Effects of Paired Associative Stimulation on Metabolites in Ischemia Stroke Rats Model as Studied by Nuclear Magnetic Resonance Spectrum.

Paired associated stimulation (PAS) has been confirmed to play a role in motor recovery after stroke, but the underlying mechanism has not been fully elucidated. In this study, we employed a comprehensive battery of measurements, including behavioral test, electrophysiology and 1H-NMR approaches, to investigate the therapeutic effects of PAS in rat model of cerebral ischemia and its underlying mechanism. Rats were randomly divided into a transient middle cerebral artery occlusion group (tMCAO group), a tMCAO + PAS group (PAS group), and a sham group. PAS was applied over 7 consecutive days in PAS group. The behavioral function of rats was evaluated by modified Garcia Scores and Rota-rod test. Electrophysiological changes were measured by motor evoked potentials (MEP). Metabolic changes of ischemic penumbra were detected by 1H-NMR. After PAS intervention, the performances on Rota-rod test and Garcia test improved and the amplitude of MEP increased significantly. The gamma-aminobutyric acid (GABA) in penumbra cortex was decreased significantly, whereas the glutamate showed the opposite changes. The results suggested that post-stroke recovery promoted by PAS may be related to the metabolites alteration in ischemic penumbra and also regulate the excitability of motor cortex.

Diffusion and Relaxation Edited Proton NMR Spectroscopy of Plasma Reveals a High-Fidelity Supramolecular Biomarker Signature of SARS-CoV-2 Infection.

We have applied nuclear magnetic resonance spectroscopy based plasma phenotyping to reveal diagnostic molecular signatures of SARS-CoV-2 infection via combined diffusional and relaxation editing (DIRE). We compared plasma from healthy age-matched controls (n = 26) with SARS-CoV-2 negative non-hospitalized respiratory patients and hospitalized respiratory patients (n = 23 and 11 respectively) with SARS-CoV-2 rRT-PCR positive respiratory patients (n = 17, with longitudinal sampling time-points). DIRE data were modelled using principal component analysis and orthogonal projections to latent structures discriminant analysis (O-PLS-DA), with statistical cross-validation indices indicating excellent model generalization for the classification of SARS-CoV-2 positivity for all comparator groups (area under the receiver operator characteristic curve = 1). DIRE spectra show biomarker signal combinations conferred by differential concentrations of metabolites with selected molecular mobility properties. These comprise the following: (a) composite N-acetyl signals from α-1-acid glycoprotein and other glycoproteins (designated GlycA and GlycB) that were elevated in SARS-CoV-2 positive patients [p = 2.52 × 10-10 (GlycA) and 1.25 × 10-9 (GlycB) vs controls], (b) two diagnostic supramolecular phospholipid composite signals that were identified (SPC-A and SPC-B) from the -+N-(CH3)3 choline headgroups of lysophosphatidylcholines carried on plasma glycoproteins and from phospholipids in high-density lipoprotein subfractions (SPC-A) together with a phospholipid component of low-density lipoprotein (SPC-B). The integrals of the summed SPC signals (SPCtotal) were reduced in SARS-CoV-2 positive patients relative to both controls (p = 1.40 × 10-7) and SARS-CoV-2 negative patients (p = 4.52 × 10-8) but were not significantly different between controls and SARS-CoV-2 negative patients. The identity of the SPC signal components was determined using one and two dimensional diffusional, relaxation, and statistical spectroscopic experiments. The SPCtotal/GlycA ratios were also significantly different for control versus SARS-CoV-2 positive patients (p = 1.23 × 10-10) and for SARS-CoV-2 negatives versus positives (p = 1.60 × 10-9). Thus, plasma SPCtotal and SPCtotal/GlycA are proposed as sensitive molecular markers for SARS-CoV-2 positivity that could effectively augment current COVID-19 diagnostics and may have value in functional assessment of the disease recovery process in patients with long-term symptoms.

Broad Learning Enhanced 1H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus.

In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (1H-MRS) to diagnose neuropsychiatric systemic lupus erythematosus (NPSLE) with the assistance of a support vector machine broad learning system (BL-SVM). We retrospectively analysed 23 confirmed patients and 16 healthy controls, who underwent a 3.0 T magnetic resonance imaging (MRI) sequence with multivoxel 1H-MRS in our hospitals. One hundred and seventeen metabolic features were extracted from the multivoxel 1H-MRS image. Thirty-three metabolic features selected by the Mann-Whitney U test were considered to have a statistically significant difference (p < 0.05). However, the best accuracy achieved by conventional statistical methods using these 33 metabolic features was only 77%. We turned to develop a support vector machine broad learning system (BL-SVM) to quantitatively analyse the metabolic features from 1H-MRS. Although not all the individual features manifested statistics significantly, the BL-SVM could still learn to distinguish the NPSLE from the healthy controls. The area under the receiver operating characteristic curve (AUC), the sensitivity, and the specificity of our BL-SVM in predicting NPSLE were 95%, 95.8%, and 93%, respectively, by 3-fold cross-validation. We consequently conclude that the proposed system effectively and efficiently working on limited and noisy samples may brighten a noinvasive in vivo instrument for early diagnosis of NPSLE.

Differentiating, evaluating, and classifying three quinoa ecotypes by washing, cooking and germination treatments, using 1H NMR-based metabolomic approach.

We processed three quinoa ecotypes as they are commonly consumed in a daily diet. For the treatments, quinoa seeds were washed, cooked, and/or germinated. Following treated, we used 1H NMR-based metabolomic profiling to explore differences between the ecotypes. Then, for a non-targeted and targeted food fingerprint analysis of samples, we performed multivariable data analyses, including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and hierarchical cluster analysis. From our study, we were able to discriminate each quinoa ecotype regardless of treatment based on its metabolomic profiling. Additionally, we were able to identify 30 metabolites that were useful to determine the effect of each treatment on nutritional composition. Germination increased the content of most metabolites irrespective of ecotype. In general, ecotype CQE_03 was different from ecotypes CQE_01 and CQE_02. Our phytochemical analysis revealed the effects of washing, cooking, and/or germination, particularly on saponins content.

Quality evaluation of table grapes during storage by using 1H NMR, LC-HRMS, MS-eNose and multivariate statistical analysis.

Three non-targeted methods, i.e. 1H NMR, LC-HRMS, and HS-SPME/MS-eNose, combined with chemometrics, were used to classify two table grape cultivars (Italia and Victoria) based on five quality levels (5, 4, 3, 2, 1). Grapes at marketable quality levels (5, 4, 3) were also discriminated from non-marketable quality levels (2 and 1). PCA-LDA and PLS-DA were applied, and results showed that, the MS-eNose provided the best results. Specifically, with the Italia table grapes, mean prediction abilities ranging from 87% to 88% and from 98% to 99% were obtained for discrimination amongst the five quality levels and of marketability/non-marketability, respectively. For the cultivar Victoria, mean predictive abilities higher than 99% were achieved for both classifications. Good models were also obtained for both cultivars using NMR and HRMS data, but only for classification by marketability. Satisfying models were further validated by MCCV. Finally, the compounds that contributed the most to the discriminations were identified.

Pipeline for High-Throughput Modeling of Marijuana and Hemp Extracts.

Authentication of Cannabis products is important for assuring the quality of manufacturing, with the increasing consumption and regulation. In this report, a two-stage pipeline was developed for high-throughput screening and chemotyping the spectra from two sets of botanical extracts from the Cannabis genus. The first set contains different marijuana samples with higher concentrations of tetrahydrocannabinol (THC). The other set includes samples from hemp, a variety of Cannabis sativa with the THC concentration below 0.3%. The first stage applies the technique of class modeling to determine whether spectra belong to marijuana or hemp and reject novel spectra that may be neither marijuana nor hemp. An automatic soft independent modeling of class analogy (aSIMCA) that self-optimizes the number of principal components and the decision threshold is utilized in the first pipeline process to achieve excellent efficiency and efficacy. Once these spectra are recognized by aSIMCA as marijuana or hemp, they are then routed to the appropriate classifiers in the second stage for chemotyping the spectra, i.e., identifying these spectra into different chemotypes so that the pharmacological properties and cultivars of the spectra can be recognized. Three multivariate classifiers, a fuzzy rule building expert system (FuRES), super partial least-squares-discriminant analysis (sPLS-DA), and support vector machine tree type entropy (SVMtreeH), are employed for chemotyping. The discriminant ability of the pipeline was evaluated with different spectral data sets of these two groups of botanical samples, including proton nuclear magnetic resonance, mass, and ultraviolet spectra. All evaluations gave good results with accuracies greater than 95%, which demonstrated promising application of the pipeline for automated high-throughput screening and chemotyping marijuana and hemp, as well as other botanical products.

Prediction of fatty acid composition in camellia oil by 1H NMR combined with PLS regression.

A rapid method for the determination of fatty acid (FA) composition in camellia oils was developed based on the 1H NMR technique combined with partial least squares (PLS) method. Outliers detection, LVs optimization and data pre-processing selection were explored during the model building process. The results showed the optimal models for predicting the content of C18:1, C18:2, C18:3, saturated, unsaturated, monounsaturated and polyunsaturated FA were achieved by Pareto scaling (Par) pretreatment, with correlation coefficient (R2) above 0.99, the root mean square error of estimation and prediction (RMSEE, RMSEP) lower than 0.954 and 0.947, respectively. Mean-centering (Ctr) was more suitable for the model of C16:0 and C18:0 with the best performance indicators (R2 ≥ 0.945, RMSEE ≤ 0.377, RMSEP ≤ 0.212). This study indicated that 1H NMR has the potential to be applied as a rapid and routine method for the analysis of FA composition in camellia oils.

A robust intracellular metabolite extraction protocol for human neutrophil metabolic profiling.

Neutrophils are phagocytic innate immune cells that play essential roles in host defence, but are also implicated in inflammatory diseases such as rheumatoid arthritis (RA) where they contribute to systemic inflammation and joint damage. Transcriptomic analysis of neutrophils has revealed significant changes in gene expression in neutrophils activated in vitro by cytokines and in vivo during inflammation in RA. However, there are no reports on the global metabolomic changes that occur as a consequence of this activation. The aim of this study was to establish protocols for the study of changes in the metabolome of human neutrophils using 1H NMR spectroscopy. Sample preparation and spectral analysis protocols were optimised using neutrophils isolated by Ficoll-Paque, with decreased washing steps and inclusion of a heat-shock step to quench metabolite turnover. Cells were incubated ± PMA for 15 min in HEPES-free media and samples were analysed by NMR using a 700 MHz NMR Avance IIIHD Bruker NMR spectrometer equipped with a TCI cryoprobe. Chenomx, Bruker TopSpin and AMIX software were used to process spectra and identify metabolites. Principal Component Analysis (PCA) and signalling pathway analysis was carried out using Metaboanalyst. Cell number and number of scans (NS) were optimised as >3.6 million cells and 512 NS. 327 spectral bins were defined in the neutrophil spectra, of which 287 (87.7%) were assigned to 110 metabolites that included: amino acids, peptides and analogues; carbohydrates, carbonyls and alcohols; nucleotides, nucleosides and analogues; lipids and lipid-like molecules; benzenoids; and other organic compounds. 43 metabolites changed at least 1.5 fold (increase or decrease) after the addition of PMA for 5 or 15 min. Pathway analysis revealed that PMA affected nicotinate and nicotinamide metabolism, aminoacyl-tRNA biosynthesis and glycolysis, suggesting a redirection of glucose metabolism from glycolysis to the pentose phosphate pathway and production of NADPH for activation of the NADPH oxidase and subsequent respiratory burst. We have developed protocols for the study of human neutrophils by 1H NMR spectroscopy. Importantly, this methodology has sufficient sensitivity and reproducibility to detect changes in metabolite abundance from cell numbers typically collected from clinical samples or experiments with multiple assay conditions.

Striatal neurometabolite levels in patients with schizophrenia undergoing long-term antipsychotic treatment: A proton magnetic resonance spectroscopy and reliability study.

Previous proton magnetic resonance spectroscopy (1H-MRS) studies have reported disrupted levels of various neurometabolites in patients with schizophrenia. An area of particular interest within this patient population is the striatum, which is highly implicated in the pathophysiology of schizophrenia. The present study examined neurometabolite levels in the striatum of 12 patients with schizophrenia receiving antipsychotic treatment for at least 1 year and 11 healthy controls using 3-Tesla 1H-MRS (PRESS, TE = 35 ms). Glutamate, glutamate+glutamine (Glx), myo-inositol, choline, N-acetylaspartate, and creatine levels were estimated using LCModel, and corrected for fraction of cerebrospinal fluid in the 1H-MRS voxel. Striatal neurometabolite levels were compared between groups. Multiple study visits permitted a reliability assessment for neurometabolite levels (days between paired 1H-MRS acquisitions: average = 90.33; range = 7-306). Striatal neurometabolite levels did not differ between groups. Within the whole sample, intraclass correlation coefficients for glutamate, Glx, myo-inositol, choline, and N-acetylaspartate were fair to excellent (0.576-0.847). The similarity in striatal neurometabolite levels between groups implies a marked difference from the antipsychotic-naïve first-episode state, especially in terms of glutamatergic neurometabolites, and might provide insight regarding illness progression and the influence of antipsychotic medication.

AQuA: An Automated Quantification Algorithm for High-Throughput NMR-Based Metabolomics and Its Application in Human Plasma.

A key limiting step for high-throughput NMR-based metabolomics is the lack of rapid and accurate tools for absolute quantification of many metabolites. We developed, implemented, and evaluated an algorithm, AQuA (Automated Quantification Algorithm), for targeted metabolite quantification from complex 1H NMR spectra. AQuA operates based on spectral data extracted from a library consisting of one standard calibration spectrum for each metabolite. It uses one preselected NMR signal per metabolite for determining absolute concentrations and does so by effectively accounting for interferences caused by other metabolites. AQuA was implemented and evaluated using experimental NMR spectra from human plasma. The accuracy of AQuA was tested and confirmed in comparison with a manual spectral fitting approach using the ChenomX software, in which 61 out of 67 metabolites quantified in 30 human plasma spectra showed a goodness-of-fit (r2) close to or exceeding 0.9 between the two approaches. In addition, three quality indicators generated by AQuA, namely, occurrence, interference, and positional deviation, were studied. These quality indicators permit evaluation of the results each time the algorithm is operated. The efficiency was tested and confirmed by implementing AQuA for quantification of 67 metabolites in a large data set comprising 1342 experimental spectra from human plasma, in which the whole computation took less than 1 s.

Temperature dependence of 1H NMR chemical shifts and its influence on estimated metabolite concentrations.

OBJECTIVES: Temperature dependent chemical shifts of important brain metabolites measured by localised 1H MRS were investigated to test how the use of incorrect prior knowledge on chemical shifts impairs the quantification of metabolite concentrations. MATERIALS AND METHODS: Phantom measurements on solutions containing 11 metabolites were performed on a 7 T scanner between 1 and 43 °C. The temperature dependence of the chemical shift differences was fitted by a linear model. Spectra were simulated for different temperatures and analysed by the AQSES program (jMRUI 5.2) using model functions with chemical shift values for 37 °C. RESULTS: Large differences in the temperature dependence of the chemical shift differences were determined with a maximum slope of about ±7.5 × 10-4 ppm/K. For 32-40 °C, only minor quantification errors resulted from using incorrect chemical shifts, with the exception of Cr and PCr. For 1-10 °C considerable quantification errors occurred if the temperature dependence of the chemical shifts was neglected. CONCLUSION: If 1H MRS measurements are not performed at 37 °C, for which the published chemical shift values have been determined, the temperature dependence of chemical shifts should be considered to avoid systematic quantification errors, particularly for measurements on animal models at lower temperatures.

Workflow for Integrated Processing of Multicohort Untargeted 1H NMR Metabolomics Data in Large-Scale Metabolic Epidemiology.

Large-scale metabolomics studies involving thousands of samples present multiple challenges in data analysis, particularly when an untargeted platform is used. Studies with multiple cohorts and analysis platforms exacerbate existing problems such as peak alignment and normalization. Therefore, there is a need for robust processing pipelines that can ensure reliable data for statistical analysis. The COMBI-BIO project incorporates serum from ∼8000 individuals, in three cohorts, profiled by six assays in two phases using both 1H NMR and UPLC-MS. Here we present the COMBI-BIO NMR analysis pipeline and demonstrate its fitness for purpose using representative quality control (QC) samples. NMR spectra were first aligned and normalized. After eliminating interfering signals, outliers identified using Hotelling's T2 were removed and a cohort/phase adjustment was applied, resulting in two NMR data sets (CPMG and NOESY). Alignment of the NMR data was shown to increase the correlation-based alignment quality measure from 0.319 to 0.391 for CPMG and from 0.536 to 0.586 for NOESY, showing that the improvement was present across both large and small peaks. End-to-end quality assessment of the pipeline was achieved using Hotelling's T2 distributions. For CPMG spectra, the interquartile range decreased from 1.425 in raw QC data to 0.679 in processed spectra, while the corresponding change for NOESY spectra was from 0.795 to 0.636, indicating an improvement in precision following processing. PCA indicated that gross phase and cohort differences were no longer present. These results illustrate that the pipeline produces robust and reproducible data, successfully addressing the methodological challenges of this large multifaceted study.