Assessment of the total effective xenoestrogen burden in extracts of human placentas

We have standardized a method to assess the total effective xenoestrogen burden (TEXB) in human placentas by the extraction and separation by high-performance liquid chromatography of two fractions containing lipophilic xenoestrogens (alpha) and endogenous hormones (beta), followed by assessing their estrogenicity in MCF-7 breast cancer cell-based E-Screen and Yeast Estrogen Screen (YES) bioassays. The means of TEXB alpha concentrations (in estradiol equivalent (Eeq) units) were 1.32 and 0.77 Eeq pM g^?1 placenta in the E-Screen and YES, respectively; TEXB beta concentrations were 6.97 and 11.56 Eeq pM g^?1 placenta, respectively. The interclass correlation coefficient was low and a fair level of agreement was observed after kappa test correction. According to the E-Screen and YES, TEXB alpha was ≥LOD in 70.0 and 55.0% of the placentas and 92.5 and 82.5% in beta, respectively. Although both bioassays can be recommended for assessing TEXB, there is greater experience with the use of the E-Screen for estrogenic assessment after extensive extraction of complex human matrices.     

Gestational diabetes mellitus epigenetically affects genes predominantly involved in metabolic diseases

Offspring exposed to gestational diabetes mellitus (GDM) have an increased risk for chronic diseases, and one promising mechanism for fetal metabolic programming is epigenetics. Therefore, we postulated that GDM exposure impacts the offspring’s methylome and used an epigenomic approach to explore this hypothesis. Placenta and cord blood samples were obtained from 44 newborns, including 30 exposed to GDM. Women were recruited at first trimester of pregnancy and followed until delivery. GDM was assessed after a 75-g oral glucose tolerance test at 24–28 weeks of pregnancy. DNA methylation was measured at > 485,000 CpG sites (Infinium HumanMethylation450 BeadChips). Ingenuity Pathway Analysis was conducted to identify metabolic pathways epigenetically affected by GDM. Our results showed that 3,271 and 3,758 genes in placenta and cord blood, respectively, were potentially differentially methylated between samples exposed or not to GDM (p-values down to 1 × 10⁻⁰⁶; none reached the genome-wide significance levels), with more than 25% (n = 1,029) being common to both tissues. Mean DNA methylation differences between groups were 5.7 ± 3.2% and 3.4 ± 1.9% for placenta and cord blood, respectively. These genes were likely involved in the metabolic diseases pathway (up to 115 genes (11%), p-values for pathways = 1.9 × 10⁻¹³ < p < 4.0 × 10⁻⁰³; including diabetes mellitus p = 4.3 × 10⁻¹¹). Among the differentially methylated genes, 326 in placenta and 117 in cord blood were also associated with newborn weight. Our results therefore suggest that GDM has epigenetic effects on genes preferentially involved in the metabolic diseases pathway, with consequences on fetal growth and development, and provide supportive evidence that DNA methylation is involved in fetal metabolic programming.     

Prenatal stress decreases Bdnf expression and increases methylation of Bdnf exon IV in rats

There is ample evidence that exposure to stress during gestation increases the risk of the offspring to develop mood disorders. Brain-derived neurotrophic factor (Bdnf) plays a critical role during neuronal development and is therefore a prime candidate to modulate neuronal signaling in adult offspring of rat dams that were stressed during gestation. In the current study, we tested the hypothesis that alterations in Bdnf expression in prenatally stressed (PNS) offspring are mediated by changes in DNA methylation in exons IV and VI of the Bdnf gene. We observed decreased Bdnf expression in the amygdala and hippocampus of prenatally stressed rats both at weaning and in adulthood. This decrease in Bdnf expression was accompanied by increased DNA methylation in Bdnf exon IV in the amygdala and hippocampus, suggesting that PNS-induced reduction in Bdnf expression may, at least in part, be mediated by increased DNA methylation of Bdnf exon IV. Expression of DNA methyltransferases (Dnmt) 1 and 3a was increased in PNS rats in the amygdala and hippocampus. Our data suggest that PNS induces decreases in Bdnf expression that may at least in part be mediated by increased DNA methylation of Bdnf exon IV.     

Species Differences in Developmental Toxicity of Epoxiconazole and Its Relevance to Humans

Epoxiconazole, a triazole‐based fungicide, was tested in toxicokinetic, prenatal and pre‐postnatal toxicity studies in guinea pigs, following oral (gavage) administration at several dose levels (high dose: 90 mg/kg body weight per day). Maternal toxicity was evidenced by slightly increased abortion rates and by histopathological changes in adrenal glands, suggesting maternal stress. No compound‐related increase in the incidence of malformations or variations was observed in the prenatal study. In the pre‐postnatal study, epoxiconazole did not adversely affect gestation length, parturition, or postnatal growth and development. Administration of epoxiconazole did not alter circulating estradiol levels. Histopathological examination of the placentas did not reveal compound‐related effects. The results in guinea pigs are strikingly different to those observed in pregnant rats, in which maternal estrogen depletion, pathological alteration of placentas, increased gestation length, late fetal death, and dystocia were observed after administration of epoxiconazole. In the studies reported here, analysis of maternal plasma concentrations and metabolism after administration of radiolabeled epoxiconazole demonstrated that the different results in rats and guinea pigs were not due to different exposures of the animals. A comprehensive comparison of hormonal regulation of pregnancy and birth in murid rodents and primates indicates that the effects on pregnancy and parturition observed in rats are not applicable to humans. In contrast, the pregnant guinea pig shares many similarities to pregnant humans regarding hormonal regulation and is therefore considered to be a suitable species for extrapolation of related effects to humans. Birth Defects Res (Part B) 98:230–246, 2013. © 2013 Wiley Periodicals, Inc.     

Maternal weight affects placental DNA methylation of genes involved in metabolic pathways in the common marmoset monkey (Callithrix jacchus)

Accumulating evidence suggests that dysregulation of placental DNA methylation (DNAm) is a mechanism linking maternal weight during pregnancy to metabolic programming outcomes. The common marmoset, Callithrix jaccus, is a platyrrhine primate species that has provided much insight into studies of the primate placenta, maternal condition, and metabolic programming, yet the relationships between maternal weight and placental DNAm are unknown. Here, we report genome-wide DNAm from term marmoset placentas using reduced representation bisulfite sequencing. We identified 74 genes whose DNAm pattern is associated with maternal weight during gestation. These genes are predominantly involved in energy metabolism and homeostasis, including the regulation of glycolytic and lipid metabolic processes pathways.     

Approaching mathematical model of the immune network based DNA Strand Displacement system

One biggest obstacle in molecular programming is that there is still no direct method to compile any existed mathematical model into biochemical reaction in order to solve a computational problem. In this paper, the implementation of DNA Strand Displacement system based on nature-inspired computation is observed. By using the Immune Network Theory and Chemical Reaction Network, the compilation of DNA-based operation is defined and the formulation of its mathematical model is derived. Furthermore, the implementation on this system is compared with the conventional implementation by using silicon-based programming. From the obtained results, we can see a positive correlation between both. One possible application from this DNA-based model is for a decision making scheme of intelligent computer or molecular robot.     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.     

Rule extraction in gene–disease relationship discovery

Background

Biomedical data available to researchers and clinicians have increased dramatically over the past years because of the exponential growth of knowledge in medical biology. It is difficult for curators to go through all of the unstructured documents so as to curate the information to the database. Associating genes with diseases is important because it is a fundamental challenge in human health with applications to understanding disease properties and developing new techniques for prevention, diagnosis and therapy.

Methods

Our study uses the automatic rule-learning approach to gene–disease relationship extraction. We first prepare the experimental corpus from MEDLINE and OMIM. A parser is applied to produce some grammatical information. We then learn all possible rules that discriminate relevant from irrelevant sentences. After that, we compute the scores of the learned rules in order to select rules of interest. As a result, a set of rules is generated.

Results

We produce the learned rules automatically from the 1000 positive and 1000 negative sentences. The test set includes 400 sentences composed of 200 positives and 200 negatives. Precision, recall and F-score served as our evaluation metrics. The results reveal that the maximal precision rate is 77.8% and the maximal recall rate is 63.5%. The maximal F-score is 66.9% where the precision rate is 70.6% and the recall rate is 63.5%.

Conclusions

We employ the rule-learning approach to extract gene–disease relationships. Our main contributions are to build rules automatically and to support a more complete set of rules than a manually generated one. The experiments show exhilarating results and some improving efforts will be made in the future.