19 research outputs found

    Higher daytime systolic BP, prepregnancy BMI and an elevated sFlt-1/PlGF ratio predict the development of hypertension in normotensive pregnant women

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    Background: The risk of hypertensive disorders of pregnancy (HDP) varies in women with gestational diabetes mellitus (GDM), depending on the degree of insulin resistance and is also influenced by obesity. The aim of this study was to evaluate clinical features, blood pressure (BP) profiles and inflammatory markers, to identify patients with an elevated risk of developing HDP. Methods: A total of 146 normotensive pregnant women were studied. We analysed the relationships of BP profiles detected by ambulatory blood pressure monitoring (ABPM) with serum biomarkers and angiogenic factors and their association with the development of HDP. Results: Fourteen (9.6%) women developed HDP, of which 11 had GDM and 8 had obesity. Women with HDP had higher values of 24-h and daytime systolic/diastolic BP (113/69 vs. 104/64; 115/72 vs. 106/66 mmHg, respectively; p < 0.05). Higher levels of leptin (10.97 ± 0.82 vs. 10.2 ± 1.11; p = 0.018) andmonocyte chemoattractant protein-1 (MCP-1) (5.24 ± 0.60 vs. 4.9 ± 0.55; p = 0.044) and a higher soluble fms-like tyrosine kinase-1/placental growth factor (sFlt-1/PlGF) ratio (4.37 ± 2.2 vs. 2.2 ± 1.43; p = 0.003) were also observed in the HDP patients. Multivariate analysis showed that a higher sFlt-1/PlGF ratio was associated with an increased risk of developing HDP [OR = 2.02; IC 95%: 1.35–3.05]. Furthermore, higher daytime systolic BP [OR = 1.27; IC 95% 1.00–1.26] and prepregnancy body mass index (BMI) [OR = 1.14; IC 95%: 1.01–1.30] significantly increased the risk of developing HDP. Conclusions: Higher daytime systolic BP values, prepregnancy BMI and the sFlt-1/PlGF ratio are useful for identifying normotensive pregnant women with an increased risk of developing HDP.12 página

    Functional microRNA screening using a comprehensive lentiviral human microRNA expression library

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    ABSTRACT: BACKGROUND: MicroRNAs (miRNAs) are a class of small regulatory RNAs that target sequences in messenger RNAs (mRNAs) to inhibit their protein output. Dissecting the complexities of miRNA function continues to prove challenging as miRNAs are predicted to have thousands of targets, and mRNAs can be targeted by dozens of miRNAs. RESULTS: To systematically address biological function of miRNAs, we constructed and validated a lentiviral miRNA expression library containing 660 currently annotated and 422 candidate human miRNA precursors. The miRNAs are expressed from their native genomic backbone, ensuring physiological processing. The arrayed layout of the library renders it ideal for high-throughput screens, but also allows pooled screening and hit picking. We demonstrate its functionality in both short- and long-term assays, and are able to corroborate previously described results of well-studied miRNAs. CONCLUSIONS: With the miRNA expression library we provide a versatile tool for the systematic elucidation of miRNA function.

    The puzzle of coeliac disease: pieces of the molecular pathogenesis

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    Coeliac disease (CD) is a chronic intolerance to a dietary protein called gluten. This protein is present in common cereals such as wheat, barley and rye, and it is needed to prepare the derivate products such as bread or pasta. CD only occurs in individuals that carry certain altered genes. The identification of these genes and how they contribute to developing the disease is therefore crucial to understanding this disorder. When we started this study in 2001, we knew that gluten activated an abnormal immune response leading to specific intestinal damage. The work performed in this project aimed to identify which genes drive the development of the disease by comparing the expression of the genes in the intestine of coeliac patients to non-coeliacs. This work unraveled new insights into the adaptive and innate immune responses to gluten, and led us to propose that the damage in the intestine occurs because the intestinal cells are not able to reach their complete functionality and their location. The knowledge gained will guide the search for other genes that cause CD. It will also help the development of diagnostic tools for identifying patients at risk of developing CD and of new avenues for therapeutic intervention

    Patterns of chromosome 18 loss of heterozygosity in multifocal ileal neuroendocrine tumors.

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    Ileal neuroendocrine tumors (NETs) represent the most common neoplasm of the small intestine. Although up to 50% of patients with ileal NETs are diagnosed with multifocal disease, the mechanisms by which multifocal ileal NETs arise are not yet understood. In this study, we analyzed genome-wide sequencing data to examine patterns of copy number variation in 40 synchronous primary ileal NETs derived from three patients. Chromosome (chr) 18 loss of heterozygosity (LOH) was the most frequent copy number alteration identified; however, not all primary tumors from the same patient had evidence of this LOH. Our data revealed three distinct patterns of chr18 allelic loss, indicating that primary tumors from the same patient can present different LOH patterns including retention of either parental allele. In conclusion, our results are consistent with the model that multifocal ileal NETs originate independently. In addition, they suggest that there is no specific germline allele on chr18 that is the target of somatic LOH

    MLPAnalyzer: Data Analysis Tool for Reliable Automated Normalization of MLPA Fragment Data

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    Background: Multiplex Ligation dependent Probe Amplification (MLPA) is a rapid, simple, reliable and customized method for detection of copy number changes of individual genes at a high resolution and allows for high throughput analysis. This technique is typically applied for studying specific genes in large sample series. The large amount of data, dissimilarities in PCR efficiency among the different probe amplification products, and sample-to-sample variation pose a challenge to data analysis and interpretation. We therefore set out to develop an MLPA data analysis strategy and tool that is simple to use, while still taking into account the above-mentioned sources of variation
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