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Studying the metabolome of specific gestational compartments is of growing interest in the context of fetus developmental disorders. However, the metabolomes of the placenta and amniotic fluid (AF) are poorly characterized. Therefore, we present the validation of a fingerprinting methodology. Using pregnant rats, we performed exhaustive and robust extractions of metabolites in the AF and lipids and more polar metabolites in the placenta. For the AF, we compared the extraction capabilities of methanol (MeOH), acetonitrile (ACN), and a mixture of both. For the placenta, we compared (i) the extraction capabilities of dichloromethane, methyl t -butyl ether (MTBE), and butanol, along with (ii) the impact of lyophilization of the placental tissue. Analyses were performed on a C18 and hydrophilic interaction liquid chromatography combined with high-resolution mass spectrometry. The efficiency and the robustness of the extractions were compared based on the number of the features or metabolites (for untargeted or targeted approach, respectively), their mean total intensity, and their coefficient of variation (% CV). The extraction capabilities of MeOH and ACN on the AF metabolome were equivalent. Lyophilization also had no significant impact and usefulness on the placental tissue metabolome profiling. Considering the placental lipidome, MTBE extraction was more informative because it allowed extraction of a slightly higher number of lipids, in higher concentration. This proof-of-concept study assessing the metabolomics and lipidomics of the AF and the placenta revealed changes in both metabolisms, at two different stages of rat gestation, and allowed a detailed prenatal metabolic fingerprinting.

Variations in muscle glycogen storage are highly correlated with variations in meat ultimate pH (pHu), a key factor for poultry meat quality. A total of two chicken lines were divergently selected on breast pHu to understand the biological basis for variations in meat quality (i.e., the pHu- and the pHu+ lines that are characterized by a 17% difference in muscle glycogen content). The effects of this selection on bird metabolism were investigated by quantifying muscle metabolites by high-resolution NMR ((1)H and (31)P) and serum metabolites by (1)H NMR. A total of 20 and 26 discriminating metabolites between the two lines were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) in the serum and muscle, respectively. There was over-representation of carbohydrate metabolites in the serum and muscle of the pHu- line, consistent with its high level of muscle glycogen. However, the pHu+ line was characterized by markers of oxidative stress and muscle catabolism, probably because of its low level of energy substrates. After OPLS-DA multiblock analysis, a metabolic set of 15 high-confidence biomarkers was identified that could be used to predict the quality of poultry meat after validation on an independent population.

The fatty acids composition of adipose tissue may provide information on the nutritional part of the risk or evolution of breast cancer. To determine whether (1)H NMR of adipose tissue provides information on the nature of the diet consumed, a dietary intervention with increasing percentage of polyunsaturated n-3 docosahexaenoic acid (DHA 22:6n-3, provided as DHASCO oil) was applied to a rat model of N-nitroso-N-methylurea-induced mammary tumors. Spectra of the lipid extracts were obtained from adipose tissues in five groups of Sprague-Dawley rats fed with a diet containing 7% peanut/rapeseed enriched with 8% (w/w) of an oil without (palm oil) or with low (1%), moderate (3%), or high (8%) DHASCO content. A control group received a basal diet with 15% peanut/rapeseed representative of the "Western" diet. After 5 months of those five controlled diets, adipose tissue was collected for analysis of the lipid extract using both (1)H NMR analysis on an 11.7 T spectrometer and gas chromatography considered as gold standard. (1)H NMR analysis showed a dose-dependent increase in DHA in the lipid extract of adipose tissues and a commensurate decrease in n-6 polyunsaturated fatty acids in the three DHA groups, which allowed one to follow n-6/n-3 ratio changes. The highest n-6/n-3 ratio was observed in the control Western diet group compared to the other diet groups. The integrated spectral regions showed separation between groups, thereby documenting a specific NMR lipid profile corresponding to each dietary intervention. Those diet-dependent NMR lipid profiles were consistent with that obtained with gas chromatography analyses of the same samples. This study is a proof of concept highlighting the potential use of the (1)H NMR approach to evaluate dietary intervention in biopsies of adipose tissues.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with no clinical biomarker. The aims of this study were to characterize a metabolic signature of ASD and to evaluate multiplatform analytical methodologies in order to develop predictive tools for diagnosis and disease follow-up. Urine samples were analyzed using (1)H and (1)H-(13)C NMR-based approaches and LC-HRMS-based approaches (ESI+ and ESI- on HILIC and C18 chromatography columns). Data tables obtained from the six analytical modalities on a training set of 46 urine samples (22 autistic children and 24 controls) were processed by multivariate analysis (orthogonal partial least-squares discriminant analysis, OPLS-DA). The predictions from each of these OPLS-DA models were then evaluated using a prediction set of 16 samples (8 autistic children and 8 controls) and receiver operating characteristic curves. Thereafter, a data fusion block-scaling OPLS-DA model was generated from the 6 best models obtained for each modality. This fused OPLS-DA model showed an enhanced performance (R(2)Y(cum) = 0.88, Q(2)(cum) = 0.75) compared to each analytical modality model, as well as a better predictive capacity (AUC = 0.91, p-value = 0.006). Metabolites that are most significantly different between autistic and control children (p < 0.05) are indoxyl sulfate, N-α-acetyl-l-arginine, methyl guanidine, and phenylacetylglutamine. This multimodality approach has the potential to contribute to find robust biomarkers and characterize a metabolic phenotype of the ASD population.

Amyotrophic lateral sclerosis (ALS) is characterized by the absence of reliable diagnostic biomarkers. The aim of the study was to (i) devise an untargeted metabolomics methodology that reliably compares cerebrospinal fluid (CSF) from ALS patients and controls by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS); (ii) ascertain a metabolic signature of ALS by use of the LC-HRMS platform; (iii) identify metabolites for use as diagnostic or pathophysiologic markers. We developed a method to analyze CSF components by UPLC coupled with a Q-Exactive mass spectrometer that uses electrospray ionization. Metabolomic profiles were created from the CSF obtained at diagnosis from ALS patients and patients with other neurological conditions. We performed multivariate analyses (OPLS-DA) and univariate analyses to assess the contribution of individual metabolites as well as compounds identified in other studies. Sixty-six CSF samples from ALS patients and 128 from controls were analyzed. Metabolome analysis correctly predicted the diagnosis of ALS in more than 80% of cases. OPLS-DA identified four features that discriminated diagnostic group (p < 0.004). Our data demonstrate that untargeted metabolomics with LC-HRMS is a robust procedure to generate a specific metabolic profile for ALS from CSF and could be an important aid to the development of biomarkers for the disease.