Obstetricians and Gynecologists, Family physicians Learning Objectives: After completion of this article, the reader should be better able to appraise the recent literature on the development of preeclampsia in the first-trimester, evaluate the predictive value of first-trimester markers and use first-trimester markers, either individually or in combination, to assess the risk of preeclampsia.
The mouse limb deformity (ld) mutations cause limb malformations by disrupting epithelial-mesenchymal signaling between the polarizing region and the apical ectodermal ridge. Formin was proposed as the relevant gene because three of the five ld alleles disrupt its C-terminal domain. In contrast, our studies establish that the two other ld alleles directly disrupt the neighboring Gremlin gene, corroborating the requirement of this BMP antagonist for limb morphogenesis. Further doubts concerning an involvement of Formin in the ld limb phenotype are cast, as a targeted mutation removing the C-terminal Formin domain by frame shift does not affect embryogenesis. In contrast, the deletion of the corresponding genomic region reproduces the ld limb phenotype and is allelic to mutations in Gremlin. We resolve these conflicting results by identifying a cis-regulatory region within the deletion that is required for Gremlin activation in the limb bud mesenchyme. This distant cis-regulatory region within Formin is also altered by three of the ld mutations. Therefore, the ld limb bud patterning defects are not caused by disruption of Formin, but by alteration of a global control region (GCR) required for Gremlin transcription. Our studies reveal the large genomic landscape harboring this GCR, which is required for tissue-specific coexpression of two structurally and functionally unrelated genes.[Keywords: cis regulation; Formin; global control region; Gremlin; limb development; regulatory landscape] Supplemental material is available at http://www.genesdev.org. Received February 8, 2004; revised version accepted May 5, 2004. The mouse is the genetic model of choice to study mammalian development and disease. In addition to alteration of specific genes by gene targeting and transgenesis, mutant mouse strains identified by phenotypic screens are commonly used to analyze developmental and disease processes (for reviews, see Justice 2000; Perkins 2002). A significant fraction of spontaneous mutations in mice (and humans) cause congenital limb malformations and have proven crucial to unravel the molecular mechanisms regulating vertebrate limb bud morphogenesis (for review, see Gurrieri et al. 2002). In particular, several alleles of the recessive mouse limb deformity (ld) mutation disrupt patterning of the distal limb skeleton. Over the years, a total of five ld alleles have been identified by phenotypic and genetic complementation analysis. All ld homozygous newborn mice display limb patterning defects characterized by synostosis of the zeugopod in combination with oligo-and syndactyly of metacarpal bones and digits (for review, see Zeller et al. 1999). In addition, ld homozygous newborn mice display varying degrees of uni-and bilateral renal aplasias depending on allele "strength" (Maas et al. 1994). Molecular analysis showed that the two ld alleles (ld
Objective The first aim was to investigate specific signature patterns of metabolites that are significantly altered in first-trimester serum of women who subsequently developed preeclampsia (PE) compared to healthy pregnancies. The second aim of this study was to examine the predictive performance of the selected metabolites for both early onset [EO-PE] and late onset PE [LO-PE].MethodsThis was a case-control study of maternal serum samples collected between 8+0 and 13+6 weeks of gestation from 167 women who subsequently developed EO-PE n = 68; LO-PE n = 99 and 500 controls with uncomplicated pregnancies. Metabolomics profiling analysis was performed using two methods. One has been optimized to target eicosanoids/oxylipins, which are known inflammation markers and the other targets compounds containing a primary or secondary biogenic amine group. Logistic regression analyses were performed to predict the development of PE using metabolites alone and in combination with first trimester mean arterial pressure (MAP) measurements.ResultsTwo metabolites were significantly different between EO-PE and controls (taurine and asparagine) and one in case of LO-PE (glycylglycine). Taurine appeared the most discriminative biomarker and in combination with MAP predicted EO-PE with a detection rate (DR) of 55%, at a false-positive rate (FPR) of 10%.ConclusionOur findings suggest a potential role of taurine in both PE pathophysiology and first trimester screening for EO-PE.
ObjectivesIn a previous study, we have described the predictive value of first-trimester Pregnancy-Associated Plasma Protein-A (PAPP-A), free β-subunit of human Chorionic Gonadotropin (fβ-hCG), Placental Growth Factor (PlGF) and A Disintegrin And Metalloprotease 12 (ADAM12) for early onset preeclampsia (EO-PE; delivery <34 weeks). The objective of the current study was to obtain the predictive value of these serum makers combined with maternal characteristics and first-trimester maternal mean arterial blood pressure (MAP) in a large series of patients, for both EO-PE and late onset PE (LO-PE; delivery ≥ 34 weeks).MethodsThis was a nested case-control study, using stored first-trimester maternal serum from women who developed EO-PE (n = 68) or LO-PE (n = 99), and 500 uncomplicated singleton pregnancies. Maternal characteristics, MAP, and pregnancy outcome were collected for each individual woman and used to calculate prior risks for PE in a multiple logistic regression model. Models containing prior PE risks, serum markers, and MAP were developed for the prediction of EO-PE and LO-PE. The model-predicted detection rates (DR) for fixed 10% false-positive rates were calculated for EO-PE and LO-PE with or without the presence of a small-for-gestational age infant (SGA, birth weight <10th centile).ResultsThe best prediction model included maternal characteristics, MAP, PAPP-A, ADAM12, and PlGF, with DR of 72% for EO-PE and 49% for LO-PE. Prediction for PE with concomitant SGA was better than for PE alone (92% for EO-PE and 57% for LO-PE).ConclusionFirst-trimester MAP, PAPP-A, ADAM12, and PlGF combined with maternal characteristics and MAP are promising markers in the risk assessment of PE, especially for EO-PE complicated by SGA.
Objective. To expand the search for preeclampsia (PE) metabolomics biomarkers through the analysis of acylcarnitines in first-trimester maternal serum. Methods. This was a nested case-control study using serum from pregnant women, drawn between 8 and 14 weeks of gestational age. Metabolites were measured using an UPLC-MS/MS based method. Concentrations were compared between controls (n = 500) and early-onset- (EO-) PE (n = 68) or late-onset- (LO-) PE (n = 99) women. Metabolites with a false discovery rate <10% for both EO-PE and LO-PE were selected and added to prediction models based on maternal characteristics (MC), mean arterial pressure (MAP), and previously established biomarkers (PAPPA, PLGF, and taurine). Results. Twelve metabolites were significantly different between EO-PE women and controls, with effect levels between −18% and 29%. For LO-PE, 11 metabolites were significantly different with effect sizes between −8% and 24%. Nine metabolites were significantly different for both comparisons. The best prediction model for EO-PE consisted of MC, MAP, PAPPA, PLGF, taurine, and stearoylcarnitine (AUC = 0.784). The best prediction model for LO-PE consisted of MC, MAP, PAPPA, PLGF, and stearoylcarnitine (AUC = 0.700). Conclusion. This study identified stearoylcarnitine as a novel metabolomics biomarker for EO-PE and LO-PE. Nevertheless, metabolomics-based assays for predicting PE are not yet suitable for clinical implementation.
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