Mild forms of hypophosphatasia mostly result from dominant negative effect of severe alleles or from compound heterozygosity for severe and moderate alleles

<p>Abstract</p> <p>Background</p> <p>Mild hypophosphatasia (HPP) phenotype may result from <it>ALPL </it>gene mutations exhibiting residual alkaline phosphatase activity or from severe heterozygous mutations exhibiting a dominant negative effect. In order to determine the cause of our failure to detect a second mutation by sequencing in patients with mild HPP and carrying on a single heterozygous mutation, we tested the possible dominant effect of 35 mutations carried by these patients.</p> <p>Methods</p> <p>We tested the mutations by site-directed mutagenesis. We also genotyped 8 exonic and intronic <it>ALPL </it>gene polymorphisms in the patients and in a control group in order to detect the possible existence of a recurrent intronic mild mutation.</p> <p>Results</p> <p>We found that most of the tested mutations exhibit a dominant negative effect that may account for the mild HPP phenotype, and that for at least some of the patients, a second mutation in linkage disequilibrium with a particular haplotype could not be ruled out.</p> <p>Conclusion</p> <p>Mild HPP results in part from compound heterozygosity for severe and moderate mutations, but also in a large part from heterozygous mutations with a dominant negative effect.</p

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

IMMUNOSENSOR SYSTEMS USING LIPOSOMES AND PLANAR LIPID BILAYER MEMBRANES FOR ION-CHANNEL MODEL SENSORS

For the purpose of constructing a new biosensor which mimics the concept of "ion-channel", two approaches were examined: l)liposome systems, and 2)planar lipid bilayer membrane systems. In both cases, some interaction such as immunoreaction, electrostatruneh dıpole,sand hydrophobic interaction of a stimulus with a model receptor site at the surface of lipid membranes triggers the change in permeation of marker ions across the lipid bilayer membranes. This change in permeation of the marker ions is subsequently monitored electrochemically, which is a direct but much amplified measure of the analyte to be assayed

Dehydroepiandrosterone supplementation and the impact of follicular fluid metabolome and cytokinome profiles in poor ovarian responders

Abstract Background Poor ovarian responders (POR) are women undergoing in-vitro fertilization who respond poorly to ovarian stimulation, resulting in the retrieval of lower number of oocytes, and subsequently lower pregnancy rates. The follicular fluid (FF) provides a crucial microenvironment for the proper development of follicles and oocytes through tightly controlled metabolism and cell signaling. Androgens such as dehydroepiandrosterone (DHEA) have been proposed to alter the POR follicular microenvironment, but the impact DHEA imposes on the FF metabolome and cytokine profiles is unknown. Therefore, the objective of this study is to profile and identify metabolomic changes in the FF with DHEA supplementation in POR patients. Methods FF samples collected from 52 POR patients who underwent IVF with DHEA supplementation (DHEA +) and without (DHEA-; controls) were analyzed using untargeted liquid chromatography-tandem mass spectrometry (LC–MS/MS) metabolomics and a large-scale multiplex suspension immunoassay covering 65 cytokines, chemokines and growth factors. Multivariate statistical modelling by partial least squares-discriminant regression (PLSR) analysis was performed for revealing metabolome-scale differences. Further, differential metabolite analysis between the two groups was performed by PLSR β-coefficient regression analysis and Student’s t-test. Results Untargeted metabolomics identified 118 FF metabolites of diverse chemistries and concentrations which spanned three orders of magnitude. They include metabolic products highly associated with ovarian function – amino acids for regulating pH and osmolarity, lipids such fatty acids and cholesterols for oocyte maturation, and glucocorticoids for ovarian steroidogenesis. Four metabolites, namely, glycerophosphocholine, linoleic acid, progesterone, and valine were significantly lower in DHEA + relative to DHEA- (p < 0.05–0.005). The area under the curves of progesterone glycerophosphocholine, linoleic acid and valine are 0.711, 0.730, 0.785 and 0.818 (p < 0.05–0.01). In DHEA + patients, progesterone positively correlated with IGF-1 (Pearson r: 0.6757, p < 0.01); glycerophosphocholine negatively correlated with AMH (Pearson r: -0.5815; p < 0.05); linoleic acid correlated with estradiol and IGF-1 (Pearson r: 0.7016 and 0.8203, respectively; p < 0.01 for both). In DHEA- patients, valine negatively correlated with serum-free testosterone (Pearson r: -0.8774; p < 0.0001). Using the large-scale immunoassay of 45 cytokines, we observed significantly lower MCP1, IFNγ, LIF and VEGF-D levels in DHEA + relative to DHEA. Conclusions In POR patients, DHEA supplementation altered the FF metabolome and cytokine profile. The identified four FF metabolites that significantly changed with DHEA may provide information for titrating and monitoring individual DHEA supplementation