling technologies were used to discriminate between patients with different outcomes after an acute myocardial infarction. These technologies seem to be a valid and accurate addition to standard stratification based on clinical and biohumoral parameters.

Classification of samples from NMR-based metabolomics using principal components analysis and partial least squares with uncertainty estimation

Recent progress in metabolomics has been aided by the development of analysis techniques such as gas and liquid chromatography coupled with mass spectrometry (GC-MS and LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. The vast quantities of data produced by these techniques has resulted in an increase in the use of machine algorithms that can aid in the interpretation of this data, such as principal components analysis (PCA) and partial least squares (PLS). Techniques such as these can be applied to biomarker discovery, interlaboratory comparison, and clinical diagnoses. However, there is a lingering question whether the results of these studies can be applied to broader sets of clinical data, usually taken from different data sources. In this work, we address this question by creating a metabolomics workflow that combines a previously published consensus analysis procedure ( https://doi.org/10.1016/j.chemolab.2016.12.010 ) with PCA and PLS models using uncertainty analysis based on bootstrapping. This workflow is applied to NMR data that come from an interlaboratory comparison study using synthetic and biologically obtained metabolite mixtures. The consensus analysis identifies trusted laboratories, whose data are used to create classification models that are more reliable than without. With uncertainty analysis, the reliability of the classification can be rigorously quantified, both for data from the original set and from new data that the model is analyzing. Graphical abstract ᅟ.

Quantitative ¹H-NMR Spectroscopy for Profiling Primary Metabolites in Mulberry Leaves

The primary metabolites in aqueous extract of mulberry (Morus alba L.) leaves were characterized by using proton nuclear magnetic resonance (¹H-NMR) spectroscopy. With the convenience of resonance assignment, GABA together with the other 10 primary metabolites was simultaneously identified and quantified in one ¹H-NMR spectrum. In this study, external calibration curves for metabolites were employed to calculate the concentrations of interests. The proposed quantitative approach was demonstrated with good linearity (r² ranged in the interval of 0.9965–0.9999), precision, repeatability, stability (RSD values in the ranges of 0.35–4.89%, 0.77–7.13% and 0.28–2.33%, respectively) and accuracy (recovery rates from 89.2% to 118.5%). The established ¹H-NMR method was then successfully applied to quantify 11 primary metabolites in mulberry leaves from different geographical regions within a rapid analysis time and a simple sample preparation procedure.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

Nuclear Magnetic Resonance of Gangliosides

Structure, conformation, and dynamics of sphingolipids can provide substantial help in better understanding sphingolipid-ligand interaction mechanisms. Both the oligosaccharide structure and the ceramide moiety of native glycosphingolipid can be established directly by NMR spectroscopic analysis without the necessity to resort to any other chemical or spectroscopic methods. NMR is a powerful technique to investigate interaction between small ligand, such as ganglioside, and membrane protein.

Composition and Statistical Analysis of Biophenols in Apulian Italian EVOOs

Extra-virgin olive oil (EVOO) is among the basic constituents of the Mediterranean diet. Its nutraceutical properties are due mainly, but not only, to a plethora of molecules with antioxidant activity known as biophenols. In this article, several biophenols were measured in EVOOs from South Apulia, Italy. Hydroxytyrosol, tyrosol and their conjugated structures to elenolic acid in different forms were identified and quantified by high performance liquid chromatography (HPLC) together with lignans, luteolin and α-tocopherol. The concentration of the analyzed metabolites was quite high in all the cultivars studied, but it was still possible to discriminate them through multivariate statistical analysis (MVA). Furthermore, principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were also exploited for determining variances among samples depending on the interval time between harvesting and milling, on the age of the olive trees, and on the area where the olive trees were grown.

Multi-approach metabolomics analysis and artificial simplified phytocomplexes reveal cultivar-dependent synergy between polyphenols and ascorbic acid in fruits of the sweet cherry (Prunus avium L.)

Fruits of the sweet cherry (Prunus avium L.) accumulate a range of antioxidants that can help to prevent cardiovascular disease, inflammation and cancer. We tested the in vitro antioxidant activity of 18 sweet cherry cultivars collected from 12 farms in the protected geographical indication region of Marostica (Vicenza, Italy) during two growing seasons. Multiple targeted and untargeted metabolomics approaches (NMR, LC-MS, HPLC-DAD, HPLC-UV) as well as artificial simplified phytocomplexes representing the cultivars Sandra Tardiva, Sandra and Grace Star were then used to determine whether the total antioxidant activity reflected the additive effects of each compound or resulted from synergistic interactions. We found that the composition of each cultivar depended more on genetic variability than environmental factors. Furthermore, phenolic compounds were the principal source of antioxidant activity and experiments with artificial simplified phytocomplexes indicated strong synergy between the anthocyanins and quercetins/ascorbic acid specifically in the cultivar Sandra Tardiva. Our data therefore indicate that the total antioxidant activity of sweet cherry fruits may originate from cultivar-dependent interactions among different classes of metabolite.

Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling

Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA–MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids.

A scoring metric for multivariate data for reproducibility analysis using chemometric methods

Process quality control and reproducibility in emerging measurement fields such as metabolomics is normally assured by interlaboratory comparison testing. As a part of this testing process, spectral features from a spectroscopic method such as nuclear magnetic resonance (NMR) spectroscopy are attributed to particular analytes within a mixture, and it is the metabolite concentrations that are returned for comparison between laboratories. However, data quality may also be assessed directly by using binned spectral data before the time-consuming identification and quantification. Use of the binned spectra has some advantages, including preserving information about trace constituents and enabling identification of process difficulties. In this paper, we demonstrate the use of binned NMR spectra to conduct a detailed interlaboratory comparison and composition analysis. Spectra of synthetic and biologically-obtained metabolite mixtures, taken from a previous interlaboratory study, are compared with cluster analysis using a variety of distance and entropy metrics. The individual measurements are then evaluated based on where they fall within their clusters, and a laboratory-level scoring metric is developed, which provides an assessment of each laboratory's individual performance.

1H NMR To Evaluate the Metabolome of Bronchoalveolar Lavage Fluid (BALf) in Bronchiolitis Obliterans Syndrome (BOS): Toward the Development of a New Approach for Biomarker Identification

This report describes the application of NMR spectroscopy to the profiling of metabolites in bronchoalveolar lavage fluid (BALf) of lung transplant recipients without bronchiolitis obliterans syndrome (BOS) (stable, S, n = 10), and with BOS at different degrees of severity (BOS 0p, n = 10; BOS I, n = 10). Through the fine-tuning of a number of parameters concerning both sample preparation/processing and variations of spectra acquisition modes, an efficient and reproducible protocol was designed for the screening of metabolites in a pulmonary fluid that should reflect the status of airway inflammation/injury. Exploiting the combination of mono- and bidimensional NMR experiments, 38 polar metabolites, including amino acids, Krebs cycle intermediates, mono- and disaccharides, nucleotides, and phospholipid precursors, were unequivocally identified. To determine which signature could be correlated with the onset of BOS, the metabolites' content of the above recipients was analyzed by multivariate (PCA and OPLS-DA) statistical methods. PCA analysis (almost) totally differentiated S from BOS I, and this discrimination was significantly improved by the application of OPLS-DA, whose model was characterized by excellent fit and prediction values (R² = 0.99 and Q² = 0.88). The analysis of S vs BOS 0p and of BOS 0p vs BOS I samples showed a clear discrimination of considered cohorts, although with a poorer efficiency compared to those measured for S vs BOS I patients. The data shown in this work assess the suitability of the NMR approach in monitoring different pathological lung conditions.

NMR window of molecular complexity showing homeostasis in superorganisms

NMR offers tremendous advantages in the analyses of molecular complexity. The “big-data” are produced during the acquisition of fingerprints that must be stored and shared for posterior analysis and verifications.

A Comparative Study of the Effect of Leukoreduction and Pre-storage Leukodepletion on Red Blood Cells during Storage

Blood transfusion is a fundamental therapy in numerous pathological conditions. Regrettably, many clinical reports describe adverse transfusion's drawbacks due to red blood cells alterations during storage. Thus, the possibility for a blood bank to ameliorate the quality of the erythrocyte concentrates units is crucial to improve clinical results and reduce transfusion adverse occurrences. Leukodepletion is a pre-storage treatment recognized to better preserve the quality of red blood cells with respect to leukoreduction. Aim of this work is to unravel the biochemical and biophysical basis that sustain the good clinical outcomes associated to the use of leukodepleted erythrocytes units. Erythrocytes concentrates were prepared as leukoreduced (n = 8) and pre-storage leukodepleted (n = 8) and then studied during 6 weeks in blood bank conditions. Overall, the data indicate that leukodepletion not only provide red blood cells with an appropriate amount of nutrients for a longer time but also selects red blood cells characterized by a more resilient plasma membrane fit to prolong their viability. We believe these results will stimulate new ideas to further optimize the current storage protocols.

A simple and accurate protocol for absolute polar metabolite quantification in cell cultures using quantitative nuclear magnetic resonance

Absolute analyte quantification by nuclear magnetic resonance (NMR) spectroscopy is rarely pursued in metabolomics, even though this would allow researchers to compare results obtained using different techniques. Here we report on a new protocol that permits, after pH-controlled serum protein removal, the sensitive quantification (limit of detection [LOD] = 5-25 μM) of hydrophilic nutrients and metabolites in the extracellular medium of cells in cultures. The method does not require the use of databases and uses PULCON (pulse length-based concentration determination) quantitative NMR to obtain results that are significantly more accurate and reproducible than those obtained by CPMG (Carr-Purcell-Meiboom-Gill) sequence or post-processing filtering approaches. Three practical applications of the method highlight its flexibility under different cell culture conditions. We identified and quantified (i) metabolic differences between genetically engineered human cell lines, (ii) alterations in cellular metabolism induced by differentiation of mouse myoblasts into myotubes, and (iii) metabolic changes caused by activation of neurotransmitter receptors in mouse myoblasts. Thus, the new protocol offers an easily implementable, efficient, and versatile tool for the investigation of cellular metabolism and signal transduction.

Nuclear Magnetic Resonance-Based Metabolic Comparative Analysis of Two Apple Varieties with Different Resistances to Apple Scab Attacks

Apple scab, caused by the fungus Venturia inaequalis, is the most serious disease of the apple worldwide. Two cultivars (Malus domestica), having different degrees of resistance against fungi attacks, were analyzed by (1)H and (13)C nuclear magnetic resonance (NMR) spectroscopy. Aqueous and organic extracts of both apple flesh and skin were studied, and over 30 metabolites, classified as organic acids, amino acids, carbohydrates, phenolic compounds, lipids, sterols, and other metabolites, were quantified by means of one-dimensional (1D) and two-dimensional (2D) NMR experiments. The metabolic profiles of the two apple cultivars were compared, and the differences were correlated with the different degrees of resistance to apple scab by means of univariate analysis. Levels of metabolites with known antifungal activity were observed not only to be higher in the Almagold cultivar but also to show different correlation patterns in comparison to Golden Delicious, implying a difference in the metabolic network involved in their biosynthesis.