Serum metabolomics profiling by proton nuclear magnetic resonance spectrometry of the response to single oral macronutrient challenges in women with polycystic ovary syndrome (PCOS) compared with male and female controls.

BACKGROUND: The polycystic ovary syndrome (PCOS) is associated with insulin resistance, obesity and cardiometabolic comorbidities. We here challenged the hypothesis, using state-of-the-art proton nuclear magnetic resonance spectrometry (1H-NMRS) metabolomics profiling, that androgen excess in women induces a certain masculinization of postprandial metabolism that is modulated by obesity. MATERIALS AND METHODS: Participants were 53 Caucasian young adults, including 17 women with classic PCOS consisting of hyperandrogenism and ovulatory dysfunction, 17 non-hyperandrogenic women presenting with regular menses, and 19 healthy men, selected to be similar in terms of age and body mass index (BMI). Half of the subjects had obesity. Patients were submitted to isocaloric separate glucose, lipid and protein oral challenges in alternate days and fasting and postprandial serum samples were submitted to 1H-NMRS metabolomics profiling for quantification of 36 low-molecular-weight polar metabolites. RESULTS: The largest postprandial changes were observed after glucose and protein intake, with lipid ingestion inducing smaller differences. Changes after glucose intake consisted of a marked increase in carbohydrates and byproducts of glycolysis, and an overall decrease in byproducts of proteolysis, lipolysis and ketogenesis. After the protein load, most amino acids and derivatives increased markedly, in parallel to an increase in pyruvate and a decrease in 3-hydroxybutyric acid and glycerol. Obesity increased ß- and D-glucose and pyruvate levels, with this effect being observed mostly after glucose ingestion in women with PCOS. Regardless of the type of macronutrient, men presented increased lysine and decreased 3-hydroxybutyric acid. In addition, non-obese men showed increased postprandial ß-glucose and decreased pyroglutamic acid, compared with non-obese control women. We observed a common pattern of postprandial changes in branched-chain and aromatic amino acids, where men showed greater amino acids increases after protein intake than control women and patients with PCOS but only within the non-obese participants. Conversely, this increase was blunted in obese men but not in obese women, who even presented a larger increase in some amino acids compared with their non-obese counterparts. Interestingly, regardless of the type of macronutrient, only obese women with PCOS showed increased leucine, lysine, phenylalanine and tryptophan levels compared with non-obese patients. CONCLUSIONS: Serum 1H-NMRS metabolomics profiling indicated sexual dimorphism in the responses to oral macronutrient challenges, which were apparently driven by the central role of postprandial insulin effects with obesity, and to a lesser extent PCOS, exerting modifying roles derived from insulin resistance. Hence, obesity impaired metabolic flexibility in young adults, yet sex and sex hormones also influenced the regulation of postprandial metabolism.

The polycystic ovary syndrome (PCOS) is a common endocrine disorder in women. PCOS is associated with diabetes, obesity and cardiometabolic disease. Mild excess of androgens (male hormones) characterize PCOS, and facilitate that body fat accumulates in the visceral abdominal area. Visceral fat promotes insulin resistance increasing the risk for diabetes and cardiometabolic disease, and further androgen excess. We here explored intermediate metabolism after the separate administration of either carbohydrates, fats or proteins, in young adult women with or without PCOS and in men, using state-of-the-art proton nuclear magnetic resonance metabolomics profiling. Results suggest that postprandial metabolomics profiles reflect mostly insulin actions, with changes derived from insulin resistance being more important with obesity but also being influenced by male sex and PCOS in women.

Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses.

Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.

Recent Advances in Batteryless NFC Sensors for Chemical Sensing and Biosensing.

This article reviews the recent advances in the field of batteryless near-field communication (NFC) sensors for chemical sensing and biosensing. The commercial availability of low-cost commercial NFC integrated circuits (ICs) and their massive integration in smartphones, used as readers and cloud interfaces, have aroused great interest in new batteryless NFC sensors. The fact that coil antennas are not importantly affected by the body compared with other wireless sensors based on far-field communications makes this technology suitable for future wearable point-of-care testing (PoCT) devices. This review first compares energy harvesting based on NFC to other energy-harvesting technologies. Next, some practical recommendations for designing and tuning NFC-based tags are described. Power transfer is key because in most cases, the energy harvested has to be stable for several seconds and not contaminated by undesired signals. For this reason, the effect of the dimensions of the coils and the conductivity on the wireless power transfer is thoroughly discussed. In the last part of the review, the state of the art in NFC-based chemical and biosensors is presented. NFC-based tags (or sensor tags) are mainly based on commercial or custom NFC ICs, which are used to harvest the energy from the RF field generated by the smartphone to power the electronics. Low-consumption colorimeters and potentiostats can be integrated into these NFC tags, opening the door to the integration of chemical sensors and biosensors, which can be harvested and read from a smartphone. The smartphone is also used to upload the acquired information to the cloud to facilitate the internet of medical things (IoMT) paradigm. Finally, several chipless sensors recently proposed in the literature as a low-cost alternative for chemical applications are discussed.

Serum metabolomics profiling by proton nuclear magnetic resonance spectroscopy reveals sexual dimorphism and masculinization of intermediate metabolism in women with polycystic ovary syndrome (PCOS).

BACKGROUND: The polycystic ovary syndrome (PCOS) is associated with insulin resistance, obesity and cardiometabolic comorbidities. We here challenged the hypothesis, using state-of-the art proton nuclear magnetic resonance spectroscopy metabolomics profiling, that androgen excess in women induces also a certain masculinization of intermediate metabolism that is modulated by obesity. METHODS: Participants were 53 Caucasian young adults, including 17 women with classic PCOS consisting of hyperandrogenism and ovulatory dysfunction, 17 non-hyperandrogenic women presenting with regular menses, and 19 healthy men, selected in order to be similar in terms of age and body mass index (BMI). Half of the subjects had obesity defined by a body mass index ≥ 30 kg/m2. Subjects maintained the same diet unrestricted in carbohydrates for 3 days before sampling and maintained their lifestyle and exercise patterns prior and during the study. Plasma samples were submitted to proton nuclear magnetic resonance spectroscopy metabolomics profiling. RESULTS: Obesity associated a metabolomics profile mainly characterized by increased branched chain and aromatic aminoacids. Regardless of obesity, this unfavorable profile also characterized men as compared with control women, and was shared by women with PCOS. Notably, the negative impact of obesity on metabolomics profile was restricted to women, with obese men showing no further deterioration when compared with their non-obese counterparts. CONCLUSIONS: Serum metabolomics profiling by proton nuclear magnetic resonance spectroscopy reveals sexual dimorphism, and masculinization of intermediate metabolism in women with PCOS, further suggesting a role for sex and sex hormones in the regulation of intermediate metabolism.

Battery-Less NFC Potentiostat for Electrochemical Point-of-Care Sensors Based on COTS Components.

This work studies the feasibility of using a battery-less Near-Field Communication (NFC) potentiostat for the next generation of electrochemical point-of-care sensors. A design based on an NFC microchip, a microcontroller, and a custom potentiostat based on an operational amplifier is presented. A proof-of-concept prototype has been designed and used to quantify glucose concentration using commercial glucose test strips from chronoamperometry measurements. The device is harvested and the sensor is read using a mobile phone. The prototype uses an antenna loop covered with ferrite sheets to ensure stable operation of the electronics when the mobile phone is used as reader. The use of ferrite reduces the detuning caused by the proximity of the metal parts of the mobile phone. A comparison with a commercial glucometer device is provided. Results obtained using a commercial glucometer and those provided by the proposed potentiostat show an excellent agreement.

Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling.

The quality of automatic metabolite profiling in NMR datasets from complex matrices can be affected by the numerous sources of variability. These sources, as well as the presence of multiple low-intensity signals, cause uncertainty in the metabolite signal parameters. Lineshape fitting approaches often produce suboptimal resolutions to adapt them in a complex spectrum lineshape. As a result, the use of software tools for automatic profiling tends to be restricted to specific biological matrices and/or sample preparation protocols to obtain reliable results. However, the analysis and modelling of the signal parameters collected during initial iteration can be further optimized to reduce uncertainty by generating narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted, and the performance of automatic profiling can be maximized. Our proposed workflow can learn and model the sample properties; therefore, restrictions in the biological matrix, or sample preparation protocol, and limitations of lineshape fitting approaches can be overcome.

Paired maternal and fetal metabolomics reveal a differential fingerprint in preeclampsia versus fetal growth restriction.

Preeclampsia (PE) and fetal growth restriction (FGR) are both placenta-mediated disorders with unclear pathogenesis. Metabolomics of maternal and fetal pairs might help in understanding these disorders. We recruited prospectively pregnancies with normotensive FGR, PE without FGR, PE + FGR and uncomplicated pregnancies as controls. Nuclear magnetic resonance metabolomics were applied on plasma samples collected at delivery. Advanced lipoprotein, glycoprotein and choline profiling was performed using the Liposcale test. The software package Dolphin was used to quantify 24 low-molecular-weight metabolites. Statistical analysis comprised the comparison between each group of complicated pregnancies versus controls, considering 5% false discovery rate correction. Lipid profiles were altered in accordance with the clinical presentation of these disorders. Specifically, PE mothers and FGR fetuses (with or without FGR or PE, respectively) exhibited a pro-atherogenic and pro-inflammatory profile, with higher concentrations of triglycerides, remnant cholesterol (VLDL, IDL) and Glc/GalNAc-linked and lipid-associated glycoproteins compared to controls. Low-molecular-weight metabolites were extensively disturbed in preeclamptic mothers, with or without FGR. Growth restricted fetuses in the presence of PE showed changes in low-molecular-weight metabolites similar to their mothers (increased creatine and creatinine), while normotensive FGR fetuses presented scarce differences, consistent with undernutrition (lower isoleucine). Further research is warranted to clarify maternal and fetal adaptations to PE and FGR.

Changes in Plasma Metabolite Concentrations after a Low-Glycemic Index Diet Intervention.

SCOPE: To examine whether a low-glycemic index (LGI) diet improves a set of plasma metabolites related to different metabolic diseases, and comparison to a high-glycemic index (HGI) diet and a low-fat (LF) diet. METHODS AND RESULTS: A parallel, randomized trial with three intervention diets: an LGI diet, an HGI diet, and an LF diet. A total of 122 adult overweight and obese subjects were enrolled in the study for 6 months. Blood samples were collected at baseline and at the end of the intervention. The plasma metabolomic profile of 102 subjects was analyzed using three different approaches: GC/quadrupole-TOF, LC/quadrupole-TOF, and nuclear magnetic resonance. Both univariate and multivariate analysis were performed. Serine levels were significantly higher following the LGI diet compared to both the HGI and LF diets (q = 0.002), whereas leucine (q = 0.015) and valine (q = 0.024) were lower in the LGI diet compared to the LF diet. A set of two sphingomyelins, two lysophosphatidylcholines, and six phosphatidylcholines were significantly modulated after the LGI diet compared to the HGI and LF diets (q < 0.05). Significant correlations between changes in plasma amino acids and lipid species with changes in body weight, glucose, insulin, and some inflammatory markers are also reported. CONCLUSION: These results suggest that an LGI diet modulates certain circulating amino acids and lipid levels. These findings may explain the health benefits attributed to LGI diets in metabolic diseases such as type 2 diabetes.

Metabolic profiling and targeted lipidomics reveals a disturbed lipid profile in mothers and fetuses with intrauterine growth restriction.

Fetal growth may be impaired by poor placental function or maternal conditions, each of which can influence the transfer of nutrients and oxygen from the mother to the developing fetus. Large-scale studies of metabolites (metabolomics) are key to understand cellular metabolism and pathophysiology of human conditions. Herein, maternal and cord blood plasma samples were used for NMR-based metabolic fingerprinting and profiling, including analysis of the enrichment of circulating lipid classes and subclasses, as well as the number of sub-fraction particles and their size. Changes in phosphatidylcholines and glycoproteins were prominent in growth-restricted fetuses indicating significant alterations in their abundance and biophysical properties. Lipoprotein profiles showed significantly lower plasma concentrations of cholesterol-intermediate density lipoprotein (IDL), triglycerides-IDL and high-density lipoprotein (HDL) in mothers of growth-restricted fetuses compared to controls (p < 0.05). In contrast, growth-restricted fetuses had significantly higher plasma concentrations of cholesterol and triglycerides transporting lipoproteins [LDL, IDL, and VLDL, (p < 0.005; all)], as well as increased VLDL particle types (large, medium and small). Significant changes in plasma concentrations of formate, histidine, isoleucine and citrate in growth-restricted fetuses were also observed. Comprehensive metabolic profiling reveals that both, mother and fetuses of pregnancies complicated with fetal growth restriction have a substantial disruption in lipid metabolism.

Improving sample classification by harnessing the potential of 1H-NMR signal chemical shifts.

NMR spectroscopy is a technology that is widely used in metabolomic studies. The information that these studies most commonly use from NMR spectra is the metabolite concentration. However, as well as concentration, pH and ionic strength information are also made available by the chemical shift of metabolite signals. This information is typically not used even though it can enhance sample discrimination, since many conditions show pH or ionic imbalance. Here, we demonstrate how chemical shift information can be used to improve the quality of the discrimination between case and control samples in three public datasets of different human matrices. In two of these datasets, chemical shift information helped to provide an AUROC value higher than 0.9 during sample classification. In the other dataset, the chemical shift also showed discriminant potential (AUROC 0.831). These results are consistent with the pH imbalance characteristic of the condition studied in the datasets. In addition, we show that this signal misalignment dependent on sample class can alter the results of fingerprinting approaches in the three datasets. Our results show that it is possible to use chemical shift information to enhance the diagnostic and predictive properties of NMR.

Metabolomic signature of the postprandial experience.

BACKGROUND: Ingestion of a meal up to maximal tolerance induces unpleasant fullness sensation and changes in circulating metabolites. Our aim was to evaluate the relation between postprandial sensations and the metabolomic responses to a comfort meal. METHODS: In 32 non-obese healthy men, homeostatic sensations (hunger/satiety, fullness), hedonic sensations (digestive well-being, mood), and the metabolomic profile in plasma (low-molecular weight metabolites and lipoprotein profiles) were measured before and 20 minutes after a comfort meal (warm ham and cheese sandwich and juice; total 300 mL; 425 kcal). Perception was measured on 10 cm scales and the metabolomic response by nuclear magnetic resonance spectroscopy. KEY RESULTS: The comfort meal induced homeostatic sensations (satiety and fullness) associated with a positive hedonic reward (enhanced digestive well-being and mood) and a clear change in the metabolomic profile with a sharp discrimination between the pre and postprandial state by a non-supervised principal component analysis. The change in circulating metabolites correlated with the postprandial sensations: the increase in alanine correlated with the increase in fullness (R = 0.50; P = 0.004) and well-being (R = 0.50; P = 0.004); the increase in glucose correlated with the sensation of fullness (R = 0.40; P = 0.023) and enhanced mood (R = 0.41; P = 0.020). CONCLUSION AND INFERENCES: Metabolomic changes in the response to a meal may provide an objective index of the postprandial experience, which may have clinical implications in the management of patients with poor meal tolerance or meal-related symptoms.

Effects of Prebiotics vs a Diet Low in FODMAPs in Patients With Functional Gut Disorders.

Prebiotics and diets low in fermentable oligo-, di-, mono-saccharides and polyols (low-FODMAP diet) might reduce symptoms in patients with functional gastrointestinal disorders, despite reports that some nonabsorbable, fermentable meal products (prebiotics) provide substrates for colonic bacteria and thereby increase gas production. We performed a randomized, parallel, double-blind study of patients with functional gastrointestinal disorders with flatulence. We compared the effects of a prebiotic supplement (2.8 g/d Bimuno containing 1.37 g beta-galactooligosaccharide) plus a placebo (Mediterranean-type diet (prebiotic group, n = 19) vs a placebo supplement (2.8 g xylose) plus a diet low in FODMAP (low-FODMAP group, n = 21) for 4 weeks; patients were then followed for 2 weeks. The primary outcome was effects on composition of the fecal microbiota, analyzed by 16S sequencing. Secondary outcomes were intestinal gas production and digestive sensations. After 4 weeks, we observed opposite effects on microbiota in each group, particularly in relation to the abundance of Bifidobacterium sequences (increase in the prebiotic group and decrease in the low-FODMAP group; P = .042), and Bilophila wadsworthia (decrease in the prebiotic group and increase in the low-FODMAP group; P = .050). After 4 weeks, both groups had statistically significant reductions in all symptom scores, except reductions in flatulence and borborygmi were not significant in the prebiotic group. Although the decrease in symptoms persisted for 2 weeks after patients discontinued prebiotic supplementation, symptoms reappeared immediately after patients discontinued the low-FODMAP diet. Intermittent prebiotic administration might therefore be an alternative to dietary restrictions for patients with functional gut symptoms. ClinicalTrials.gov no.: NCT02210572.

rDolphin: a GUI R package for proficient automatic profiling of 1D 1H-NMR spectra of study datasets.

INTRODUCTION: Adoption of automatic profiling tools for 1H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices. OBJECTIVES: To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process. METHODS: rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality. RESULTS: The information and quality achieved in two public datasets of complex matrices are maximized. CONCLUSION: rDolphin is an open-source R package ( http://github.com/danielcanueto/rDolphin ) able to provide the best balance between accuracy, reproducibility and ease of use.

Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for biological applications.

Mass spectrometry imaging (MSI) is a label-free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach. MSI data processing is challenging due to several factors. The large volume of mass spectra involved in a MSI experiment makes choosing the correct computational strategies critical. Furthermore, pixel to pixel variation inherent in the technique makes choosing the correct preprocessing steps critical. The primary aim of this review was to provide an overview of the data-processing steps and tools that can be applied to an MSI experiment, from preprocessing the raw data to the more advanced strategies for image visualization and segmentation. This review is particularly aimed at researchers performing MSI experiments and who are interested in incorporating new data-processing features, improving their computational strategy, and/or desire access to data-processing tools currently available. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:281-306, 2018.

A baseline metabolomic signature is associated with immunological CD4+ T-cell recovery after 36 months of antiretroviral therapy in HIV-infected patients.

OBJECTIVES: Poor immunological recovery in treated HIV-infected patients is associated with greater morbidity and mortality. To date, predictive biomarkers of this incomplete immune reconstitution have not been established. We aimed to identify a baseline metabolomic signature associated with a poor immunological recovery after antiretroviral therapy (ART) to envisage the underlying mechanistic pathways that influence the treatment response. DESIGN: This was a multicentre, prospective cohort study in ART-naive and a pre-ART low nadir (<200 cells/µl) HIV-infected patients (n = 64). METHODS: We obtained clinical data and metabolomic profiles for each individual, in which low molecular weight metabolites, lipids and lipoproteins (including particle concentrations and sizes) were measured by NMR spectroscopy. Immunological recovery was defined as reaching CD4 T-cell count at least 250 cells/µl after 36 months of virologically successful ART. We used univariate comparisons, Random Forest test and receiver-operating characteristic curves to identify and evaluate the predictive factors of immunological recovery after treatment. RESULTS: HIV-infected patients with a baseline metabolic pattern characterized by high levels of large high density lipoprotein (HDL) particles, HDL cholesterol and larger sizes of low density lipoprotein particles had a better immunological recovery after treatment. Conversely, patients with high ratios of non-HDL lipoprotein particles did not experience this full recovery. Medium very-low-density lipoprotein particles and glucose increased the classification power of the multivariate model despite not showing any significant differences between the two groups. CONCLUSION: In HIV-infected patients, a baseline healthier metabolomic profile is related to a better response to ART where the lipoprotein profile, mainly large HDL particles, may play a key role.

Effect of pistachio consumption on the modulation of urinary gut microbiota-related metabolites in prediabetic subjects.

The specific nutritional composition of nuts could affect different metabolic pathways involved in a broad range of metabolic diseases. We therefore investigated whether chronic consumption of pistachio nuts modifies the urine metabolome in prediabetic subjects. We designed a randomized crossover clinical trial in 39 prediabetic subjects. They consumed a pistachio-supplemented diet (PD, 50% carbohydrates, 33% fat, including 57 g/d of pistachios daily) and a control diet (CD, 55% carbohydrates, 30% fat) for 4 months each, separated by a 2-week wash-out. Nuclear magnetic resonance (NRM) was performed to determine changes in 24-h urine metabolites. Significant changes in urine metabolites according to the different intervention periods were found in uni- and multivariate analysis. Score plot of the first two components of the multilevel partial least squares discriminant analysis (ML-PLS-DA) showed a clear separation of the intervention periods. Three metabolites related with gut microbiota metabolism (i.e., hippurate, p-cresol sulfate and dimethylamine) were found decreased in PD compared with CD (P<.05). Moreover, cis-aconitate [intermediate of the tricarboxylic acid (TCA)] was also found decreased following PD compared with CD. Intragroup analysis showed that creatinine levels were significantly increased in PD (P=.023), whereas trimethylamine N-oxide (TMAO) was found significantly reduced following PD (P=.034). Our results suggest that chronic pistachio consumption may modulate some urinary metabolites related to gut microbiota metabolism and the TCA cycle; all associated with metabolic derangements associated with insulin resistance and Type 2 diabetes.

Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation.

Metabolomics GC-MS samples involve high complexity data that must be effectively resolved to produce chemically meaningful results. Multivariate curve resolution-alternating least squares (MCR-ALS) is the most frequently reported technique for that purpose. More recently, independent component analysis (ICA) has been reported as an alternative to MCR. Those algorithms attempt to infer a model describing the observed data and, therefore, the least squares regression used in MCR assumes that the data is a linear combination of that model. However, due to the high complexity of real data, the construction of a model to describe optimally the observed data is a critical step and these algorithms should prevent the influence from outlier data. This study proves independent component regression (ICR) as an alternative for GC-MS compound identification. Both ICR and MCR though require least squares regression to correctly resolve the mixtures. In this paper, a novel orthogonal signal deconvolution (OSD) approach is introduced, which uses principal component analysis to determine the compound spectra. The study includes a compound identification comparison between the results by ICA-OSD, MCR-OSD, ICR and MCR-ALS using pure standards and human serum samples. Results shows that ICR may be used as an alternative to multivariate curve methods, as ICR efficiency is comparable to MCR-ALS. Also, the study demonstrates that the proposed OSD approach achieves greater spectral resolution accuracy than the traditional least squares approach when compounds elute under undue interference of biological matrices.

Dolphin: a tool for automatic targeted metabolite profiling using 1D and 2D (1)H-NMR data.

One of the main challenges in nuclear magnetic resonance (NMR) metabolomics is to obtain valuable metabolic information from large datasets of raw NMR spectra in a high throughput, automatic, and reproducible way. To date, established software packages used to match and quantify metabolites in NMR spectra remain mostly manually operated, leading to low resolution results and subject to inconsistencies not attributable to the NMR technique itself. Here, we introduce a new software package, called Dolphin, able to automatically quantify a set of target metabolites in multiple sample measurements using an approach based on 1D and 2D NMR techniques to overcome the inherent limitations of 1D (1)H-NMR spectra in metabolomics. Dolphin takes advantage of the 2D J-resolved NMR spectroscopy signal dispersion to avoid inconsistencies in signal position detection, enhancing the reliability and confidence in metabolite matching. Furthermore, in order to improve accuracy in quantification, Dolphin uses 2D NMR spectra to obtain additional information on all neighboring signals surrounding the target metabolite. We have compared the targeted profiling results of Dolphin, recorded from standard biological mixtures, with those of two well established approaches in NMR metabolomics. Overall, Dolphin produced more accurate results with the added advantage of being a fully automated and high throughput processing package.

Nutri-metabolomics: subtle serum metabolic differences in healthy subjects by NMR-based metabolomics after a short-term nutritional intervention with two tomato sauces.

Postgenomics research and development is witnessing novel intersections of omics data intensive technology and applications in health and personalized nutrition. Chief among these is the nascent field of nutri-metabolomics that harnesses metabolomics platforms to discern person-to-person variations in nutritional responses. To this end, differences in the origin and ripening stage of fruits might have a strong impact on their phytochemical composition, and consequently, on their potential nutri-metabolomics effects on health. The objective of the present study was to evaluate the effects of a 4-week cross-over nutritional intervention on the metabolic status of 24 young healthy subjects. The intervention was carried out with two tomato sauces differing in their natural lycopene content, which was achieved by using tomatoes harvested at different times. Blood samples were drawn from each subject before and after each intervention period. Aqueous and lipid extracts from serum samples were analyzed by 1H-NMR metabolic profiling combined with analysis of variance simultaneous component analysis (ASCA) and multilevel simultaneous component analysis (MSCA). These methods allowed the interpretation of the variation induced by the main factors of the study design (sauce treatment and time). The levels of creatine, creatinine, leucine, choline, methionine, and acetate in aqueous extracts were increased after the intervention with the high-lycopene content sauce, while those of ascorbic acid, lactate, pyruvate, isoleucine, alanine were increased after the normal-lycopene content sauce. In conclusion, NMR-based metabolomics of aqueous and lipid extracts allowed the detection of different metabolic changes after the nutritional intervention. This outcome might partly be due to the different ripening state of the fruits used in production of the tomato sauces. The findings presented herein collectively attest to the emergence of the field of nutri-metabolomics as a novel subspecialty of postgenomics integrative biology.

Metabolomic assessment of the effect of dietary cholesterol in the progressive development of fatty liver disease.

Nonalcoholic fatty liver disease is considered to be the hepatic manifestation of metabolic syndrome and is usually related to high-fat, high-cholesterol diets. With the rationale that the identification and quantification of metabolites in different metabolic pathways may facilitate the discovery of clinically accessible biomarkers, we report the use of (1)H NMR metabolomics for quantitative profiling of liver extracts from LDLr(-/-) mice, a well-documented mouse model of fatty liver disease. A total of 55 metabolites were identified, and multivariate analyses in a diet- and time-comparative strategy were performed. Dietary cholesterol increased the hepatic concentrations of cholesterol, triglycerides, and oleic acid but also decreased the [PUFA/MUFA] ratio as well as the relative amount of long-chain polyunsaturated fatty acids in the liver. This was also accompanied by variations of the hepatic concentration of taurine, glutathione, methionine, and carnitine. Heat-map correlation analyses demonstrated that hepatic inflammation and development of steatosis correlated with cholesterol and triglyceride NMR derived signals, respectively. We conclude that dietary cholesterol is a causal factor in the development of both liver steatosis and hepatic inflammation.