A combination of lipidomics, MS imaging, and PET scan imaging reveals differences in cerebral activity in rat pups according to the lipid quality of infant formulas.

We evaluated the effect of adding docosahexaenoic:arachidonic acids (3:2) (DHA+ARA) to 2 representative commercial infant formulas on brain activity and brain and eye lipids in an artificially reared rat pup model. The formula lipid background was either a pure plant oil blend, or dairy fat with a plant oil blend (1:1). Results at weaning were compared to breast milk-fed pups. Brain functional activity was determined by positron emission tomography scan imaging, the brain and eye fatty acid and lipid composition by targeted and untargeted lipidomics, and DHA brain regional location by mass-spectrometry imaging. The brain functional activity was normalized to controls with DHA+ARA added to the formulas. DHA in both brain and eyes was influenced by formula intake, but more than two-thirds of tissue DHA-glycerolipids remained insensitive to the dietary challenge. However, the DHA lipidome correlated better with brain function than sole DHA content ( r = 0.70 vs. r = 0.48; P < 0.05). Brain DHA regional distribution was more affected by the formula lipid background than the provision of PUFAs. Adding DHA+ARA to formulas alters the DHA content and lipidome of nervous tissue in the neonate, making it closer to dam milk-fed controls, and normalizes brain functional activity.-Aidoud, N., Delplanque, B., Baudry, C., Garcia, C., Moyon, A., Balasse, L., Guillet, B., Antona, C., Darmaun, D., Fraser, K., Ndiaye, S., Leruyet, P., Martin, J.-C. A combination of lipidomics, MS imaging, and PET scan imaging reveals differences in cerebral activity in rat pups according to the lipid quality of infant formulas.

Combined 1H-NMR and 1H-13C HSQC-NMR to improve urinary screening in autism spectrum disorders.

Autism spectrum disorders (ASD) are neurodevelopmental diseases with complex genetic and environmental etiological factors. Although genetic causes play a significant part in the etiology of ASD, metabolic disturbances may also play a causal role or modulate the clinical features of ASD. The number of ASD studies involving metabolomics is increasing, and sometime with conflicting findings. We assessed the metabolomics profiling of urine samples to determine a comprehensive biochemical signature of ASD. Furthermore, to date no study has combined metabolic profiles obtained from different analytical techniques to distinguish patient with ASD from healthy individuals. We obtained (1)H-NMR spectra and 2D (1)H-(13)C HSQC NMR spectra from urine samples of patients with ASD or healthy controls. We analyzed these spectra by multivariate statistical data analysis. The OPLS-DA model obtained from (1)H NMR spectra showed a good discrimination between ASD samples and non-ASD samples (R(2)Y(cum) = 0.70 and Q(2) = 0.51). Combining the (1)H NMR spectra and the 2D (1)H-(13)C HSQC NMR spectra increased the overall quality and predictive value of the OPLS-DA model (R(2)Y(cum) = 0.84 and Q(2) = 0.71), leading to a better sensitivity and specificity. Urinary excretion of succinate, glutamate and 3-methyl-histidine differed significantly between ASD and non-ASD samples. Urinary screening of children with neurodevelopmental disorders by combining NMR spectroscopies (1D and 2D) in multivariate analysis is a better sensitive and a straightforward method that could help the diagnosis ASD.

GC-MS-based urine metabolic profiling of autism spectrum disorders.

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders resulting from multiple factors. Diagnosis is based on behavioural and developmental signs detected before 3 years of age, and there is no reliable biological marker. The purpose of this study was to evaluate the value of gas chromatography combined with mass spectroscopy (GC-MS) associated with multivariate statistical modeling to capture the global biochemical signature of autistic individuals. GC-MS urinary metabolic profiles of 26 autistic and 24 healthy children were obtained by liq/liq extraction, and were or were not subjected to an oximation step, and then were subjected to a persilylation step. These metabolic profiles were then processed by multivariate analysis, in particular orthogonal partial least-squares discriminant analysis (OPLS-DA, R(2)Y(cum) = 0.97, Q(2)(cum) = 0.88). Discriminating metabolites were identified. The relative concentrations of the succinate and glycolate were higher for autistic than healthy children, whereas those of hippurate, 3-hydroxyphenylacetate, vanillylhydracrylate, 3-hydroxyhippurate, 4-hydroxyphenyl-2-hydroxyacetate, 1H-indole-3-acetate, phosphate, palmitate, stearate, and 3-methyladipate were lower. Eight other metabolites, which were not identified but characterized by a retention time plus a quantifier and its qualifier ion masses, were found to differ between the two groups. Comparison of statistical models leads to the conclusion that the combination of data obtained from both derivatization techniques leads to the model best discriminating between autistic and healthy groups of children.