Enzyme replacement prevents enamel defects in hypophosphatasia mice

Hypophosphatasia (HPP) is the inborn error of metabolism characterized by deficiency of alkaline phosphatase activity, leading to rickets or osteomalacia and to dental defects. HPP occurs from loss-of-function mutations within the gene that encodes the tissue-nonspecific isozyme of alkaline phosphatase (TNAP). TNAP knockout (Alpl-/-, aka Akp2-/-) mice closely phenocopy infantile HPP, including the rickets, vitamin B6-responsive seizures, improper dentin mineralization, and lack of acellular cementum. Here, we report that lack of TNAP in Alpl-/- mice also causes severe enamel defects, which are preventable by enzyme replacement with mineral-targeted TNAP (ENB-0040). Immunohistochemistry was used to map the spatiotemporal expression of TNAP in the tissues of the developing enamel organ of healthy mouse molars and incisors. We found strong, stage-specific expression of TNAP in ameloblasts. In the Alpl-/- mice, histological, mu CT, and scanning electron microscopy analysis showed reduced mineralization and disrupted organization of the rods and inter-rod structures in enamel of both the molars and incisors. All of these abnormalities were prevented in mice receiving from birth daily subcutaneous injections of mineral-targeting, human TNAP at 8.2?mg/kg/day for up to 44 days. These data reveal an important role for TNAP in enamel mineralization and demonstrate the efficacy of mineral-targeted TNAP to prevent enamel defects in HPP. (C) 2012 American Society for Bone and Mineral Research.

BayGO: Bayesian analysis of ontology term enrichment in microarray data

Abstract Background The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem. Results BayGO implements a Bayesian approach to search for enriched terms from microarray data. The R source-code is freely available at http://blasto.iq.usp.br/~tkoide/BayGO in three versions: Linux, which can be easily incorporated into pre-existent pipelines; Windows, to be controlled interactively; and as a web-tool. The software was validated using a bacterial heat shock response dataset, since this stress triggers known system-level responses. Conclusion The Bayesian model accounts for the fact that, eventually, not all the genes from a given category are observable in microarray data due to low intensity signal, quality filters, genes that were not spotted and so on. Moreover, BayGO allows one to measure the statistical association between generic ontology terms and differential expression, instead of working only with the common significance analysis.