BackgroundAutism spectrum disorders (ASDs) are relatively common neurodevelopmental conditions whose biological basis has been incompletely determined. Several biochemical markers have been associated with ASDs, but there is still no laboratory test for these conditions.MethodsWe analyzed the metabolic profile of lymphoblastoid cell lines from 137 patients with neurodevelopmental disorders with or without ASDs and 78 normal individuals, using Biolog Phenotype MicroArrays.ResultsMetabolic profiling of lymphoblastoid cells revealed that the 87 patients with ASD as a clinical feature, as compared to the 78 controls, exhibited on average reduced generation of NADH when tryptophan was the sole energy source. The results correlated with the behavioral traits associated with either syndromal or non-syndromal autism, independent of the genetic background of the individual. The low level of NADH generation in the presence of tryptophan was not observed in cell lines from non-ASD patients with intellectual disability, schizophrenia or conditions exhibiting several similarities with syndromal autism except for the behavioral traits. Analysis of a previous small gene expression study found abnormal levels for some genes involved in tryptophan metabolic pathways in 10 patients.ConclusionsTryptophan is a precursor of important compounds, such as serotonin, quinolinic acid, and kynurenic acid, which are involved in neurodevelopment and synaptogenesis. In addition, quinolinic acid is the structural precursor of NAD+, a critical energy carrier in mitochondria. Also, the serotonin branch of the tryptophan metabolic pathway generates NADH. Lastly, the levels of quinolinic and kynurenic acid are strongly influenced by the activity of the immune system. Therefore, decreased tryptophan metabolism may alter brain development, neuroimmune activity and mitochondrial function. Our finding of decreased tryptophan metabolism appears to provide a unifying biochemical basis for ASDs and perhaps an initial step in the development of a diagnostic assay for ASDs.

Free-living elderly people aged > or = 65 y were recruited to assess riboflavin and vitamin B-6 intakes and status and the effect of riboflavin supplementation on biochemical indicators of these 2 vitamins. The status of riboflavin (erythrocyte glutathione reductase activation coefficient; EGRAC) and vitamin B-6 (plasma pyridoxal-5'-phosphate; PLP) were determined in a total sample of 92 subjects, from whom dietary intake data were obtained by using the diet history method (n = 83). Although dietary intakes of both vitamins were considered to be adequate according to current reference values, abnormal EGRAC and plasma PLP values were identified in 49% and 38% of subjects, respectively, with 21% having suboptimal status for both nutrients. A subgroup of subjects from the initial sample (n = 45) was assigned in a double-blind manner to receive either 1.6 or 25 mg riboflavin or placebo daily for 12 wk. In those subjects with a baseline EGRAC or plasma PLP value falling outside the currently accepted threshold value for adequacy, low-dose riboflavin supplementation improved status of the limiting nutrient significantly (P<0.0001 and P = 0.020 for EGRAC and plasma PLP responses, respectively). We conclude that a high proportion of healthy elderly people may have suboptimal status for these nutrients despite apparently adequate dietary intakes. Furthermore, we showed that riboflavin supplementation at physiologic doses corrects biochemical abnormalities of not only EGRAC, but also plasma PLP, confirming the biochemical interdependency of these vitamins and suggesting that riboflavin is the limiting nutrient.

Background The most important factor affecting metabolic excretion of compounds from the body is their half-life time. This provides an indication of compound stability of, for example, drug molecules. We report on our efforts to develop QSAR models for metabolic stability of compounds, based on in vitro half-life assay data measured in human liver microsomes. Method A variety of QSAR models generated using different statistical methods and descriptor sets implemented in both open-source and commercial programs (KNIME, GUSAR and StarDrop) were analyzed. The models obtained were compared using four different external validation sets from public and commercial data sources, including two smaller sets of in vivo half-life data in humans. Conclusion In many cases, the accuracy of prediction achieved on one external test set did not correspond to the results achieved with another test set. The most predictive models were used for predicting the metabolic stability of compounds from the open NCI database, the results of which are publicly available on the NCI/CADD Group web server (http://cactus.nci.nih.gov).