Prenatal screening of sialic acid storage disease and confirmation in cultured fibroblasts by LC-MS/MS

Sialic acid storage disease (SASD) is an inborn error resulting from defects in the lysosomal membrane protein sialin. The SASD phenotypical spectrum ranges from a severe presentation, infantile sialic acid storage disease (ISSD) which may present as hydrops fetalis, to a relatively mild form, Salla disease. Screening for SASD is performed by determination of free sialic acid (FSA) in urine or amniotic fluid supernatant (AFS). Subsequent diagnosis of SASD is performed by quantification of FSA in cultured fibroblasts and by mutation analysis of the sialin gene, SLC17A5. We describe simple quantitative procedures to determine FSA as well as conjugated sialic acid in AFS, and FSA in cultured fibroblasts, using isotope dilution (13C3-sialic acid) and multiple reaction monitoring LC-ESI-MS/MS. The whole procedure can be performed in 2–4 h. Reference values in AFS were 0–8.2 μmol/L for 15–25 weeks of gestation and 3.2-12.0 μmol/L for 26–38 weeks of gestation. In AFS samples from five fetuses affected with ISSD FSA was 23.9-58.9 μmol/L demonstrating that this method is able to discriminate ISSD pregnancies from normal ones. The method was also validated for determination of FSA in fibroblast homogenates. FSA in SASD fibroblasts (ISSD; 20–154 nmol/mg protein, intermediate SASD; 12.9-15.1 nmol/mg, Salla disease; 5.9-7.4 nmol/mg) was clearly elevated compared to normal controls (0.3-2.2 nmol/mg). In conclusion, we report simple quantitative procedures to determine FSA in AFS and cultured fibroblasts improving both prenatal diagnostic efficacy for ISSD as well as confirmatory testing in cultured fibroblasts following initial screening in urine or AFS

Economic evaluation of multiplex ligation-dependent probe amplification and karyotyping in prenatal diagnosis: a cost-minimization analysis

textabstractPurpose: To assess the cost-effectiveness of Multiplex Ligation-dependent Probe Amplification (MLPA, P095 kit) compared to karyotyping. Methods: A cost-minimization analysis alongside a nationwide prospective clinical study of 4,585 women undergoing amniocentesis on behalf of their age (≥36 years), an increased risk following first trimester prenatal screening or parental anxiety. Results: Diagnostic accuracy of MLPA (P095 kit) was comparable to karyotyping (1.0 95% CI 0.999-1.0). Health-related quality of life did not differ between the strategies (summary physical health: mean difference 0.31, p = 0.82; summary mental health: mean difference 1.91, p = 0.22). Short-term costs were lower for MLPA: mean difference €315.68 (bootstrap 95% CI €315.63-315.74; -44.4%). The long-term costs were slightly higher for MLPA: mean difference €76.42 (bootstrap 95% CI €71.32-81.52; +8.6%). Total costs were on average €240.13 (bootstrap 95% CI €235.02-245.23; -14.9%) lower in favor of MLPA. Cost differences were sensitive to proportion of terminated pregnancies, sample throughput, individual choice and performance of tests in one laboratory, but not to failure rate or the exclusion of polluted samples. Conclusion: From an economic perspective, MLPA is the preferred prenatal diagnostic strategy in women who undergo amniocentesis on behalf of their age, following prenatal screening or parental anxiety

Multiplex ligation-dependent probe amplification versus karyotyping in prenatal diagnosis: the M.A.K.E. study

Abstract BACKGROUND: In the past 30 years karyotyping was the gold standard for prenatal diagnosis of chromosomal aberrations in the fetus. Traditional karyotyping (TKT) has a high accuracy and reliability. However, it is labor intensive, the results take 14-21 days, the costs are high and unwanted findings such as abnormalities with unknown clinical relevance are not uncommon. These disadvantages challenged the practice of karyotyping. Multiplex ligation-dependent probe amplification (MLPA) is a new molecular genetic technique in prenatal diagnosis. Previous preclinical evidence suggests equivalence of MLPA and traditional karyotyping (TKT) regarding test performance. METHODS/DESIGN: The proposed study is a multicentre diagnostic substitute study among pregnant women, who choose to have amniocentesis for the indication advanced maternal age and/or increased risk following prenatal screening test. In all subjects, both MLPA and karyotyping will be performed on the amniotic fluid sample. The primary outcome is diagnostic accuracy. Secondary outcomes will be maternal quality of life, women's preferences and costs. Analysis will be intention to treat and per protocol analysis. Quality of life analysis will be carried out within the study population. The study aims to include 4500 women. DISCUSSION: The study results are expected to help decide whether MLPA can replace traditional karyotyping for 'low-risk' pregnancies in terms of diagnostic accuracy, quality of life and women's preferences. This will be the first clinical study to report on all relevant aspects of the potential replacement

Beyond ecosystem modeling: a roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundationsof community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is key to meeting the pressing needs of science and society in the 21st century