Eduardo Tejera

Co-expression network analysis and genetic algorithms for gene prioritization in preeclampsia.

BackgroundIn this study, we explored the gene prioritization in preeclampsia, combining co-expression network analysis and genetic algorithms optimization approaches. We analysed five public projects obtaining 1,146 significant genes after cross-platform and processing of 81 and 149 microarrays in preeclamptic and normal conditions, respectively.MethodsAfter co-expression network construction, modular and node analysis were performed using several approaches. Moreover, genetic algorithms were also applied in combination with the nearest neighbour and discriminant analysis classification methods.ResultsSignificant differences were found in the genes connectivity distribution, both in normal and preeclampsia conditions pointing to the need and importance of examining connectivity alongside expression for prioritization. We discuss the global as well as intra-modular connectivity for hubs detection and also the utility of genetic algorithms in combination with the network information. FLT1, LEP, INHA and ENG genes were identified according to the literature, however, we also found other genes as FLNB, INHBA, NDRG1 and LYN highly significant but underexplored during normal pregnancy or preeclampsia.ConclusionsWeighted genes co-expression network analysis reveals a similar distribution along the modules detected both in normal and preeclampsia conditions. However, major differences were obtained by analysing the nodes connectivity. All models obtained by genetic algorithm procedures were consistent with a correct classification, higher than 90%, restricting to 30 variables in both classification methods applied.Combining the two methods we identified well known genes related to preeclampsia, but also lead us to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which may have to be validated experimentally.

Adhesion molecules (VCAM‐1 and ICAM‐1) and C‐reactive protein in women with history of preeclampsia

Several studies suggest that women with previous preeclampsia (PE) are at increased risk of cardiovascular disease (CVD). We examined circulating concentrations of adhesion molecules and C‐reactive protein (CRP), markers for endothelial and inflammatory reactions, in addition to blood pressure and anthropometric measurements in 58 women with a history of PE and 49 control women with no pathology associated to pregnancy. Soluble adhesion molecules were measured by standard commercial ELISA methods and plasma CRP levels by automated enzymatic assays. Systolic and diastolic blood pressures and waist‐to‐hip ratios were significantly higher in women with history of PE than in control group. There were no significant differences in circulating levels of sICAM‐1, sVCAM‐1 and CRP in the study population. Women with a history of PE do not have a persistent inflammatory state that could induce overexpression of these molecules, which was supported by normal levels of CRP. The study supports the existence of common risk factors for PE and CVD, namely obesity and hypertension.

Preeclampsia: a bioinformatics approach through protein-protein interaction networks analysis

BackgroundIn this study we explored preeclampsia through a bioinformatics approach. We create a comprehensive genes/proteins dataset by the analysis of both public proteomic data and text mining of public scientific literature. From this dataset the associated protein-protein interaction network has been obtained. Several indexes of centrality have been explored for hubs detection as well as the enrichment statistical analysis of metabolic pathway and disease.ResultsWe confirmed the well known relationship between preeclampsia and cardiovascular diseases but also identified statistically significant relationships with respect to cancer and aging. Moreover, significant metabolic pathways such as apoptosis, cancer and cytokine-cytokine receptor interaction have also been identified by enrichment analysis. We obtained FLT1, VEGFA, FN1, F2 and PGF genes with the highest scores by hubs analysis; however, we also found other genes as PDIA3, LYN, SH2B2 and NDRG1 with high scores.ConclusionsThe applied methodology not only led to the identification of well known genes related to preeclampsia but also to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which eventually need to be validated experimentally. Moreover, new possible connections were detected between preeclampsia and other diseases that could open new areas of research. More must be done in this area to resolve the identification of unknown interactions of proteins/genes and also for a better integration of metabolic pathways and diseases.

Haemostatic factors in women with history of preeclampsia.

OBJECTIVE Evaluation of haemostatic parameters--Plasma tissue plasminogen activator (t-PA), plasminogen activator inhibitor type 1 (PAI-1) and fibrin fragment D-dimer several years after the end of pregnancy to investigate if they are modified in women with history of preeclampsia (PE). STUDY DESIGN 65 healthy women with history of PE and 54 control women with previous normal pregnancy were enrolled in this study. Groups were matched for age, time period since delivery, smoking status and alcohol consumption. t-PA, PAI-1 and fibrin fragment D-dimer antigen levels were quantified using standards commercial ELISA methods. Plasma fibrinogen was measured using automated capillary zone electrophoresis. RESULTS Systolic and diastolic blood pressures were higher in women with history of PE. Levels of t-PA, PAI-1 and fibrinogen were similar between groups as well as the t-PA/PAI-1 ratio. A significant increase in D-dimer levels was observed in women with history of PE. CONCLUSION The increase in D-dimer level suggests an abnormal haemostatic potential namely increased intravascular coagulation. This, together with the increased blood pressure, can reflect a tendency for an increased risk of cardiovascular/thrombotic events later in life.

Application of desirability-based multi(bi)-objective optimization in the design of selective arylpiperazine derivates for the 5-HT1A serotonin receptor.

The multiobjective optimization technique based on the desirability estimation of several interrelated responses (MOOP-DESIRE) has been recently applied to quantitative structure-activity relationship (QSAR) studies. However, the advantage of applying this new methodology to the study of selectivity and affinity to competitive targets has been little explored. We used the MOOP-DESIRE methodology and a variation of this, to study the arylpiperazine derivates that could interact with 5-HT(1A) and 5-HT(2A), serotonin receptor subtypes with the objective of designing more selective molecules for the 5-HT(1A) receptor. We did show that the model results are in agreement with the available pharmacophore descriptions, guaranteeing an appropriate structural correlation and proving the methodology, as a useful tool for the important problem of selective drug design.

Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes

Objective. A model construction for classification of women with normal, hypertensive and preeclamptic pregnancy in different gestational ages using maternal heart rate variability (HRV) indexes. Method and patients. In the present work, we applied the artificial neural network for the classification problem, using the signal composed by the time intervals between consecutive RR peaks (RR) (n = 568) obtained from ECG records. Beside the HRV indexes, we also considered other factors like maternal history and blood pressure measurements. Results and conclusions. The obtained result reveals sensitivity for preeclampsia around 80% that increases for hypertensive and normal pregnancy groups. On the other hand, specificity is around 85–90%. These results indicate that the combination of HRV indexes with artificial neural networks (ANN) could be helpful for pregnancy study and characterization.

Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands.

Telomeres and telomerase are key players in tumorogenesis. Among the various strategies proposed for telomerase inhibition or telomere uncapping, the stabilization of telomeric G-quadruplex (G4) structures is a very promising one. Additionally, G4 stabilizing ligands also act over tumors mediated by the alternative elongation of telomeres. Accordingly, the discovery of novel compounds able to act on telomeres and/or inhibit the telomerase enzyme by stabilizing DNA telomeric G4 structures as well as the development of approaches efficiently prioritizing such compounds constitute active areas of research in computational medicinal chemistry and anticancer drug discovery. In this direction, we applied a virtual screening strategy based on the rigorous application of QSAR best practices and its harmonized integration with structure-based methods. More than 600,000 compounds from commercial databases were screened, the first 99 compounds were prioritized, and 21 commercially available and structurally diverse candidates were purchased and submitted to experimental assays. Such strategy proved to be highly efficient in the prioritization of G4 stabilizer hits, with a hit rate of 23.5%. The best G4 stabilizer hit found exhibited a shift in melting temperature from FRET assay of +7.3 °C at 5 μM, while three other candidates also exhibited a promising stabilizing profile. The two most promising candidates also exhibited a good telomerase inhibitory ability and a mild inhibition of HeLa cells growth. None of these candidates showed antiproliferative effects in normal fibroblasts. Finally, the proposed virtual screening strategy proved to be a practical and reliable tool for the discovery of novel G4 ligands which can be used as starting points of further optimization campaigns.

Desirability-Based Multi-Objective QSAR in Drug Discovery

The adjustment of multiple criteria in hit-to-lead identification and lead optimization is a major advance in drug discovery. Thus, the development of approaches able to handle additional criteria for the early simultaneous treatment of the most important properties determining the pharmaceutical profile of a drug candidate is an emergent issue in this area. In this paper, we review a desirability-based multi-objective QSAR method allowing the joint handling of multiple properties of interest in drug discovery: the MOOP-DESIRE methodology. This methodology adapts desirability theory concepts allowing the holistic modeling of the many and conflicting biological properties determining the therapeutic utility of a drug candidate. Here we survey their suitability for key tasks involving the use of chemoinformatics methods in medicinal chemistry and drug discovery.

Blood Pressure and Heart Rate Variability Complexity Analysis in Pregnant Women with Hypertension

Background. In this work, we perform a comparative analysis of blood pressure and heart rate variability complexity during pregnancy between normal, hypertensive, and preeclamptic women. Methods and Results. A total of 563 short electrocardiographic (10 min) records were obtained from 217 pregnant women (135 normal, 55 hypertensive, and 27 preeclamptic) during several gestational ages in sitting position. We used a mixed unbalanced model for the longitudinal statistical analysis and besides the conventional spectral analysis, we applied Lempel–Ziv complexity, sample entropy, approximated entropy, and detrended fluctuation analysis in the complexity measurement. Conclusions. The obtained results revealed significant differences between pathological and normal states with important considerations related to pregnancy adaptability and evolution as well as the relationship of complexity and blood pressure with factors such as maternal age, familial history of diabetes or hypertension, and parity.

Relationship between Heart Rate Variability Indexes and Common Biochemical Markers in Normal and Hypertensive Third Trimester Pregnancy

Background. In this study, we explored the correlations between heart rate variability indexes and some biochemical markers during the third trimester of normal, hypertensive, and preeclamptic pregnancies. Methods and Results. The obtained indexes are associated with complexity and spectral variables calculated from short electrocardiographic records. Conclusions. Including all the subjects in the analysis, we found that complexity indexes are positively related with hemoglobin concentration in the pathologic group and uric acid blood levels whereas low frequency (LF) was negatively correlated with uric acid and creatinine concentration as well as positively correlated with platelet levels. The LF was the only spectral region with significant correlation. Through an independent analysis of groups, only significant correlations were found in normal and preeclamptic groups between LF and uric acid concentration and in normal and hypertensive groups for LF and creatinine blood levels.