Gene expression profile of endothelial cells exposed to estrogenic environmental compounds: implications to pulmonary vascular lesions

AIMS: The cardiovascular system is an important target of estrogenic compounds. Considering the recent studies that question previously reported cardio-protective effects of estrogen, there is a growing concern that estrogenic environmental compounds may contribute to the pathology of vascular lesion formation. MAIN METHODS: Real-time quantitative PCR was used to monitor the expression of genes involved in vascularization. Using Bayesian network modeling, we determined a gene network that estrogenic chemicals modulate in human vascular endothelial cells. KEY FINDINGS: We showed that planar and coplanar polychlorinated biphenyls (PCBs) induce the expression of different genes compared to estradiol. Non-planar PCB congener 153 induced NOTCH3 which is a new finding as well as CCL2 and IL8 similar to what has been reported by other non-planar PCBs in endothelial cells. Our gene network indicated that experimental treatments signal a network containing TGF-beta receptor and NOTCH3; molecules biologically relevant to signaling pulmonary vascular lesions. SIGNIFICANCE: We report in the present study that exposure of vascular endothelial cells to environmentally relevant concentrations of estrogenic PCBs induce gene networks implicated in the process of inflammation and adhesion. Our data suggest that PCBs can promote vascular lesion formation by activating gene networks involved in endothelial cell adhesion, cell growth, and pro-inflammatory molecules which were different from natural estrogen. Since inflammation and adhesion are a hallmark in the pathology of endothelial cell dysfunction, reconstructing gene networks provide insight into the potential mechanisms that may contribute to the vascular risks associated with estrogenic environmental chemicals

Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma

In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I–IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96–100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential ‘hubs of activity’. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several ‘key genes’ may be required for the development of glioblastoma. Further studies are needed to validate these ‘key genes’ as useful tools for early detection and novel therapeutic options for these tumors

Causal effects in microbiomes using interventional calculus

Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques to understand causal relationships between various entities in a microbiome, and to use the resulting causal network to make useful computations. We introduce a novel pipeline for microbiome analysis, which includes adding an outcome or “disease” variable, and then computing the causal network, referred to as a “disease network”, with the goal of identifying disease-relevant causal factors from the microbiome. Internventional techniques are then applied to the resulting network, allowing us to compute a measure called the causal effect of one or more microbial taxa on the outcome variable or the condition of interest. Finally, we propose a measure called causal influence that quantifies the total influence exerted by a microbial taxon on the rest of the microiome. Our pipeline is robust, sensitive, different from traditional approaches, and able to predict interventional effects without any controlled experiments. The pipeline can be used to identify potential eubiotic and dysbiotic microbial taxa in a microbiome. We validate our results using synthetic data sets and using results on real data sets that were previously published

Parent Grief 1–13 Months After Death in Neonatal and Pediatric Intensive Care Units

Objective Describe changes in mothers’ and fathers’ grief from 1 to 13 months after infant/child neonatal/pediatric intensive care unit death and identify factors related to their grief. Methods Mothers (n = 130) and fathers (n = 52) of 140 children (newborn-18 years) completed the Hogan Grief Reaction Checklist at 1, 3, 6, and 13 months post-death. Results Grief decreased from 3 to 13 months for mothers and from 3 to 6 months for fathers. Grief was more intense for: mothers of deceased adolescents and mothers whose child was declared brain dead. Conclusion Mothers’ and fathers’ grief intensity may not coincide, resulting in different needs during the 13 months after infant/child death

Health and Functioning in Grandparents After a Young Grandchild?s Death

This cross-sectional study examined the physical and mental health, grief and role functioning of 136 grandparents in the first year after death of their young grandchild (newborn through 6 years). Grandparents were 36–77 years old; 73 % female; 24 % Hispanic, 38 % Black/African American, and 38 % White. Mean age of the 115 deceased grandchildren was 12.8 months (SD = 20.71) with 37 %\1 month old; 65 % were male, 77 % died in the hospital. Grandparents were recruited through state death records and interviewed by telephone. Grandparents experienced: clinical depression (31 %), PTSD (35 %); illnesses (28 %), hospitalizations, new chronic health conditions (mental disorders, hypertension, angina, cancer), and medication changes. Grandparents who provided care for the deceased grandchild had more intense symptoms of grief, depression and PTSD and more trouble focusing at their jobs. Severity of depressive and/or PTSD symptoms were more likely to be at clinically important levels for grandparents who had provided childcare for the deceased grandchild than for non-caregiving grandparents. Black grandparents had more severe symptoms of PTSD and thought more about their deceased grandchild on the job than White grandparents. The interaction effect of race/ethnicity and provision of child care was significant for PTSD and Blame and Anger. Hispanic grandparents who provided some child care for their deceased grandchild had less severe PTSD symptoms than caregiving Black and White grandparents. Caregiving Hispanic grandparents also experienced less Blame and Anger than White caregiving grandparents

Immunopathogenesis of HIV Infection in Cocaine Users: Role of Arachidonic Acid

Arachidonic acid (AA) is known to be increased in HIV infected patients and illicit drug users are linked with severity of viral replication, disease progression, and impaired immune functions. Studies have shown that cocaine accelerates HIV infection and disease progression mediated by immune cells. Dendritic cells (DC) are the first line of antigen presentation and defense against immune dysfunction. However, the role of cocaine use in HIV associated acceleration of AA secretion and its metabolites on immature dendritic cells (IDC) has not been elucidated yet. The aim of this study is to elucidate the mechanism of AA metabolites cyclooxygenase-2 (COX-2), prostaglandin E2 synthetase (PGE2), thromboxane A2 receptor (TBXA2R), cyclopentenone prostaglandins (CyPG), such as 15-deoxy-Δ12,14-PGJ2 (15d-PGJ2), 14-3-3 ζ/δ and 5-lipoxygenase (5-LOX) mediated induction of IDC immune dysfunctions in cocaine using HIV positive patients. The plasma levels of AA, PGE2, 15d-PGJ2, 14-3-3 ζ/δ and IDC intracellular COX-2 and 5-LOX expression were assessed in cocaine users, HIV positive patients, HIV positive cocaine users and normal subjects. Results showed that plasma concentration levels of AA, PGE2 and COX-2, TBXA2R and 5-LOX in IDCs of HIV positive cocaine users were significantly higher whereas 15d-PGJ2 and 14-3-3 ζ/δ were significantly reduced compared to either HIV positive subjects or cocaine users alone. This report demonstrates that AA metabolites are capable of mediating the accelerative effects of cocaine on HIV infection and disease progression