Lectin histochemistry for detecting cadmium-induced changes in the glycosylation pattern of rat placenta

Cadmium (Cd) is an industrial and environmental pollutant that produces toxic effects on gametogenesis, pre- and post-implantation embryos, and the placenta. Because the effects of acute Cd intoxication on the placenta are not well understood, we investigated changes in its glycosylated components in Cd treated dams at days 4, 7, 10 and 15 of gestation using lectin histochemistry. CdCl₂ was administered to pregnant rats; control animals received sterile normal saline. Placentas were processed for DBA, Con A, SBA, PNA, UEA-I, RCA-I and WGA lectin histochemistry to evaluate changes in the carbohydrate pattern of the placenta that might modify cell interactions and contribute to embryonic alterations. Lectin binding was analyzed in the yolk sac; trophoblast giant cells; trophoblast I, II and III; spongiotrophoblast cells and endovascular trophoblast cells in the chorioallantoic placenta. Our lectin binding patterns showed that Cd caused alteration of SBA and DBA labeling of trophoblast-derived cells, which suggested increased expressions of α and β GalNAc. Cd also caused decreased UEA-1 binding affinity, which indicated fewer α-L-Fuc residues in placentas of Cd treated dams. The nonreactivity in trophoblast I of the control placentas incubated with Con-A contrasted with the labeling in placentas of experimental dams, which indicated increased expression of terminal α-D-Man, and α-D-Glc residues. We found that Cd altered the reactivity of placenta to several lectins, which indicated modification of the glycotype presented by the fetal component of the placenta. We report that Cd exerts a deleterious effect on the glycosylation pattern of the placenta.     

Reranking candidate gene models with cross-species comparison for improved gene prediction

Background  

Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc). Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features.