This study was designed to assess the effect of succinylsulfathiazole on the apparent bioavailability of folate added to milk-containing diets. We also evaluated the impact of milk type on the relative bioavailability (bioavailability) of folate following pasteurization (62.5 degrees C, 30 min) and on the bioavailability of folic acid (PteGlu) vs. pteroylhexa-gamma-L-glutamic acid (PteGlu6). Following folate depletion (5 wk), 108 rats (six per group) were fed for 4 wk diets with or without 20 g milk solids/100 g diet and containing PteGlu, PteGlu + 5 g/kg succinylsulfathiazole, or PteGlu6. Folate bioavailability was determined using plasma folate concentration and a standard curve generated from rats fed milk-free diets with graded levels of PteGlu. The PteGlu and PteGlu6 bioavailability from human milk-containing diets was twice that of diets containing cow or goat milk (P < 0.05). Incorporation of a sulfa drug into diets containing human or cow milk reduced PteGlu bioavailability by one half (P < 0.05). Further, the values for bioavailability of PteGlu from diets containing human or goat milk no longer differed (0.86 and 0.75, respectively), and bioavailabilities from human milk- and goat milk-containing diets were greater than that of the cow-milk-containing diet (0.54) (P < 0.05). Pasteurization of milk did not influence folate bioavailability. The bioavailability of PteGlu6 was 49-71% that of PteGlu (P < 0.05). In summary, milk type differentially affects intestinal folate biosynthesis, and the superior folate bioavailability from human milk-containing diets is due in part to enhanced intestinal biosynthesis of folate.
Gene expression profiles from microarray time course experiments provide a unique opportunity to examine genome-wide signal processing and gene responses. A fundamental issue in microarray experiments is that the treatment condition can only be controlled at the cell level rather than at the gene level. The treatment condition does not affect all genes equally. Some genes depend on other genes to detect external changes. The dependency between genes is not fully deterministic and may vary with treatment condition. Thus the expression of each gene is potentially affected by two confounding effects: the treatment effect and the gene context effect arising from the regulatory interactions among genes. This gene context effect is hard to isolate. Neither can it be simply ignored. Instead, this gene context information which may be different under different treatment conditions is of primary biological interest. We introduce an approach which deals with the confounding effects and takes into account the uncontrollable gene context effect. Our method is based on the estimation of the number of hidden states, which, in our development, corresponds to the order of a hidden Markov model (HMM). For each gene, its observed expression is modeled by a gamma distribution determined by the corresponding hidden state at each time point. Those genes showing evidence for more than one hidden state can be categorized as the signalling genes, or in a wider sense, as the response genes which are coordinated by a cell system in reaction to a specific external condition. These response genes can be used in the comparison of different treatment conditions, to investigate the gene context effect under different treatments. Microarray time course data are also analyzed to demonstrate our method.
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