Fingerprinting and profiling in metabolomics of biosamples.

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

Selenium Biofortification Impacts the Tomato Fruit Metabolome and Transcriptional Profile at Ripening.

In the present work, the effects of enriching tomatoes with selenium were studied in terms of physiological, metabolic, and molecular processes in the last stages of fruit development, particularly during ripening. A selenium concentration of 10 mg L-1 with sodium selenate and selenium nanoparticles was used in the spray treatments on the whole plants. No significant effects of selenium enrichment were detected in terms of ethylene production or color changes in the ripening fruit. However, selenium enrichment had an influence on both the primary and secondary metabolic processes and thus the biochemical composition of ripe tomatoes. Selenium decreased the amount of ß-carotene, increased the accumulation of naringenin and chlorogenic acid, and decreased the coumaric acid level. Selenium also affected the volatile organic compound profile, with changes in the level of specific apocarotenoid compounds, such as ß-ionone. These metabolomic changes may, to some extent, be due to the impact of selenium treatment on the transcription of genes involved in the metabolism of these compounds. RNA-seq analysis showed that the selenium application mostly impacted the expression of the genes involved in hormonal signaling, secondary metabolism, flavonoid biosynthesis, and glycosaminoglycan degradation.

From adenoma to CRC stages: the oral-gut microbiome axis as a source of potential microbial and metabolic biomarkers of malignancy.

BACKGROUND: Approximately 95% of Colorectal cancers (CRC) consist of adenocarcinomas originating from colonic Adenomatous polyps (AP). Increasing importance in CRC occurrence and progression has been attributed to the gut microbiota; however, a huge proportion of microorganisms inhabit the human digestive system. So, to comprehensively study the microbial spatial variations and their role in CRC progression, from AP to the different CRC phases, a holistic vision is imperative, including the simultaneous evaluation of multiple niches from the gastrointestinal system. Through an integrated approach, we identified potential microbial and metabolic biomarkers, able to discriminate human CRC from AP and/or also the different Tumor node metastasis (TNM) staging. In addition, as the microbiota contributes to the production of essential metabolic products detectable in fecal samples, we analysed and compared metabolites obtained from CRC and AP patients by using a Nuclear magnetic resonance (NMR) approach. METHODS: In this observational study, saliva, tissue and stool samples from 61 patients, have been collected, including 46 CRC and 15 AP patients, age and sex-matched, undergoing surgery in 2018 at the Careggi University Hospital (Florence, Italy). First, the microbiota in the three-district between CRC and AP patients has been characterized, as well as in different CRC TNM stages. Subsequently, proton NMR spectroscopy has been used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profile of a restricted group of CRC and AP patients. RESULTS: CRC patients display a different profile of tissue and fecal microbiota with respect to AP patients. Significant differences have been observed in CRC tissue microbial clades, with a rise of the Fusobacterium genus. In addition, significant taxa increase at the genus level has been observed in stool samples of CRC patients. Furthermore, Fusobacterium found in intestinal tissue has been positively correlated with fecal Parvimonas, for the first time. Moreover, as predicted by metagenomics pathway analysis, a significant increase of lactate (p=0.037) has been observed in the CRC fecal metabolic profiles, and positively correlated with Bifidobacterium (p=0.036). Finally, minor bacterial differences in CRC patients at stage T2 (TNM classification) have been detected, with a raise of the Spirochaetota phylum in CRC samples, with a slight increase of the Alphaproteobacteria class in fecal samples. CONCLUSION: Our results suggest the importance of microbiota communities and oncometabolites in CRC development. Further studies on CRC/AP management with a focus on CRC assessment are needed to investigate novel microbial-related diagnostic tools aimed to improve therapeutic interventions.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments.

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using 1H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the 1H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

Profiling metabolites and lipoproteins in COMETA, an Italian cohort of COVID-19 patients.

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.

Metabolite and lipoprotein profiles reveal sex-related oxidative stress imbalance in de novo drug-naive Parkinson's disease patients.

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.

Lipid and metabolite correlation networks specific to clinical and biochemical covariate show differences associated with sexual dimorphism in a cohort of nonagenarians.

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.

Fecal metabolomic profiles: A comparative study of patients with colorectal cancer vs adenomatous polyps.

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.

Metabolomics of gingival crevicular fluid to identify biomarkers for periodontitis: A systematic review with meta-analysis.

The present systematic review aimed to examine periodontitis-specific biomarkers in the gingival crevicular fluid (GCF) that could have a diagnostic relevance, and to provide a qualitative assessment of the current literature. Metabolites are reliable indicators of pathophysiological statuses, and their quantification in the GCF can provide an outlook of the changes associated with periodontitis and have diagnostic value. Relevant studies identified from PubMed, Embase, Cochrane Library, and Scopus databases were examined to answer the following PECO question: "In systemically healthy individuals, can concentration of specific metabolites in the GCF be used to discriminate subjects with healthy periodontium (H) or gingivitis from patients with periodontitis (P) and which is the diagnostic accuracy?" Quality of included studies was rated using a modified version of the QUADOMICS tool. Meta-analysis was conducted whenever possible. After the screening of 1,554 titles, 15 studies were selected, with sample size ranging from 30 to 93 subjects. Eleven studies performed targeted metabolomics analysis and provided data for 10 metabolites. Among the most consistent markers, malondialdehyde levels were found higher in the P group compared with H group (SMD = 2.86; 95% CI: 1.64, 4.08). Also, a significant increase of 8-hydroxy-deoxyguanosine, 4-hydroxynonenal, and neopterin was detected in periodontally diseased sites, while glutathione showed an inverse trend. When considering data from untargeted metabolomic analysis in four studies, more than 40 metabolites were found significantly discriminant, mainly related to amino acids and lipids degradation pathways. Notably, only one study reported measures of diagnostic accuracy. Several metabolites were differentially expressed in GCF of subjects across different periodontal conditions, having a major potential for investigating periodontal pathophysiology and for site-specific diagnosis. Oxidative stress-related molecules, such as malondialdehyde and 8-hydroxy-deoxyguanosine, were the most consistently associated to periodontitis (PROSPERO CRD42020188482).

Metabolomic/lipidomic profiling of COVID-19 and individual response to tocilizumab.

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.

A geroscience approach for Parkinson's disease: Conceptual framework and design of PROPAG-AGEING project.

Advanced age is the major risk factor for idiopathic Parkinson's disease (PD), but to date the biological relationship between PD and ageing remains elusive. Here we describe the rationale and the design of the H2020 funded project "PROPAG-AGEING", whose aim is to characterize the contribution of the ageing process to PD development. We summarize current evidences that support the existence of a continuum between ageing and PD and justify the use of a Geroscience approach to study PD. We focus in particular on the role of inflammaging, the chronic, low-grade inflammation characteristic of elderly physiology, which can propagate and transmit both locally and systemically. We then describe PROPAG-AGEING design, which is based on the multi-omic characterization of peripheral samples from clinically characterized drug-naïve and advanced PD, PD discordant twins, healthy controls and "super-controls", i.e. centenarians, who never showed clinical signs of motor disability, and their offspring. Omic results are then validated in a large number of samples, including in vitro models of dopaminergic neurons and healthy siblings of PD patients, who are at higher risk of developing PD, with the final aim of identifying the molecular perturbations that can deviate the trajectories of healthy ageing towards PD development.

Changes in the Salivary Metabolic Profile of Generalized Periodontitis Patients after Non-surgical Periodontal Therapy: A Metabolomic Analysis Using Nuclear Magnetic Resonance Spectroscopy.

Pattern analysis of the salivary metabolic profile has been proven accurate in discriminating between generalized periodontitis (GP) patients and healthy individuals (HI), as this disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data from previous studies, this study aimed to evaluate if non-surgical periodontal therapy (NST) could affect the metabolomic profile in GP patients' saliva and if it compares to that of HI. Unstimulated salivary samples were collected from 11 HI and 12 GP patients before and 3 months after NST. Nuclear Magnetic Resonance (NMR) spectroscopy, followed by a supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis, were performed to obtain metabolic profiles. In the GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in distinguishing between metabolic profiles of GP patients before and after NST. Orthogonal projection to latent structure was able to discriminate between the three groups of subjects with an accuracy of 85.6%. However, the post-NST metabolic profile of GP patients could not be completely assimilated to that of HI. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

Metabolomics profile in gastrointestinal cancers: Update and future perspectives.

Despite recent progress in diagnosis and therapy, gastrointestinal (GI) cancers remain one of the most important causes of death with a poor prognosis due to late diagnosis. Serum tumor markers and detection of occult blood in the stool are the current tests used in the clinic of GI cancers; however, these tests are not useful as diagnostic screening since they have low specificity and low sensitivity. Considering that one of the hallmarks of cancer is dysregulated metabolism and metabolomics is an optimal approach to illustrate the metabolic mechanisms that belong to living systems, is now clear that this -omics could open a new way to study cancer. In the last years, nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for diseases' diagnosis nevertheless a few studies focus on the NMR capability to find new biomarkers for early diagnosis of GI cancers. For these reasons in this review, we will give an update on the status of NMR metabolomic studies for the diagnosis and development of GI cancers using biological fluids.

Nuclear Magnetic Resonance-Based Metabolomic Comparison of Breast Milk and Organic and Traditional Formula Milk Brands for Infants and Toddlers.

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 (1H 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.

The inner temperature of the olives (cv. Leccino) before processing affects the volatile profile and the composition of the oil.

The effects of pre-processing decreasing temperature (19, 15 and 10 °C) of olive fruit (cv. Leccino) harvested at three developmental stages (semi-ripe, ripe, advanced ripening) have been evaluated on oil in terms of basic quality parameters, composition, organoleptic traits, and aroma profiles. A total of 40 metabolites (volatiles and non-volatiles) were identified by 1H NMR and GC/MS analyses. Multivariate statistical analysis showed that samples obtained from ripe and advanced ripe olives cooled at 10 and 15 °C better correlated with C6 aldehydes, mainly associated with herbal/green olfactory traits. Compounds responsible for sweet/fruity traits were more abundantly present in oil extracted from 19 °C olive samples. Decreasing pulp temperature before crushing also resulted in reduced presence of 1-penten-3-ol, 1-penten-3-one, acetic acid and ethyl alcohol, associated with specific defects of the oil. Results indicate that slightly lowering fruit temperature just before crushing modulates oil composition by reducing oil off flavours while enhancing green and fresh attributes in particular when ripe olives are processed.

The metabolic fingerprints of HCV and HBV infections studied by Nuclear Magnetic Resonance Spectroscopy.

Few studies are available on metabolic changes in liver injuries and this is the first metabolomic study evaluating a group of HCV-positive patients, before and after viral eradication via DAA IFN-free regimens, using 1H-NMR to characterize and compare their serum fingerprints to naïve HBV-patients and healthy donors. The investigation clearly shows differences in the metabolomic profile of HCV patients before and after effective DAA treatment. Significant changes in metabolites levels in patients undergoing therapy suggest alterations in several metabolic pathways. It has been shown that 1H-NMR fingerprinting approach is an optimal technique in predicting the specific infection and the healthy status of studied subjects (Monte-Carlo cross validated accuracies: 86% in the HCV vs HBV model, 98.7% in the HCV vs HC model). Metabolite data collected support the hypothesis that the HCV virus induces glycolysis over oxidative phosphorylation in a similar manner to the Warburg effect in cancer, moreover our results have demonstrated a different action of the two viruses on cellular metabolism, corroborating the hypothesis that the metabolic perturbation on patients could be attributed to a direct role in viral infection. This metabolomic study has revealed some alteration in metabolites for the first time (2-oxoglutarate and 3-hydroxybutrate) concerning the HCV-infection model that could explain several extrahepatic manifestations associated with such an infection.

High-Throughput Metabolomics by 1D NMR.

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Effect of non-surgical periodontal therapy on salivary metabolic fingerprint of generalized chronic periodontitis using nuclear magnetic resonance spectroscopy.

OBJECTIVE: Metabolomic analysis of saliva proved its accuracy in discriminating patients with generalized chronic periodontitis (GCP) from healthy subjects by identifying specific molecular signatures of the disease. There is lack of investigations concerning the effect of periodontal treatment on individual metabolic fingerprints. Therefore, the aim of this study was to determine whether non-surgical periodontal therapy could change salivary metabolomic profile in GCP to one more similar to periodontal health. DESIGN: Unstimulated whole saliva of 32 controls and 19 GCP patients were obtained prior to and 3 months after conventional staged non-surgical periodontal therapy. Metabolic profiling was performed using Nuclear Magnetic Resonance (NMR) spectroscopy, followed by univariate and multivariate paired approaches to assess the changes introduced by the therapy. RESULTS: In GCP group, periodontal treatment led to an improvement in all clinical parameters (p < 0.001). The accuracy of the multivariate model in discriminating the metabolomic profile of each GCP patient at two time points was 92.5%. Despite the almost perfect separation of the spectra in the metabolic space, the univariate analysis failed to identify significant variations in single metabolite content. The post-treatment metabolic profile of GCP patients could not be assimilated to that of healthy controls who exhibited different levels of lactate, pyruvate, valine, proline, tyrosine, and formate. CONCLUSIONS: Based on these data, NMR-spectroscopic analysis revealed that, despite significant changes in the overall metabolomic fingerprint after non-surgical therapy, GCP patients maintained a distinctive metabolic profile compared to healthy individuals.

NMR metabolomic fingerprinting distinguishes milk from different farms.

A fast and reproducible protocol for milk Nuclear Magnetic Resonance (NMR) metabolomic fingerprinting was developed, allowing for an accurate discrimination among milk samples from large-scale distribution, as well as among milk sample from different farms located in the same restricted geographical area. Seasonal variations in milk composition and correlations with cows' nutritional patterns are also assessed, underlining relationships between feeding and metabolites. The most important difference was related to the use of silage feeding. This finding is relevant to assess the suitability of milk for different dairy products. A prominent example is parmesan cheese, the preparation protocol of which excludes milk from silage-fed cows.

Analysis of salivary phenotypes of generalized aggressive and chronic periodontitis through nuclear magnetic resonance-based metabolomics.

BACKGROUND: Recent findings about the differential gene expression signature of periodontal lesions have raised the hypothesis of distinctive biological phenotypes expressed by generalized chronic periodontitis (GCP) and generalized aggressive periodontitis (GAgP) patients. Therefore, this cross-sectional investigation was planned, primarily, to determine the ability of nuclear magnetic resonance (NMR) spectroscopic analysis of unstimulated whole saliva to discriminate GCP and GAgP disease-specific metabolomic fingerprint and, secondarily, to assess potential metabolites discriminating periodontitis patients from periodontally healthy individuals (HI). METHODS: NMR-metabolomics spectra were acquired from salivary samples of patients with a clinical diagnosis of GCP (n = 33) or GAgP (n = 28) and from HI (n = 39). The clustering of HI, GCP, and GAgP patients was achieved by using a combination of the Principal Component Analysis and Canonical Correlation Analysis on the NMR profiles. RESULTS: These analyses revealed a significant predictive accuracy discriminating HI from GCP, and discriminating HI from GAgP patients (both 81%). In contrast, the GAgP and GCP saliva samples seem to belong to the same metabolic space (60% predictive accuracy). Significantly lower levels (P < 0.05) of pyruvate, N-acetyl groups and lactate and higher levels (P < 0.05) of proline, phenylalanine, and tyrosine were found in GCP and GAgP patients compared with HI. CONCLUSIONS: Within the limitations of this study, CGP and GAgP metabolomic profiles were not unequivocally discriminated through a NMR-based spectroscopic analysis of saliva.