Search

ABSTRACT: Proton nuclear magnetic resonance (1H NMR) spectroscopy is acknowledged as one of the most powerful analytical methods with cross-cutting applications in dairy foods. To date, the use of 1H NMR spectroscopy for the collection of milk metabolic profile is hindered by costly and time-consuming sample preparation and analysis. The present study aimed at evaluating the accuracy of mid-infrared spectroscopy (MIRS) as a rapid method for the prediction of cow milk metabolites determined through 1H NMR spectroscopy. Bulk milk (n = 72) and individual milk samples (n = 482) were analyzed through one-dimensional 1H NMR spectroscopy and MIRS. Nuclear magnetic resonance spectroscopy identified 35 milk metabolites, which were quantified in terms of relative abundance, and MIRS prediction models were developed on the same 35 milk metabolites, using partial least squares regression analysis. The best MIRS prediction models were developed for galactose-1-phosphate, glycerophosphocholine, orotate, choline, galactose, lecithin, glutamate, and lactose, with coefficient of determination in external validation from 0.58 to 0.85, and ratio of performance to deviation in external validation from 1.50 to 2.64. The remaining 27 metabolites were poorly predicted. This study represents a first attempt to predict milk metabolome. Further research is needed to specifically address whether developed prediction models may find practical application in the dairy sector, with particular regard to the screening of dairy cows' metabolic status, the quality control of dairy foods, and the identification of processed milk or incorrectly stored milk.

ABSTRACT: Effective traceability tools able to characterize milk from pasture are important to safeguard low-input farming systems, niche dairy products, and local traditions. The aims of the present study were to investigate the ability of proton nuclear magnetic resonance (1H NMR) spectroscopy to discriminate between milk produced from cows before and after the beginning of the grazing season, and to assess the effects of grazing on milk metabolites. The research trial involved a single alpine holding with 72 lactating cows. Individual milks were repeatedly sampled from the same animals before (i.e., d −3 and −1) and after (i.e., d 2, 3, 7, 10, and 14) the onset of the grazing period. One-dimensional 1H NMR spectra of milk extracts were collected through a Bruker spectrometer. Random forest discriminant analysis was applied to 1H NMR spectra to predict the period of collection for each sample. Data concerning the relative abundance of milk metabolites were analyzed through a linear mixed model, which included the fixed effects of period of sampling, cow breed, stage of lactation, and parity, and the random effect of cow nested within breed. The random forest model exhibited great accuracy (93.1%) in discriminating between samples collected on d −3, −1, 2, and 3 and those collected on d 7, 10, and 14. Univariate analysis performed on the 40 detected metabolites highlighted that milk samples from pasture had lower levels of 14 compounds (with fumarate being the most depressed metabolite) and greater levels of 15 compounds (with methanol and hippurate being the most elevated metabolites). Results indicate that milk 1H NMR spectra are promising to identify milk produced in different conditions. Also, our study highlights that grazing is associated with significant changes of milk metabolic profile, suggesting the potential use of several metabolites as indicators of farm management.

ABSTRACT: Nuclear magnetic resonance spectroscopy was applied to investigate the association between milk metabolome and udder quarter health status in dairy cows. Mammary gland health status was defined by combining information provided by traditional somatic cell count (SCC) and differential SCC (DSCC), which expresses the percentage of neutrophils and lymphocytes over total SCC. Quarter milk samples were collected in triplicate (d 1 to 3) from 10 Simmental cows, 5 defined as cases and 5 defined as controls according to SCC levels at d 0. A total of 120 samples were collected and analyzed for bacteriology, milk composition, SCC, DSCC, and milk metabolome. Bacteriological analysis revealed the presence of mostly coagulase-negative staphylococci in quarter milk samples of cows defined as cases. Nuclear magnetic resonance spectra of all quarter samples were first analyzed using the unsupervised multivariate approach principal component analysis, which revealed a specific metabolomic fingerprint of each cow. Then, the supervised cross-validated orthogonal projections to latent structures discriminant analysis unquestionably showed that each cow could be very well identified according to its milk metabolomic fingerprint (accuracy = 95.8%). The comparison of 12 different models, built on bucketed 1-dimensional NOESY spectra (noesygppr1d, Bruker BioSpin) using different SCC and DSCC thresholds, corroborated the assumption of improved udder health status classification ability by joining information provided by both SCC and DSCC. Univariate analysis performed on the 34 quantitated metabolites revealed lower levels of riboflavin, galactose, galactose-1-phosphate, dimethylsulfone, carnitine, hippurate, orotate, lecithin, succinate, glucose, and lactose, and greater levels of lactate, phenylalanine, choline, acetate, O-acetylcarnitine, 2-oxoglutarate, and valine, in milk samples with high somatic cells. In the 5 cases, results of the udder quarter with the highest SCC compared with its symmetrical relative were in line with quarter-level findings. Our study suggests that increased SCC is associated with changes in milk metabolite fingerprint and highlights the potential use of different metabolites as novel indicators of udder health status and milk quality.

Cross-relaxation terms in paramagnetic systems that reorient rigidly with slow tumbling times can increase the effective longitudinal relaxation rates of protons of more than 1 order of magnitude. This is evaluated by simulating the time evolution of the nuclear magnetization using a complete relaxation rate-matrix approach. The calculations show that the Solomon dependence of the paramagnetic relaxation rates on the metal–proton distance (as r−6) can be incorrect for protons farther than 15 Å from the metal and thus can cause sizable errors in R1-derived distance restraints used, for instance, for protein structure determination. Furthermore, the chemical exchange of these protons with bulk water protons can enhance the relaxation rate of the solvent protons by far more than expected from the paramagnetic Solomon equation. Therefore, it may contribute significantly to the water proton relaxation rates measured at magnetic resonance imaging (MRI) magnetic fields in the presence of slow-rotating nanoparticles containing paramagnetic ions and a large number of exchangeable surface protons.

Plasma of COVID-19 patients contains a strong metabolomic/lipoproteomic signature, revealed by the NMR analysis of a cohort of >500 patients sampled during various waves of COVID-19 infection, corresponding to the spread of different variants, and having different vaccination status. This composite signature highlights common traits of the SARS-CoV-2 infection. The most dysregulated molecules display concentration trends that scale with disease severity and might serve as prognostic markers for fatal events. Metabolomics evidence is then used as input data for a sex-specific multi-organ metabolic model. This reconstruction provides a comprehensive view of the impact of COVID-19 on the entire human metabolism. The human (male and female) metabolic network is strongly impacted by the disease to an extent dictated by its severity. A marked metabolic reprogramming at the level of many organs indicates an increase in the generic energetic demand of the organism following infection. Sex-specific modulation of immune response is also suggested., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ghini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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 ( 1 H 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., Competing Interests: The authors declare no competing interests., (© 2023.)

Proton nuclear magnetic resonance ( 1 H NMR) spectroscopy is acknowledged as one of the most powerful analytical methods with cross-cutting applications in dairy foods. To date, the use of 1 H NMR spectroscopy for the collection of milk metabolic profile is hindered by costly and time-consuming sample preparation and analysis. The present study aimed at evaluating the accuracy of mid-infrared spectroscopy (MIRS) as a rapid method for the prediction of cow milk metabolites determined through 1 H NMR spectroscopy. Bulk milk (n = 72) and individual milk samples (n = 482) were analyzed through one-dimensional 1 H NMR spectroscopy and MIRS. Nuclear magnetic resonance spectroscopy identified 35 milk metabolites, which were quantified in terms of relative abundance, and MIRS prediction models were developed on the same 35 milk metabolites, using partial least squares regression analysis. The best MIRS prediction models were developed for galactose-1-phosphate, glycerophosphocholine, orotate, choline, galactose, lecithin, glutamate, and lactose, with coefficient of determination in external validation from 0.58 to 0.85, and ratio of performance to deviation in external validation from 1.50 to 2.64. The remaining 27 metabolites were poorly predicted. This study represents a first attempt to predict milk metabolome. Further research is needed to specifically address whether developed prediction models may find practical application in the dairy sector, with particular regard to the screening of dairy cows' metabolic status, the quality control of dairy foods, and the identification of processed milk or incorrectly stored milk., (© 2023, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Background: Aggregation of α-synuclein (α-syn) is a prominent feature of Parkinson's disease (PD) and other synucleinopathies. Currently, α-syn seed amplification assays (SAAs) using cerebrospinal fluid (CSF) represent the most promising diagnostic tools for synucleinopathies. However, CSF itself contains several compounds that can modulate the aggregation of α-syn in a patient-dependent manner, potentially undermining unoptimized α-syn SAAs and preventing seed quantification., Methods: In this study, we characterized the inhibitory effect of CSF milieu on detection of α-syn aggregates by means of CSF fractionation, mass spectrometry, immunoassays, transmission electron microscopy, solution nuclear magnetic resonance spectroscopy, a highly accurate and standardized diagnostic SAA, and different in vitro aggregation conditions to evaluate spontaneous aggregation of α-syn., Results: We found the high-molecular weight fraction of CSF (> 100,000 Da) to be highly inhibitory on α-syn aggregation and identified lipoproteins to be the main drivers of this effect. Direct interaction between lipoproteins and monomeric α-syn was not detected by solution nuclear magnetic resonance spectroscopy, on the other hand we observed lipoprotein-α-syn complexes by transmission electron microscopy. These observations are compatible with hypothesizing an interaction between lipoproteins and oligomeric/proto-fibrillary α-syn intermediates. We observed significantly slower amplification of α-syn seeds in PD CSF when lipoproteins were added to the reaction mix of diagnostic SAA. Additionally, we observed a decreased inhibition capacity of CSF on α-syn aggregation after immunodepleting ApoA1 and ApoE. Finally, we observed that CSF ApoA1 and ApoE levels significantly correlated with SAA kinetic parameters in n = 31 SAA-negative control CSF samples spiked with preformed α-syn aggregates., Conclusions: Our results describe a novel interaction between lipoproteins and α-syn aggregates that inhibits the formation of α-syn fibrils and could have relevant implications. Indeed, the donor-specific inhibition of CSF on α-syn aggregation explains the lack of quantitative results from analysis of SAA-derived kinetic parameters to date. Furthermore, our data show that lipoproteins are the main inhibitory components of CSF, suggesting that lipoprotein concentration measurements could be incorporated into data analysis models to eliminate the confounding effects of CSF milieu on α-syn quantification efforts., (© 2023. The Author(s).)

Colorectal cancer (CRC), one of the most prevalent and deadly cancers worldwide, generally evolves from adenomatous polyps. The understanding of the molecular mechanisms underlying this pathological evolution is crucial for diagnostic and prognostic purposes. Integrative systems biology approaches offer an optimal point of view to analyze CRC and patients with polyposis. The present study analyzed the association networks constructed from a publicly available array of 113 serum metabolites measured on a cohort of 234 subjects from three groups (66 CRC patients, 76 patients with polyposis, and 92 healthy controls), which concentrations were obtained via targeted liquid chromatography-tandem mass spectrometry. In terms of architecture, topology, and connectivity, the metabolite-metabolite association network of CRC patients appears to be completely different with respect to patients with polyposis and healthy controls. The most relevant nodes in the CRC network are those related to energy metabolism. Interestingly, phenylalanine, tyrosine, and tryptophan metabolism are found to be involved in both CRC and polyposis. Our results demonstrate that the characterization of metabolite-metabolite association networks is a promising and powerful tool to investigate molecular aspects of CRC.

Background: We previously showed that metabolomics predicts relapse in early breast cancer (eBC) patients, unselected by age. This study aims to identify a "metabolic signature" that differentiates eBC from advanced breast cancer (aBC) patients, and to investigate its potential prognostic role in an elderly population., Methods: Serum samples from elderly breast cancer (BC) patients enrolled in 3 onco-geriatric trials, were retrospectively analyzed via proton nuclear magnetic resonance (1H NMR) spectroscopy. Three nuclear magnetic resonance (NMR) spectra were acquired for each serum sample: NOESY1D, CPMG, Diffusion-edited. Random Forest (RF) models to predict BC relapse were built on NMR spectra, and resulting RF risk scores were evaluated by Kaplan-Meier curves., Results: Serum samples from 140 eBC patients and 27 aBC were retrieved. In the eBC cohort, median age was 76 years; 77% of patients had luminal, 10% HER2-positive and 13% triple negative (TN) BC. Forty-two percent of patients had tumors >2 cm, 43% had positive axillary nodes. Using NOESY1D spectra, the RF classifier discriminated free-from-recurrence eBC from aBC with sensitivity, specificity and accuracy of 81%, 67% and 70% respectively. We tested the NOESY1D spectra of each eBC patient on the RF models already calculated. We found that patients classified as "high risk" had higher risk of disease recurrence (hazard ratio (HR) 3.42, 95% confidence interval (CI) 1.58-7.37) than patients at low-risk., Conclusions: This analysis suggests that a "metabolic signature", identified employing NMR fingerprinting, is able to predict the risk of disease recurrence in elderly patients with eBC independently from standard clinicopathological features., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Effective traceability tools able to characterize milk from pasture are important to safeguard low-input farming systems, niche dairy products, and local traditions. The aims of the present study were to investigate the ability of proton nuclear magnetic resonance ( 1 H NMR) spectroscopy to discriminate between milk produced from cows before and after the beginning of the grazing season, and to assess the effects of grazing on milk metabolites. The research trial involved a single alpine holding with 72 lactating cows. Individual milks were repeatedly sampled from the same animals before (i.e., d -3 and -1) and after (i.e., d 2, 3, 7, 10, and 14) the onset of the grazing period. One-dimensional 1 H NMR spectra of milk extracts were collected through a Bruker spectrometer. Random forest discriminant analysis was applied to 1 H NMR spectra to predict the period of collection for each sample. Data concerning the relative abundance of milk metabolites were analyzed through a linear mixed model, which included the fixed effects of period of sampling, cow breed, stage of lactation, and parity, and the random effect of cow nested within breed. The random forest model exhibited great accuracy (93.1%) in discriminating between samples collected on d -3, -1, 2, and 3 and those collected on d 7, 10, and 14. Univariate analysis performed on the 40 detected metabolites highlighted that milk samples from pasture had lower levels of 14 compounds (with fumarate being the most depressed metabolite) and greater levels of 15 compounds (with methanol and hippurate being the most elevated metabolites). Results indicate that milk 1 H NMR spectra are promising to identify milk produced in different conditions. Also, our study highlights that grazing is associated with significant changes of milk metabolic profile, suggesting the potential use of several metabolites as indicators of farm management., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The development of metabolomics in clinical applications has been limited by the lack of validation in large multicenter studies. Large population cohorts and their biobanks are a valuable resource for acquiring insights into molecular disease mechanisms. Nevertheless, most of their collections are not tailored for metabolomics and have been created without specific attention to the pre-analytical requirements for high-quality metabolome assessment. Thus, comparing samples obtained by different pre-analytical procedures remains a major challenge. Here, 1 H NMR-based analyses are used to demonstrate how human serum and plasma samples collected with different operating procedures within several large European cohort studies from the Biobanking and Biomolecular Resources Infrastructure - Large Prospective Cohorts (BBMRI-LPC) consortium can be easily revealed by supervised multivariate statistical analyses at the initial stages of the process, to avoid biases in the downstream analysis. The inter-biobank differences are discussed in terms of deviations from the validated CEN/TS 16945:2016 / ISO 23118:2021 norms. It clearly emerges that biobanks must adhere to the evidence-based guidelines in order to support wider-scale application of metabolomics in biomedicine, and that NMR spectroscopy is informative in comparing the quality of different sample sources in multi cohort/center studies., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Metabolomics and lipidomics have been used in several studies to define the biochemical alterations induced by COVID-19 in comparison with healthy controls. Those studies highlighted the presence of a strong signature, attributable to both metabolites and lipoproteins/lipids. Here, 1H NMR spectra were acquired on EDTA-plasma from three groups of subjects: i) hospitalized COVID-19 positive patients (≤21 days from the first positive nasopharyngeal swab); ii) hospitalized COVID-19 positive patients (>21 days from the first positive nasopharyngeal swab); iii) subjects after 2-6 months from SARS-CoV-2 eradication. A Random Forest model built using the EDTA-plasma spectra of COVID-19 patients ≤21 days and Post COVID-19 subjects, provided a high discrimination accuracy (93.6%), indicating both the presence of a strong fingerprint of the acute infection and the substantial metabolic healing of Post COVID-19 subjects. The differences originate from significant alterations in the concentrations of 16 metabolites and 74 lipoprotein components. The model was then used to predict the spectra of COVID-19>21 days subjects. In this group, the metabolite levels are closer to those of the Post COVID-19 subjects than to those of the COVID-19≤21 days; the opposite occurs for the lipoproteins. Within the acute phase patients, characteristic trends in metabolite levels are observed as a function of the disease severity. The metabolites found altered in COVID-19≤21 days patients with respect to Post COVID-19 individuals overlap with acute infection biomarkers identified previously in comparison with healthy subjects. Along the trajectory towards healing, the metabolome reverts back to the "healthy" state faster than the lipoproteome., Competing Interests: The authors have declared that no competing interests exist.

Background: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. Metabolomics may help refine risk assessment and potentially guide HF management, but dedicated studies are few. This study aims at stratifying the long-term risk of death in a cohort of patients affected by HF due to dilated cardiomyopathy (DCM) using serum metabolomics via nuclear magnetic resonance (NMR) spectroscopy., Methods: A cohort of 106 patients with HF due to DCM, diagnosed and monitored between 1982 and 2011, were consecutively enrolled between 2010 and 2012, and a serum sample was collected from each participant. Each patient underwent half-yearly clinical assessments, and survival status at the last follow-up visit in 2019 was recorded. The NMR serum metabolomic profiles were retrospectively analyzed to evaluate the patient's risk of death. Overall, 26 patients died during the 8-years of the study., Results: The metabolomic fingerprint at enrollment was powerful in discriminating patients who died (HR 5.71, p = 0.00002), even when adjusted for potential covariates. The outcome prediction of metabolomics surpassed that of N-terminal pro b-type natriuretic peptide (NT-proBNP) (HR 2.97, p = 0.005). Metabolomic fingerprinting was able to sub-stratify the risk of death in patients with both preserved/mid-range and reduced ejection fraction [hazard ratio (HR) 3.46, p = 0.03; HR 6.01, p = 0.004, respectively]. Metabolomics and left ventricular ejection fraction (LVEF), combined in a score, proved to be synergistic in predicting survival (HR 8.09, p = 0.0000004)., Conclusions: Metabolomic analysis via NMR enables fast and reproducible characterization of the serum metabolic fingerprint associated with poor prognosis in the HF setting. Our data suggest the importance of integrating several risk parameters to early identify HF patients at high-risk of poor outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Vignoli, Fornaro, Tenori, Castelli, Cecconi, Olivotto, Marchionni, Alterini and Luchinat.)

This study defines and estimates the metabolite-lipidic component association networks constructed from an array of 20 metabolites and 114 lipids identified and quantified via NMR spectroscopy in the serum of a cohort of 355 Italian nonagenarians and ultra-nonagenarian. Metabolite-lipid association networks were built for men and women and related to an array of 101 clinical and biochemical parameters, including the presence of diseases, bio-humoral parameters, familiarity diseases, drugs treatments, and risk factors. Different connectivity patterns were observed in lipids, branched chains amino acids, alanine, and ketone bodies, suggesting their association with the sex-related and sex-clinical condition-related intrinsic metabolic changes. Furthermore, our results demonstrate, using a holistic system biology approach, that the characterization of metabolic structures and their dynamic inter-connections is a promising tool to shed light on the dimorphic pathophysiological mechanisms of aging at the molecular level., (© 2021. The Author(s).)

Parkinson's disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil the molecular basis of PD, providing a detailed characterization of potentially pathological alterations in various biological specimens. Metabolomics could provide useful insights to deepen our knowledge of PD aetiopathogenesis, to identify signatures that distinguish groups of patients and uncover responsive biomarkers of PD that may be significant in early detection and in tracking the disease progression and drug treatment efficacy. The present work is the first large metabolomic study based on nuclear magnetic resonance (NMR) with an independent validation cohort aiming at the serum characterization of de novo drug-naive PD patients. Here, NMR is applied to sera from large training and independent validation cohorts of German subjects. Multivariate and univariate approaches are used to infer metabolic differences that characterize the metabolite and the lipoprotein profiles of newly diagnosed de novo drug-naive PD patients also in relation to the biological sex of the subjects in the study, evidencing a more pronounced fingerprint of the pathology in male patients. The presence of a validation cohort allowed us to confirm altered levels of acetone and cholesterol in male PD patients. By comparing the metabolites and lipoproteins levels among de novo drug-naive PD patients, age- and sex-matched healthy controls, and a group of advanced PD patients, we detected several descriptors of stronger oxidative stress., (© 2022. The Author(s).)

Introduction: Body-mass index is a major determinant of left-ventricular-mass (LVM). Bariatric-metabolic surgery (BMS) reduces cardiovascular mortality. Its mechanism of action, however, often encompasses a weight-dependent effect. In this translational study, we aimed at investigating the mechanisms by which BMS leads to LVM reduction and functional improvement., Methods: Twenty patients (45.2 ± 8.5years) were studied with echocardiography at baseline and at 1,6,12 and 48 months after sleeve-gastrectomy (SG). Ten Wistar rats aged 10-weeks received high-fat diet ad libitum for 10 weeks before and 4 weeks after SG or sham-operation. An oral-glucose-tolerance-test was performed to measure whole-body insulin-sensitivity. Plasma metabolomics was analysed in both human and rodent samples. RNA quantitative Real-Time PCR and western blots were performed in rodent heart biopsies. The best-fitted partial-least-square discriminant-analysis model was used to explore the variable importance in the projection score of all metabolites., Findings: Echocardiographic LVM (-12%,-23%,-28% and -43% at 1,6,12 and 48 months, respectively) and epicardial fat decreased overtime after SG in humans while insulin-sensitivity improved. In rats, SG significantly reduced LVM and epicardial fat, enhanced ejection-fraction and improved insulin-sensitivity compared to sham-operation. Metabolomics showed a progressive decline of plasma branched-chain amino-acids (BCAA), alanine, lactate, 3-OH-butyrate, acetoacetate, creatine and creatinine levels in both humans and rodents. Hearts of SG rats had a more efficient BCAA, glucose and fatty-acid metabolism and insulin signaling than sham-operation. BCAAs in cardiomyocyte culture-medium stimulated lipogenic gene transcription and reduced mRNA levels of key mitochondrial β-oxidation enzymes promoting lipid droplet accumulation and glycolysis., Interpretation: After SG a prompt and sustained decrease of the LVM, epicardial fat and insulin resistance was found. Animal and in vitro studies showed that SG improves cardiac BCAA metabolism with consequent amelioration of fat oxidation and insulin signaling translating into decreased intra-myocytic fat accumulation and reduced lipotoxicity., Funding: This work was supported by the University of Rome Sapienza., Competing Interests: Declaration of interests All authors report no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Nuclear magnetic resonance spectroscopy was applied to investigate the association between milk metabolome and udder quarter health status in dairy cows. Mammary gland health status was defined by combining information provided by traditional somatic cell count (SCC) and differential SCC (DSCC), which expresses the percentage of neutrophils and lymphocytes over total SCC. Quarter milk samples were collected in triplicate (d 1 to 3) from 10 Simmental cows, 5 defined as cases and 5 defined as controls according to SCC levels at d 0. A total of 120 samples were collected and analyzed for bacteriology, milk composition, SCC, DSCC, and milk metabolome. Bacteriological analysis revealed the presence of mostly coagulase-negative staphylococci in quarter milk samples of cows defined as cases. Nuclear magnetic resonance spectra of all quarter samples were first analyzed using the unsupervised multivariate approach principal component analysis, which revealed a specific metabolomic fingerprint of each cow. Then, the supervised cross-validated orthogonal projections to latent structures discriminant analysis unquestionably showed that each cow could be very well identified according to its milk metabolomic fingerprint (accuracy = 95.8%). The comparison of 12 different models, built on bucketed 1-dimensional NOESY spectra (noesygppr1d, Bruker BioSpin) using different SCC and DSCC thresholds, corroborated the assumption of improved udder health status classification ability by joining information provided by both SCC and DSCC. Univariate analysis performed on the 34 quantitated metabolites revealed lower levels of riboflavin, galactose, galactose-1-phosphate, dimethylsulfone, carnitine, hippurate, orotate, lecithin, succinate, glucose, and lactose, and greater levels of lactate, phenylalanine, choline, acetate, O-acetylcarnitine, 2-oxoglutarate, and valine, in milk samples with high somatic cells. In the 5 cases, results of the udder quarter with the highest SCC compared with its symmetrical relative were in line with quarter-level findings. Our study suggests that increased SCC is associated with changes in milk metabolite fingerprint and highlights the potential use of different metabolites as novel indicators of udder health status and milk quality., (© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Background: Colorectal cancer (CRC), the third most common cause of death in both males and females worldwide, shows a positive response to therapy and usually a better prognosis when detected at an early stage. However, the survival rate declines when the diagnosis is late and the tumor spreads to other organs. Currently, the measures widely used in the clinic are fecal occult blood test and evaluation of serum tumor markers, but the lack of sensitivity and specificity of these markers restricts their use for CRC diagnosis. Due to its high sensitivity and precision, colonoscopy is currently the gold-standard screening technique for CRC, but it is a costly and invasive procedure. Therefore, the implementation of custom-made methodologies including those with minimal invasiveness, protection, and reproducibility is highly desirable. With regard to other screening methods, the screening of fecal samples has several benefits, and metabolomics is a successful method to classify the metabolite shift in living systems as a reaction to pathophysiological influences, genetic modifications, and environmental factors., Aim: To characterize the variation groups and potentially recognize some diagnostic markers, we compared with healthy controls (HCs) the fecal nuclear magnetic resonance (NMR) metabolomic profiles of patients with CRC or adenomatous polyposis (AP)., Methods: Proton nuclear magnetic resonance spectroscopy was used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profiles of 32 CRC patients, 16 AP patients, and 38 HCs well matched in age, sex, and body mass index., Results: NMR metabolomic analyses revealed that fecal sample profiles differed among CRC patients, AP patients, and HCs, and some discriminatory metabolites including acetate, butyrate, propionate, 3-hydroxyphenylacetic acid, valine, tyrosine and leucine were identified., Conclusion: In conclusion, we are confident that our data can be a forerunner for future studies on CRC management, especially the diagnosis and evaluation of the effectiveness of treatments., Competing Interests: Conflict-of-interest statement: All other authors have nothing to disclose., (©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.)

Background: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks., Methods: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology., Results: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets., Conclusions: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care., (© 2021. The Author(s).)

Mammographic breast density (MBD) is a strong independent risk factor for breast cancer (BC). We designed a matched case-case study in the EPIC Florence cohort, to evaluate possible associations between the pre-diagnostic metabolomic profile and the risk of BC in high- versus low-MBD women who developed BC during the follow-up. A case-case design with 100 low-MBD (MBD ≤ 25%) and 100 high-MDB BC cases (MBD > 50%) was performed. Matching variables included age, year and type of mammographic examination. 1 H NMR metabolomic spectra were available for 87 complete case-case sets. The conditional logistic analyses showed an inverse association between serum levels of alanine, leucine, tyrosine, valine, lactic acid, pyruvic acid, triglycerides lipid main fraction and 11 VLDL lipid subfractions and high-MBD cases. Acetic acid was directly associated with high-MBD cases. In models adjusted for confounding variables, tyrosine remained inversely associated with high-MBD cases while 3 VLDL subfractions of free cholesterol emerged as directly associated with high-MBD cases. A pathway analysis showed that the "phenylalanine, tyrosine and tryptophan pathway" emerged and persisted after applying the FDR procedure. The supervised OPLS-DA analysis revealed a slight but significant separation between high- and low-MBD cases. This case-case study suggested a possible role for pre-diagnostic levels of tyrosine in modulating the risk of BC in high- versus low-MBD women. Moreover, some differences emerged in the pre-diagnostic concentration of other metabolites as well in the metabolomic fingerprints among the two groups of patients.

Adjuvant treatment for patients with early stage colorectal cancer (eCRC) is currently based on suboptimal risk stratification, especially for elderly patients. Metabolomics may improve the identification of patients with residual micrometastases after surgery. In this retrospective study, we hypothesized that metabolomic fingerprinting could improve risk stratification in patients with eCRC. Serum samples obtained after surgery from 94 elderly patients with eCRC (65 relapse free and 29 relapsed, after 5-years median follow up), and from 75 elderly patients with metastatic colorectal cancer (mCRC) obtained before a new line of chemotherapy, were retrospectively analyzed via proton nuclear magnetic resonance spectroscopy. The prognostic role of metabolomics in patients with eCRC was assessed using Kaplan-Meier curves. PCA-CA-kNN could discriminate the metabolomic fingerprint of patients with relapse-free eCRC and mCRC (70.0% accuracy using NOESY spectra). This model was used to classify the samples of patients with relapsed eCRC: 69% of eCRC patients with relapse were predicted as metastatic. The metabolomic classification was strongly associated with prognosis ( p -value 0.0005, HR 3.64), independently of tumor stage. In conclusion, metabolomics could be an innovative tool to refine risk stratification in elderly patients with eCRC. Based on these results, a prospective trial aimed at improving risk stratification by metabolomic fingerprinting (LIBIMET) is ongoing.

In this study, we investigated blood lipoprotein and lipid fraction profiles, quantified using nuclear magnetic resonance, in a cohort of 844 healthy blood donors, integrating standard univariate and multivariate analysis with predictive modeling and network analysis. We observed a strong association of lipoprotein and lipid main fraction profiles with sex and age. Our results suggest an age-dependent remodulation of lipase lipoprotein activity in men and a change in the mechanisms controlling the ratio between esterified and non-esterified cholesterol in both men and women.

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient's unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine)., Competing Interests: The authors declare no conflict of interest.

The current pandemic emergence of novel coronavirus disease (COVID-19) poses a relevant threat to global health. SARS-CoV-2 infection is characterized by a wide range of clinical manifestations, ranging from absence of symptoms to severe forms that need intensive care treatment. Here, plasma-EDTA samples of 30 patients compared with age- and sex-matched controls were analyzed via untargeted nuclear magnetic resonance (NMR)-based metabolomics and lipidomics. With the same approach, the effect of tocilizumab administration was evaluated in a subset of patients. Despite the heterogeneity of the clinical symptoms, COVID-19 patients are characterized by common plasma metabolomic and lipidomic signatures (91.7% and 87.5% accuracy, respectively, when compared to controls). Tocilizumab treatment resulted in at least partial reversion of the metabolic alterations due to SARS-CoV-2 infection. In conclusion, NMR-based metabolomic and lipidomic profiling provides novel insights into the pathophysiological mechanism of human response to SARS-CoV-2 infection and to monitor treatment outcomes., Competing Interests: The authors have declared that no competing interests exist.

Cross-relaxation terms in paramagnetic systems that reorient rigidly with slow tumbling times can increase the effective longitudinal relaxation rates of protons of more than 1 order of magnitude. This is evaluated by simulating the time evolution of the nuclear magnetization using a complete relaxation rate-matrix approach. The calculations show that the Solomon dependence of the paramagnetic relaxation rates on the metal-proton distance (as r - 6 ) can be incorrect for protons farther than 15 Å from the metal and thus can cause sizable errors in R 1 -derived distance restraints used, for instance, for protein structure determination. Furthermore, the chemical exchange of these protons with bulk water protons can enhance the relaxation rate of the solvent protons by far more than expected from the paramagnetic Solomon equation. Therefore, it may contribute significantly to the water proton relaxation rates measured at magnetic resonance imaging (MRI) magnetic fields in the presence of slow-rotating nanoparticles containing paramagnetic ions and a large number of exchangeable surface protons., Competing Interests: The authors declare that they have no conflict of interest., (Copyright: © 2021 Giovanni Bellomo et al.)

Magnetic resonance imaging (MRI) often requires contrast agents to improve the visualization in some tissues and organs, including the gastrointestinal tract. In this latter case, instead of intravascular administration, oral agents can be used. Natural oral contrast agents, such as fruit juice, have the advantages of better taste, tolerability, and lower price with respect to the artificial agents. We have characterized the relaxometry profiles of pineapple juice in order to understand the origin of the increase in relaxation rates (and thus of the MRI contrast) in reference to its content of manganese ions. Furthermore, we have characterized the relaxometry profiles of pineapple juice in the presence of alginate in different amounts; the interaction of the manganese ions with alginate slows down their reorientation time to some extent, with a subsequent increase in the relaxation rates. The relaxometry profiles were also compared with those of manganese(II) solutions in 50 mmol/dm 3 sodium acetate solution (same pH of pineapple juice), which revealed sizable differences, mostly in the number of water molecules coordinated to the metal ion, their lifetimes, and in the constant of the Fermi-contact interaction. Finally, the fit of the transverse relaxivity shows that the increased viscosity in the hydrogel formulations can improve significantly the negative contrast of pineapple juice at the magnetic fields relevant for clinical MRI., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Domenico Rizzo et al.)

Nature has evolved several molecular machineries to promote the formation at physiological conditions of inorganic materials, which would otherwise be formed in extreme conditions. The molecular determinants of this process have been established over the last decade, identifying a strong role of electrostatics in the first steps of the precipitation. However, no conclusive, structure-based evidence has been provided so far. In this manuscript, we test the binding of lysozyme with silica and titania potential precursors. In contrast with the absence of structural information about the interaction with the silica precursor, we observe the interaction with a mononuclear titanium(IV) species, which is found to occur in a region rich of positive charges.

In the treatment of advanced non-small cell lung cancer (NSCLC), immune checkpoint inhibitors have shown remarkable results. However, not all patients with NSCLC respond to this drug treatment or receive durable benefits. Thus, patient stratification and selection, as well as the identification of predictive biomarkers, represent pivotal aspects to address. In this framework, metabolomics can be used to support the discrimination between responders and non-responders. Here, metabolomics was used to analyze the sera samples from 50 patients with NSCL treated with immune checkpoint inhibitors. All the samples were collected before the beginning of the treatment and were analyzed by NMR spectroscopy and multivariate statistical analyses. Significantly, we show that the metabolomic fingerprint of serum acts as a predictive "collective" biomarker to immune checkpoint inhibitors response, being able to predict individual therapy outcome with > 80% accuracy. Metabolomics represents a potential strategy for the real-time selection and monitoring of patients treated with immunotherapy. The prospective identification of responders and non-responders could improve NSCLC treatment and patient stratification, thus avoiding ineffective therapeutic strategies.

Reduced activation of energy metabolism increases adiposity in humans and other mammals. Thus, exploring dietary and molecular mechanisms able to improve energy metabolism is of paramount medical importance because such mechanisms can be leveraged as a therapy for obesity and related disorders. Here, we show that a designer protein-deprived diet enriched in free essential amino acids can 1 ) promote the brown fat thermogenic program and fatty acid oxidation, 2 ) stimulate uncoupling protein 1 (UCP1)-independent respiration in subcutaneous white fat, 3 ) change the gut microbiota composition, and 4 ) prevent and reverse obesity and dysregulated glucose homeostasis in multiple mouse models, prolonging the healthy life span. These effects are independent of unbalanced amino acid ratio, energy consumption, and intestinal calorie absorption. A brown fat-specific activation of the mechanistic target of rapamycin complex 1 seems involved in the diet-induced beneficial effects, as also strengthened by in vitro experiments. Hence, our results suggest that brown and white fat may be targets of specific amino acids to control UCP1-dependent and -independent thermogenesis, thereby contributing to the improvement of metabolic health., (© 2020 by the American Diabetes Association.)

The establishment of the beneficial interactions between the host and its microbiota is essential for the correct functioning of the organism, since microflora alterations can lead to many diseases. Probiotics improve balanced microbial communities, exerting substantial health-promoting effects. Here we monitored the molecular outcomes, obtained by gut microflora modulation through probiotic treatment, on human urine and serum metabolic profiles, with a metabolomic approach. Twenty-two subjects were enrolled in the study and administered with two different probiotic types, both singularly and in combination, for 8 weeks. Urine and serum samples were collected before and during the supplementation and were analyzed by nuclear magnetic resonance (NMR) spectroscopy and statistical analyses. After eight weeks of treatment, probiotics deeply influence the urinary metabolic profiles of the volunteers, without significantly altering their single phenotypes. Anyway, bacteria supplementation tends to reduce the differences in metabolic phenotypes among individuals. Overall, the effects are recipient-dependent, and in some individuals, robust effects are already well visible after four weeks. Modifications in metabolite levels, attributable to each type of probiotic administration, were also monitored. Metabolomic analysis of biofluids turns out to be a powerful technique to monitor the dynamic interactions between the microflora and the host, and the individual response to probiotic assumption.

In recent years, new formula milk (FM) products based on milk from farms that strictly adhere to the "organic farming" practices became available. However, little is known about the differences in nutritional profile of these organic formulae with respect to traditional ones. We comprehensively evaluated the metabolite profiles of FM with nuclear magnetic resonance (NMR)-based metabolomic analysis. Five commercial brands of organic and nonorganic formula liquid milk for infants (0-12 months) and toddlers (1-3 years) were analyzed, together with human milk (HM) samples. Proton NMR ( 1 H NMR) spectroscopy mapped molecular characteristics of FM linked to different production techniques, and identified differences between FM and HM samples. We performed a metabolic fingerprint analysis using multivariate and univariate statistical techniques. A clear distinction is found among different commercial brands of the FM samples. In addition, several differences in metabolomic profiles of FM have been found in comparison with HM for the first time. Notably, it was possible to identify, both in the formulations for toddlers and for infants, metabolites that vary in concentration between the formulae produced with milk obtained according to organic farming techniques, and those produced using nonorganic milk. In particular, organic and nonorganic formulations are differentiated by the levels of glucose, methionine, o -phosphocholine, butyrate, hippurate, creatine, and dimethyl sulfone. Importantly, the HM appeared to differ from both organic and nonorganic brands in a context of metabolites. These findings inform efforts to design FM in ways that closely mimic HM, and guide research to differentiate organic and traditional FM.

Trisomy 21 (Down syndrome, DS) is the main human genetic cause of intellectual disability (ID). Lejeune hypothesized that DS could be considered a metabolic disease, and we found that subjects with DS have a specific plasma and urinary metabolomic profile. In this work we confirmed the alteration of mitochondrial metabolism in DS and also investigated if metabolite levels are related to cognitive aspects of DS. We analyzed the metabolomic profiles of plasma samples from 129 subjects with DS and 46 healthy control (CTRL) subjects by 1 H Nuclear Magnetic Resonance (NMR). Multivariate analysis of the NMR metabolomic profiles showed a clear discrimination (up to 94% accuracy) between the two groups. The univariate analysis revealed a significant alteration in 7 metabolites out of 28 assigned unambiguously. Correlations among the metabolite levels in DS and CTRL groups were separately investigated and statistically significant relationships appeared. On the contrary, statistically significant correlations among the NMR-detectable part of DS plasma metabolome and the different intelligence quotient ranges obtained by Griffiths-III or WPPSI-III tests were not found. Even if metabolic imbalance provides a clear discrimination between DS and CTRL groups, it appears that the investigated metabolomic profiles cannot be associated with the degree of ID.

Protein-protein interactions (PPIs) contribute to the onset and/or progression of several diseases, especially cancer, and this discovery has paved the way for considering disruption of the PPIs as an attractive anti-tumor strategy. In this regard, simple and efficient biophysical methods for detecting the interaction of the inhibitors with the protein counterpart are still in high demand. Herein, we describe a convenient NMR method for the screening of putative PPI inhibitors based on the use of "hot peptides" (HOPPI-NMR). As a case study, HOPPI-NMR was successful applied to the well-known p53/MDM2 system. Our outcomes highlight the main advantages of the method, including the use of a small amount of unlabeled proteins, the minimization of the risk of protein aggregation, and the ability to identify weak binders. The last leaves open the possibility for application of HOPPI-NMR in tandem with fragment-based drug discovery as a valid strategy for the identification of novel chemotypes acting as PPI inhibitors., Competing Interests: The authors declare no competing financial interest., (Copyright © 2020 American Chemical Society.)

HER2-positive breast cancer (BC) represents a heterogeneous cancer disease. In an attempt to identify new stratification models useful for prognosis and therapeutic strategy, we investigated the influence of estrogen receptor (ER) status on the host immune and metabolomics profile of HER2-positive BC patients enrolled for neoadjuvant targeted chemotherapy (NATC). The study enrolled 43 HER2-positive BC patients eligible for NATC based on the trastuzumab-paclitaxel combination. Baseline circulatory cytokines and 1 H NMR plasma metabolomics profiles were investigated. Differences in the immune cytokines and metabolomics profile as a function of the ER status, and their association with clinical outcomes were studied by multivariate and univariate analysis. Baseline metabolomics profiles were found to discriminate HER2-positive ER(+) from ER(-) BC patients. Within the ER(+) group an immune-metabolomics model, based on TNF-α and valine, predicted pathological complete response to NATC with 90.9% accuracy (AUROC = 0.92, p = 0.004). Moreover, metabolomics information integrated with IL-2 and IL-10 cytokine levels were prognostic of relapse with an accuracy of 95.5%. The results indicate that in HER2-positive BC patients the ER status influences the host circulatory immune-metabolomics profile. The baseline immune-metabolomics assessment in combination with ER status could represent an independent stratification tool able to predict NATC response and disease relapse of HER2-positive patients., Competing Interests: The authors have no competing financial and non-financial interests to disclose.

X-ray crystallography and NMR contain complementary information for the structural characterization of biological macromolecules. X-ray diffraction is primarily sensitive to the overall shape of the molecule, whereas NMR is mostly sensitive to the atomic detail. Their combination can therefore provide a stronger justification for the resulting structure. For their combination we have recently proposed REFMAC-NMR, which relies on primary data from both techniques for joint refinement. This possibility raises the compelling question of how far the complementarity can be extended. In this paper, we describe an integrative approach to the refinement with NMR data of four X-ray structures of hen-egg-white lysozyme, solved at atomic resolution in four different crystal forms, and we demonstrate that the outcome critically depends on the crystal form itself, reflecting the sensitivity of NMR to fine details., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 Published by Elsevier Inc.)

Docosahexaenoic acid (DHA) has been reported to have a positive impact on many diet-related disease risks, including metabolic syndrome. Although many DHA-enriched foods have been marketed, the impact of different food matrices on the effect of DHA is unknown. As well, the possibility to enhance DHA effectiveness through the co-administration of other bioactives has seldom been considered. We evaluated DHA effects on the serum metabolome administered to volunteers at risk of metabolic syndrome as an ingredient of three different foods. Foods were enriched with DHA alone or in combination with oat beta-glucan or anthocyanins and were administered to volunteers for 4 weeks. Serum samples collected at the beginning and end of the trial were analysed by NMR-based metabolomics. Multivariate and univariate statistical analyses were used to characterize modifications in the serum metabolome and to evaluate bioactive-bioactive and bioactive-food matrix interactions. DHA administration induces metabolome perturbation that is influenced by the food matrix and the co-presence of other bioactives. In particular, when co-administered with oat beta-glucan, DHA induces a strong rearrangement in the lipoprotein profile of the subjects. The observed modifications are consistent with clinical results and indicate that metabolomics represents a possible strategy to choose the most appropriate food matrices for bioactive enrichment.