1H NMR analysis of metabolites from leaf tissue of resistant and susceptible oil palm breeding materials against Ganoderma boninense.

INTRODUCTION: Breeding for oil palm resistance against basal stem rot caused by Ganoderma boninense is challenging and time-consuming. Advanced oil palm gene pools are very limited, hence it is assumed that parental palms have experienced genetic drift and lost their resistance genes against Ganoderma. High-throughput selection criteria should be developed. Metabolomic analysis using 1H nuclear magnetic resonance (NMR) spectroscopy is easy, and the resulting metabolite can be used as a diagnostic tool for detecting disease in various host-pathogen combinations. OBJECTIVES: The objective of this study was to identify metabolite variations in Dura (D) and Pisifera (P) parental palms with different resistance levels against Ganoderma and moderately resistant DxP using 1H NMR analysis. METHODS: Leaf tissues of seven different oil palm categories consisting of: resistant, moderate, and susceptible Dura (D); moderate and susceptible Pisifera (P); resistant Tenera/Pisifera (T/P) parental palms; and moderately resistant DxP variety progenies, were sampled and their metabolites were determined using NMR spectroscopy. RESULTS: Twenty-nine types of metabolites were identified, and most of the metabolites fall in the monosaccharides, amino acids, and fatty acids compound classes. The PCA, PLS-DA, and heatmap multivariate analysis indicated two identified groups of resistance based on their metabolites. The first group consisted of resistant T/P, moderate P, resistant D, and moderately resistant DxP. In contrast, the second group consisted of susceptible P, moderate D, and susceptible D. Glycerol and ascorbic acid were detected as biomarker candidates by OPLS-DA to differentiate moderately resistant DxP from susceptible D and P. The pathway analysis suggested that glycine, serine, and threonine metabolism and taurine and hypotaurine metabolism were involved in the oil palm defense mechanism against Ganoderma. CONCLUSION: A metabolomic study with 1H NMR was able to describe the metabolite composition that could differentiate the characteristics of oil palm resistance against basal stem rot (BSR) caused by G. boninense. These metabolites revealed in this study have enormous potential to become support tools for breeding new oil palm varieties with higher resistance against BSR.

Exercise-induced changes in intramuscular total creatine concentration measured with 1H magnetic resonance spectroscopy: A pilot study.

Total amount of creatine (Cr) and phosphocreatine, or total creatine (tCr), may have a significant impact on the performance of skeletal muscles. In sports such as bodybuilding, it is popular to take Cr supplements to maintain tCr level. However, no study has explored the quantitative relationship between exercise intensity and the induced change in muscle's tCr. In this well-controlled study, straight-leg plantar flexion with specific load and duration was performed by 10 healthy subjects inside an MRI scanner, immediately followed by 1H MR spectroscopy (MRS) for measuring tCr concentration in gastrocnemius. For repeatability assessment, the experiment was repeated for each subject on two different days. Across all the subjects, baseline tCr was 46.6 ± 2.4 mM, ranging from 40.6 to 50.1 mM; with exercise, tCr significantly decreased by 10.9% ± 1.0% with 6-lb load and 21.0% ± 1.3% with 12-lb load (p < 0.0001). Between two different days, baseline tCr, percentage decrease induced by exercise with a 6-lb and 12-lb load differed by 2.2% ± 2.3%, 11.7% ± 6.0% and 4.9% ± 3.2%, respectively. In conclusion, the proposed protocol of controlled exercise stimulation and MRS acquisition can reproducibly monitor tCr level and its exercise-induced change in skeletal muscles. The measured tCr level is sensitive to exercise intensity, so can be used to quantitatively assess muscle performance or fatigue.

Frontal and occipital brain glutathione levels are unchanged in autistic adults.

BACKGROUND: The neurobiological underpinnings of Autism Spectrum Disorder (ASD) are diverse and likely multifactorial. One possible mechanism is increased oxidative stress leading to altered neurodevelopment and brain function. However, this hypothesis has mostly been tested in post-mortem studies. So far, available in vivo studies in autistic individuals have reported no differences in glutathione (GSH) levels in frontal, occipital, and subcortical regions. However, these studies were limited by the technically challenging quantification of GSH, the main brain antioxidant molecule. This study aimed to overcome previous studies' limitations by using a GSH-tailored spectroscopy sequence and optimised quantification methodology to provide clarity on GSH levels in autistic adults. METHODS: We used spectral editing proton-magnetic resonance spectroscopy (1H-MRS) combined with linear combination model fitting to quantify GSH in the dorsomedial prefrontal cortex (DMPFC) and medial occipital cortex (mOCC) of autistic and non-autistic adults (male and female). We compared GSH levels between groups. We also examined correlations between GSH and current autism symptoms, measured using the Autism Quotient (AQ). RESULTS: Data were available from 31 adult autistic participants (24 males, 7 females) and 40 non-autistic participants (21 males, 16 females); the largest sample to date. The GSH levels did not differ between groups in either region. No correlations with AQ were observed. CONCLUSION: GSH levels as measured using 1H-MRS are unaltered in the DMPFC and mOCC regions of autistic adults, suggesting that oxidative stress in these cortical regions is not a marked neurobiological signature of ASD.

Addressing the Opioids Lipophilicity Challenge via a Straightforward and Simultaneous 1H NMR-Based logP/D Determination, Both Separately and in Mixtures.

A systematic study of trends in the lipophilicity of prominent representatives of the opioid family, including natural, semisynthetic, synthetic, and endogenous neuropeptide opioids, is described. This was enabled by a straightforward 1H NMR-based logP/D determination method developed for compounds holding at least one aromatic hydrogen atom. Moreover, the new method enables a direct simultaneous logD determination of opioid mixtures, overcoming the high sensitivity of this family to the measurement conditions, which is critical when a determination of the exact ΔlogD values of matched pairs is required. Interpretation of the experimental ΔlogD7.4 values of selected matched pairs, focusing inter alia on the 3-OMe and 14-OMe motifs in morphinan opioids, is suggested with the aid of DFT calculations and may be useful for the discovery of new opioid therapeutics.

Contributions of Brain Microstructures and Metabolism to Visual Field Loss Patterns in Glaucoma Using Archetypal and Information Gain Analyses.

Purpose: To investigate the contributions of the microstructural and metabolic brain environment to glaucoma and their association with visual field (VF) loss patterns by using advanced diffusion magnetic resonance imaging (dMRI), proton magnetic resonance spectroscopy (MRS), and clinical ophthalmic measures. Methods: Sixty-nine glaucoma and healthy subjects underwent dMRI and/or MRS at 3 Tesla. Ophthalmic data were collected from VF perimetry and optical coherence tomography. dMRI parameters of microstructural integrity in the optic radiation and MRS-derived neurochemical levels in the visual cortex were compared among early glaucoma, advanced glaucoma, and healthy controls. Multivariate regression was used to correlate neuroimaging metrics with 16 archetypal VF loss patterns. We also ranked neuroimaging, ophthalmic, and demographic attributes in terms of their information gain to determine their importance to glaucoma. Results: In dMRI, decreasing fractional anisotropy, radial kurtosis, and tortuosity and increasing radial diffusivity correlated with greater overall VF loss bilaterally. Regionally, decreasing intra-axonal space and extra-axonal space diffusivities correlated with greater VF loss in the superior-altitudinal area of the right eye and the inferior-altitudinal area of the left eye. In MRS, both early and advanced glaucoma patients had lower gamma-aminobutyric acid (GABA), glutamate, and choline levels than healthy controls. GABA appeared to associate more with superonasal VF loss, and glutamate and choline more with inferior VF loss. Choline ranked third for importance to early glaucoma, whereas radial kurtosis and GABA ranked fourth and fifth for advanced glaucoma. Conclusions: Our findings highlight the importance of non-invasive neuroimaging biomarkers and analytical modeling for unveiling glaucomatous neurodegeneration and how they reflect complementary VF loss patterns.

1H-NMR-based metabolomics reveals metabolic alterations in early development of a mouse model of Angelman syndrome.

BACKGROUND: Angelman syndrome (AS) is a rare neurodevelopmental genetic disorder caused by the loss of function of the ubiquitin ligase E3A (UBE3A) gene, affecting approximately 1:15,000 live births. We have recently shown that mitochondrial function in AS is altered during mid to late embryonic brain development leading to increased oxidative stress and enhanced apoptosis of neural precursor cells. However, the overall alterations of metabolic processes are still unknown. Hence, as a follow-up, we aim to investigate the metabolic profiles of wild-type (WT) and AS littermates and to identify which metabolic processes are aberrant in the brain of AS model mice during embryonic development. METHODS: We collected brain tissue samples from mice embryos at E16.5 and performed metabolomic analyses using proton nuclear magnetic resonance (1H-NMR) spectroscopy. Multivariate and Univariate analyses were performed to determine the significantly altered metabolites in AS mice. Pathways associated with the altered metabolites were identified using metabolite set enrichment analysis. RESULTS: Our analysis showed that overall, the metabolomic fingerprint of AS embryonic brains differed from those of their WT littermates. Moreover, we revealed a significant elevation of distinct metabolites, such as acetate, lactate, and succinate in the AS samples compared to the WT samples. The elevated metabolites were significantly associated with the pyruvate metabolism and glycolytic pathways. LIMITATIONS: Only 14 metabolites were successfully identified and investigated in the present study. The effect of unidentified metabolites and their unresolved peaks was not determined. Additionally, we conducted the metabolomic study on whole brain tissue samples. Employing high-resolution NMR studies on different brain regions could further expand our knowledge regarding metabolic alterations in the AS brain. Furthermore, increasing the sample size could reveal the involvement of more significantly altered metabolites in the pathophysiology of the AS brain. CONCLUSIONS: Ube3a loss of function alters bioenergy-related metabolism in the AS brain during embryonic development. Furthermore, these neurochemical changes could be linked to the mitochondrial reactive oxygen species and oxidative stress that occurs during the AS embryonic development.

Studying lipid flip-flop in asymmetric liposomes using 1H NMR and TR-SANS.

The specific spatial and temporal distribution of lipids in membranes play a crucial role in determining the biochemical and biophysical properties of the system. In nature, the asymmetric distribution of lipids is a dynamic process with ATP-dependent lipid transporters maintaining asymmetry, and passive transbilayer diffusion, that is, flip-flop, counteracting it. In this chapter, two probe-free techniques, 1H NMR and time-resolved small angle neutron scattering, are described in detail as methods of investigating lipid flip-flop rates in synthetic liposomes that have been generated with an asymmetric bilayer composition.

Study on the Relationship Between MRI Functional Imaging and Multiple Immunohistochemical Features of Glioma: A Noninvasive and More Precise Glioma Management.

Objective: To investigate the performance of diffusion-tensor imaging (DTI) and hydrogen proton magnetic resonance spectroscopy (1H-MRS) parameters in predicting the immunohistochemistry (IHC) biomarkers of glioma. Methods: Patients with glioma confirmed by pathology from March 2015 to September 2019 were analyzed, the preoperative DTI and 1H-MRS images were collected, apparent diffusion coefficient (ADC) and fractional anisotropy (FA), in the lesion area were measured, the relative values relative ADC (rADC) and relative FA (rFA) were obtained by the ratio of them in the lesion area to the contralateral normal area. The peak of each metabolite in the lesion area of 1H-MRS image: N-acetylaspartate (NAA), choline (Cho), and creatine (Cr), and metabolite ratio: NAA/Cho, NAA/(Cho + Cr) were selected and calculated. The preoperative IHC data were collected including CD34, Ki-67, p53, S-100, syn, vimentin, NeuN, Nestin, and glial fibrillary acidic protein. Results: One predicting parameter of DTI was screened, the rADC of the Ki-67 positive group was lower than that of the negative group. Two parameters of 1H-MRS were found to have significant reference values for glioma grades, the NAA and Cr decreased as the grade of glioma increased, moreover, Ki-67 Li was negatively correlated with NAA and Cr. Conclusion: NAA and Cr have potential application value in predicting glioma grades and tumor proliferation activity. Only rADC has predictive value for Ki-67 expression among DTI parameters.

Glycerol-slaved 1H-1H NMR cross-relaxation in quasi-native lysozyme.

1H-1H nuclear cross-relaxation experiments have been carried out with lysozyme in variable glycerol viscosity to study intramolecular motion, self-diffusion, and isotropic rigid-body rotational tumbling at 298 K, pH 3.8. Dynamics of intramolecular 1H-1H cross-relaxation rates, the increase in internuclear spatial distances, and lateral and rotational diffusion coefficients all show fractional viscosity dependence with a power law exponent κ in the 0.17-0.83 range. The diffusion coefficient of glycerol Ds with the bulk viscosity itself is non-Stokesian, having a fractional viscosity dependence on the medium viscosity (Ds âˆ¼ Î·-κ, κ ≈ 0.71). The concurrence and close similarity of the fractional viscosity dependence of glycerol diffusion on the one hand, and diffusion and intramolecular cross-relaxation rates of the protein on the other lead to infer that relaxation of glycerol slaves protein relaxations. Glycerol-transformed native lysozyme to a quasi-native state does not affect the conclusion that both global and internal fluctuations are slaved to glycerol relaxation.

1H and 31P Magnetic Resonance Spectroscopic Metabolomic Imaging: Assessing Mitogen-Activated Protein Kinase Inhibition in Melanoma.

The MAPK signaling pathway with BRAF mutations has been shown to drive the pathogenesis of 40-60% of melanomas. Inhibitors of this pathway's BRAF and MEK components are currently used to treat these malignancies. However, responses to these treatments are not always successful. Therefore, identifying noninvasive biomarkers to predict treatment responses is essential for personalized medicine in melanoma. Using noninvasive 1H magnetic resonance spectroscopy (1H MRS), we previously showed that BRAF inhibition reduces lactate and alanine tumor levels in the early stages of effective therapy and could be considered as metabolic imaging biomarkers for drug response. The present work demonstrates that these metabolic changes observed by 1H MRS and those assessed by 31P MRS are also found in preclinical human melanoma models treated with MEK inhibitors. Apart from 1H and 31P MRS, additional supporting in vitro biochemical analyses are described. Our results indicate significant early metabolic correlations with response levels to MEK inhibition in the melanoma models and are consistent with our previous study of BRAF inhibition. Given these results, our study supports the potential clinical utility of noninvasive MRS to objectively image metabolic biomarkers for the early prediction of melanoma's response to MEK inhibition.

Blending Samples to Increase Accuracy and Precision of 1H NMR Urine Metabolomics.

Urine is an equally attractive biofluid for metabolomics analysis, as it is a challenging matrix analytically. Accurate urine metabolite concentration estimates by Nuclear Magnetic Resonance (NMR) are hampered by pH and ionic strength differences between samples, resulting in large peak shift variability. Here we show that calculating the spectra of original samples from mixtures of samples using linear algebra reduces the shift problems and makes various error estimates possible. Since the use of two-dimensional (2D) NMR to confirm metabolite annotations is effectively impossible to employ on every sample of large sample sets, stabilization of metabolite peak positions increases the confidence in identifying metabolites, avoiding the pitfall of oranges-to-apples comparisons.

Self-organizing maps for exploration and classification of nuclear magnetic resonance spectra for untargeted metabolomics of breast cancer.

Metabolomics has emerged as a powerful tool for identifying biomarkers of disease, and nuclear magnetic resonance (NMR) spectroscopy allows for the simultaneous detection of a wide range of metabolites. However, due to complex interactions within metabolic networks, metabolites often exhibit high correlation and collinearity. To address this challenge, self-organizing maps (SOMs) of Kohonen maps and counter propagation-artificial neural networks (CP-ANN) were employed in this study to model proton nuclear magnetic resonance spectroscopic (1HNMR) data from control samples and breast cancer (BC) patients. Blood serum samples from a control group (n=24) and BC patients (n=18) were used to extract metabolites using methanol and chloroform solvents in optimum extraction conditions. The 1HNMR data was preprocessed by performing phase, baseline, and shift corrections. Subsequently, the preprocessed data was modeled using Kohonen network as an unsupervised technique and CP-ANN as a supervised technique. In this regard, the model built with CP-ANN successfully distinguished between the two classes with an accuracy of 100 % for both group and sensitivity of 96 % and 100 % for control group and BC patients, respectively. Additionally, CP-ANN algorithm demonstrated predictive capabilities by accurately classifying test samples with 90 % sensitivity, 98 % specificity, and 96 % accuracy for control group and 100 % sensitivity, 90 % specificity, and 96 % accuracy for BC patients. Furthermore, analysis of the resulting topological map revealed 14 significant variables (biomarkers) such as sarcosine, lysine, trehalose, tryptophan, and betaine that effectively differentiated between healthy individuals and BC patients.

Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling.

PURPOSE: Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear-combination model (2D-LCM) of individual transients ("model-based FPC"). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates. METHODS: We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground-truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data. RESULTS: 2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of FPC and amplitudes performed substantially better at low-to-very-low SNR. CONCLUSION: Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, for example, long TEs or strong diffusion weighting.

Neurochemical changes in the progression of Huntington's disease: A meta-analysis of in vivo1H-MRS studies.

Proton magnetic resonance spectroscopy (1H-MRS) allows measuring specific brain metabolic alterations in Huntington's disease (HD), and these metabolite profiles may serve as non-invasive biomarkers associated with disease progression. Despite this potential, previous findings are inconsistent. Accordingly, we performed a meta-analysis on available in vivo1H-MRS studies in premanifest (Pre-HD) and symptomatic HD stages (Symp-HD), and quantified neurometabolic changes relative to controls in 9 Pre-HD studies (227 controls and 188 mutation carriers) and 14 Symp-HD studies (326 controls and 306 patients). Our results indicated decreased N-acetylaspartate and creatine in the basal ganglia in both Pre-HD and Symp-HD. The overall level of myo-inositol was decreased in Pre-HD while increased in Symp-HD. Besides, Symp-HD patients showed more severe metabolism disruption than Pre-HD patients. Taken together, 1H-MRS is important for elucidating progressive metabolite changes from Pre-HD to clinical conversion; N-acetylaspartate and creatine in the basal ganglia are already sensitive at the preclinical stage and are promising biomarkers for tracking disease progression; overall myo-inositol is a possible characteristic metabolite for distinguishing HD stages.

Personalized and muscle-specific OXPHOS measurement with integrated CrCEST MRI and proton MR spectroscopy.

Creatine chemical exchange saturation transfer (CrCEST) MRI is an emerging high resolution and noninvasive method for measuring muscle specific oxidative phosphorylation (OXPHOS). However, CrCEST measurements are sensitive to changes in muscle pH, which might confound the measurement and interpretation of creatine recovery time (τCr). Even with the same prescribed exercise stimulus, the extent of acidification and hence its impact on τCr is expected to vary between individuals. To address this issue, a method to measure pH pre- and post-exercise and its impact on CrCEST MRI with high temporal resolution is needed. In this work, we integrate carnosine 1H- magnetic resonance spectroscopy (MRS) and 3D CrCEST to establish "mild" and "moderate/intense" exercise stimuli. We then test the dependence of CrCEST recovery time on pH using different exercise stimuli. This comprehensive metabolic imaging protocol will enable personalized, muscle specific OXPHOS measurements in both healthy aging and myriad other disease states impacting muscle mitochondria.

Region-specific Cerebral Metabolic Alterations in Parkinson's Disease Patients With/without Mild Cognitive Impairment.

N-acetylaspartate (NAA), choline (Cho) and creatine (Cr) are brain metabolites involved in some key neuronal functions within the brain, such as cognitive function. The aim of this study was to investigate whether Parkinson's disease (PD) with different cognitive status induces regional brain metabolite differences. 38 diagnosed PD patients, including 18 PD patients with normal cognitive (PDN), 20 PD subjects with cognitive impairment (PDMCI) and 25 healthy controls (HC) participated in this study. All subjects underwent a single-voxel proton MR spectroscopy (1H-MRS) on a 3T scanner. 1H-MRS were obtained from bilateral PCC, left thalamus and PFC regions in all subjects, respectively. Region-specific cerebral metabolic alterations existed in PD patients with different cognitive status. PDMCI patients showed a significant reduction of NAA, Cho and tCr in the PCC and left thalamus, compared to healthy controls; whereas lower levels of NAA and Cho in thalamus were found in PDN patients. Moreover, Cho and tCr levels were positively correlated with MMSE scores. Both NAA and tCr in PCC levels were positively correlated with MMSE and MoCA scores. The combination of thalamic and PCC metabolites showed a 75.6% accuracy in distinguishing PDMCI patients from PDN patients. This study provides preliminary evidence that thalamic, PCC and PFC neurometabolic alterations occur in PD patients with cognition decline. Findings of this study indicate that NAA and tCr abnormalities in PCC and thalamus might be used as a biomarker to track cognitive decline in Parkinson's disease in clinical settings.

1H NMR profiling and chemometric analysis for ripening and production characterization of Grana Padano cheese.

Grana Padano (GP) cheese is a renowned PDO Italian cheese whose nutritional characteristics and market price are influenced by the ripening stage. In this work, it was demonstrated that the combined use of untargeted 1H NMR profiling and chemometric analysis can be used as a powerful tool to quantitatively characterize GP ripening and production, focusing on both aqueous and lipid fractions. An initial exploratory analysis revealed substantial variations in the aqueous fraction attributable to aging time, year and season of production. Multivariate analysis was adopted to show these differences, mainly attributable to amino acids. In contrast, the lipid fraction analysis highlighted the impact of production season on fatty acid unsaturation, influenced by feed variations. As regards the production process, this study focuses on the variations induced by bactofugation. In this respect, the aqueous fraction was found to be extensively influenced by this centrifugation step, affecting compounds crucial to organoleptic characteristics.

A Novel Data Fusion Strategy of GC-MS and 1H NMR Spectra for the Identification of Different Vintages of Maotai-flavor Baijiu.

Counterfeit Baijiu has been emerging because of the price variances of real-aged Chinese Baijiu. Accurate identification of different vintages is of great interest. In this study, the combination of gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy was applied for the comprehensive analysis of chemical constituents for Maotai-flavor Baijiu. Furthermore, a novel data fusion strategy combined with machine learning algorithms has been established. The results showed that the midlevel data fusion combined with the random forest algorithm were the best and successfully applied for classification of different Baijiu vintages. A total of 14 differential compounds (belonging to fatty acid ethyl esters, alcohols, organic acids, and aldehydes) were identified, and used for evaluation of commercial Maotai-flavor Baijiu. Our results indicated that both volatiles and nonvolatiles contributed to the vintage differences. This study demonstrated that GC-MS and 1H NMR spectra combined with a data fusion strategy are practical for the classification of different vintages of Maotai-flavor Baijiu.

1H NMR spectroscopic characterisation of HepG2 cells as a model metabolic system for toxicology studies.

The immortalised human hepatocellular HepG2 cell line is commonly used for toxicology studies as an alternative to animal testing due to its characteristic liver-distinctive functions. However, little is known about the baseline metabolic changes within these cells upon toxin exposure. We have applied 1H Nuclear Magnetic Resonance (NMR) spectroscopy to characterise the biochemical composition of HepG2 cells at baseline and post-exposure to hydrogen peroxide (H2O2). Metabolic profiles of live cells, cell extracts, and their spent media supernatants were obtained using 1H high-resolution magic angle spinning (HR-MAS) NMR and 1H NMR spectroscopic techniques. Orthogonal partial least squares discriminant analysis (O-PLS-DA) was used to characterise the metabolites that differed between the baseline and H2O2 treated groups. The results showed that H2O2 caused alterations to 10 metabolites, including acetate, glutamate, lipids, phosphocholine, and creatine in the live cells; 25 metabolites, including acetate, alanine, adenosine diphosphate (ADP), aspartate, citrate, creatine, glucose, glutamine, glutathione, and lactate in the cell extracts, and 22 metabolites, including acetate, alanine, formate, glucose, pyruvate, phenylalanine, threonine, tryptophan, tyrosine, and valine in the cell supernatants. At least 10 biochemical pathways associated with these metabolites were disrupted upon toxin exposure, including those involved in energy, lipid, and amino acid metabolism. Our findings illustrate the ability of NMR-based metabolic profiling of immortalised human cells to detect metabolic effects on central metabolism due to toxin exposure. The established data sets will enable more subtle biochemical changes in the HepG2 model cell system to be identified in future toxicity testing.