A senataxin-associated exonuclease SAN1 is required for resistance to DNA interstrand cross-links.

Interstrand DNA cross-links (ICLs) block both replication and transcription, and are commonly repaired by the Fanconi anemia (FA) pathway. However, FA-independent repair mechanisms of ICLs remain poorly understood. Here we report a previously uncharacterized protein, SAN1, as a 5' exonuclease that acts independently of the FA pathway in response to ICLs. Deletion of SAN1 in HeLa cells and mouse embryonic fibroblasts causes sensitivity to ICLs, which is prevented by re-expression of wild type but not nuclease-dead SAN1. SAN1 deletion causes DNA damage and radial chromosome formation following treatment with Mitomycin C, phenocopying defects in the FA pathway. However, SAN1 deletion is not epistatic with FANCD2, a core FA pathway component. Unexpectedly, SAN1 binds to Senataxin (SETX), an RNA/DNA helicase that resolves R-loops. SAN1-SETX binding is increased by ICLs, and is required to prevent cross-link sensitivity. We propose that SAN1 functions with SETX in a pathway necessary for resistance to ICLs.

Decreased tryptophan metabolism in patients with autism spectrum disorders.

BACKGROUND: Autism 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. METHODS: We 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. RESULTS: Metabolic 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. CONCLUSIONS: Tryptophan 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.

Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes.

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 ).