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The Consumer Product Safety Commission (CPSC) convened a Chronic Hazard Advisory Panel (CHAP) on Phthalates found in children’s toys, and childcare products, and in products used by women of childbearing age. The CHAP conducted a risk assessment on phthalates and phthalate substitutes, and made recommendations to either ban, impose an interim ban, or allow the continued use of phthalates and phthalate substitutes in the above products. After a review of the literature, the evaluation included toxic end points of primary concern, biomonitoring results, extant exposure reconstruction, and epidemiological results. The health end points chosen were associated with the rat phthalate syndrome, which is characterized by malformations of the epididymis, vas deferens, seminal vesicles, prostate, external genitalia (hypospadias), and by cryptorchidism (undescended testes), retention of nipples/areolae, and demasculinization (~incomplete masculinization) of the perineum, resulting in reduced anogenital distance. Risk assessment demonstrated that some phthalates should be permanently banned, removed from the banned list, or remain interim banned. Biomonitoring and toxicology data provided the strongest basis for a mixture risk assessment. In contrast, external exposure data were the weakest and need to be upgraded for epidemiological studies and risk assessments. Such studies would focus on routes and sources. The review presents recommendations and uncertainties.

It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs (miRNAs). These are short RNA sequences that can down-regulate the levels of mRNAs or proteins in animals and plants. Genes regulated by miRNAs are called targets. Typically, methods for target prediction are based solely on sequence data and on the structure information. In this paper we propose a Bayesian graphical modeling approach that infers the miRNA regulatory network by integrating expression levels of miRNAs with their potential mRNA targets and, via the prior probability model, with their sequence/structure information. We use a directed graphical model with a particular structure adapted to our data based on biological considerations. We then achieve network inference using stochastic search methods for variable selection that allow us to explore the huge model space via MCMC. A time-dependent coefficients model is also implemented. We consider experimental data from a study on a very well-known developmental toxicant causing neural tube defects, hyperthermia. Some of the pairs of target gene and miRNA we identify seem very plausible and warrant future investigation. Our proposed method is general and can be easily applied to other types of network inference by integrating multiple data sources.

Background: Previous work has shown that teratogens such as hyperthermia (HS), 4-hydroperoxycyclophosphamide (4CP), and staurosporine (ST) induce cell death in day 9 mouse embryos by activating the mitochondrial apoptotic pathway. Key to the activation of this pathway is the activation of a caspase cascade involving the cleavage-induced activation of an initiator procaspase, caspase-9, and the downstream effector procaspase, caspase-3. For example, procaspase-3, an inactive proenzyme of 32 kDa is cleaved by activated caspase-9 to generate a large subunit of approximately 17 kDa and a small subunit of approximately 10 kDa. In turn, caspase-3 is known to target a variety of cellular proteins for proteolytic cleavage as part of the process by which dying cells are eliminated. Previous work has also shown that neuroepithelial cells are sensitive to teratogen-induced activation of this pathway and subsequent cell death whereas cells of the heart are resistant. Although caspase-3 is a key effector caspase activated by teratogens, two other effector caspases, caspase-6 and caspase-7, are known; however, their role in teratogen-induced cell death is unknown. Methods: Because cleavage-induced generation of specific subunits is the most specific assay for activation of caspases, we have used antibodies that recognize the procaspase and one of its active subunits and a Western blot approach to assess the activation of caspase-6 and caspase-7 in day 9 mouse embryos (or heads, hearts and trunks isolated from whole embryos) exposed to HS, 4CP, and ST. To probe the relationship between teratogen-induced activation of caspase-9/caspase-3 and the activation of caspase-6/caspase-7, we used a mitochondrial-free embryo lysate with or without the addition of cytochrome c, recombinant active caspase-3, or recombinant active caspase-9. Results: Western blot analyses show that these three teratogens, HS, 4CP, and ST, induce the activation of procaspase-6 (appearance of the 13 kDa subunit, p13) and caspase-7 (appearance of the 19 kDa subunit, p19) in day 9 mouse embryos. In vitro studies showed that both caspase-6 and caspase-7 could be activated by the addition of cytochrome c to a lysate prepared from untreated embryos. In addition, caspase-6 could be activated by the addition of either recombinant caspase-3 or caspase-9 to a lysate prepared from untreated embryos. In contrast, caspase-7 could be activated by addition of recombinant caspase-3 but only minimally by recombinant caspase-9. Like caspase-9/caspase-3, caspase-6 and caspase-7 were not activated in hearts isolated from embryos exposed to these three teratogens. Conclusions: HS, 4CP and ST induce the cleavage-dependent activation of caspase-6 and caspase-7 in day 9 mouse embryos. Results using DEVD-CHO, a caspase-3 inhibitor, suggest that teratogen-induced activation of caspase-6 is mediated by caspase-3. In addition, our data suggest that caspase-7 is activated primarily by caspase-3; however, we cannot rule out the possibility that this caspase is also activated by caspase-9. Finally, we also show that teratogen-induced activation of caspase-6 and caspase-7 are blocked in the heart, a tissue resistant to teratogen-induced cell death.

It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs (miRNAs). These are short RNA sequences that can down-regulate the levels of mRNAs or proteins in animals and plants. Genes regulated by miRNAs are called targets. Typically, methods for target prediction are based solely on sequence data and on the structure information. In this paper we propose a Bayesian graphical modeling approach that infers the miRNA regulatory network by integrating expression levels of miRNAs with their potential mRNA targets and, via the prior probability model, with their sequence/structure information. We use a directed graphical model with a particular structure adapted to our data based on biological considerations. We then achieve network inference using stochastic search methods for variable selection that allow us to explore the huge model space via MCMC. A time-dependent coefficients model is also implemented. We consider experimental data from a study on a very well-known developmental toxicant causing neural tube defects, hyperthermia. Some of the pairs of target gene and miRNA we identify seem very plausible and warrant future investigation. Our proposed method is general and can be easily applied to other types of network inference by integrating multiple data sources.