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Obesity is a multifactorial disease with both genetic and environmental components. The prevailing view is that obesity results from an imbalance between energy intake and expenditure caused by overeating and insufficient exercise. We describe another environmental element that can alter the balance between energy intake and energy expenditure: obesogens. Obesogens are a subset of environmental chemicals that act as endocrine disruptors affecting metabolic endpoints. The obesogen hypothesis posits that exposure to endocrine disruptors and other chemicals can alter the development and function of the adipose tissue, liver, pancreas, gastrointestinal tract, and brain, thus changing the set point for control of metabolism. Obesogens can determine how much food is needed to maintain homeostasis and thereby increase the susceptibility to obesity. The most sensitive time for obesogen action is in utero and early childhood, in part via epigenetic programming that can be transmitted to future generations. This review explores the evidence supporting the obesogen hypothesis and highlights knowledge gaps that have prevented widespread acceptance as a contributor to the obesity pandemic. Critically, the obesogen hypothesis changes the narrative from curing obesity to preventing obesity.

Growth factors and the extracellular matrix have been shown to fulfill vital developmental roles in many embryonic systems. Our hypothesis is that a developmental role played by the extracellular matrix in sea urchins may be the binding of a PDGF-like growth factor to promote signaling activity. We report here that anti-human PDGF-B antibodies and anti-human TGF-[alpha] antibodies immunoprecipitated specific proteins isolated from Lytechinus embryos. Addition of these antibodies to Lytechinus embryos inhibited gastrulation and spiculogenesis. The embryos are sensitive to the antibodies from the four-cell through the hatching blastula stages, which suggests that the TGF-[alpha]-like and PDGF-like ligands are required for the early differentiation of the gut and spicules. We present evidence that the PDGF-like growth factor depends on the extracellular matrix for signaling activity. Synthetic peptides representing the heparan sulfate proteoglycan binding sequence on human PDGF-B were added to Lytechinus embryo cultures to compete for binding sites with the endogenous PDGF-like growth factor. The experimental peptide inhibited gastrulation and caused radially arranged multiple spicules to form. Development was unaffected by a control peptide. These studies support our hypothesis and suggest that TGF-[alpha]-like and PDGF-like growth factors induce signaling events required for sea urchin gastrulation and spiculogenesis and suggest that an extracellular matrix-associated PDGF-like growth factor is involved in differentiation along the oral-aboral axis.

Abstract Background The use of retinoids as anti-cancer agents has been limited due to resistance and low efficacy. The dynamics of nuclear receptor coregulation are incompletely understood. Cell-and context-specific activities of nuclear receptors may be in part due to distinct coregulator complexes recruited to distinct subsets of target genes. RIP140 (also called NRIP1) is a ligand-dependent corepressor that is inducible with retinoic acid (RA). We had previously shown that RIP140 limits RA induced tumor cell differentiation of embryonal carcinoma; the pluriopotent stem cells of testicular germ cell tumors. This implies that RIP140 represses key genes required for RA-mediated tumor cell differentiation. Identification of these genes would be of considerable interest. Results To begin to address this issue, microarray technology was employed to elucidate in a de novo fashion the global role of RIP140 in RA target gene regulation of embryonal carcinoma. Subclasses of genes were affected by RIP140 in distinct manners. Interestingly, approximately half of the RA-dependent genes were unaffected by RIP140. Hence, RIP140 appears to discriminate between different classes of RA target genes. In general, RIP140-dependent gene expression was consistent with RIP140 functioning to limit RA signaling and tumor cell differentiation. Few if any genes were regulated in a manner to support a role for RIP140 in "active repression". We also demonstrated that RIP140 silencing sensitizes embryonal carcinoma cells to low doses of RA. Conclusion Together the data demonstrates that RIP140 has profound effects on RA-mediated gene expression in this cancer stem cell model. The RIP140-dependent RA target genes identified here may be particularly important in mediating RA-induced tumor cell differentiation and the findings suggest that RIP140 may be an attractive target to sensitize tumor cells to retinoid-based differentiation therapy. We discuss these data in the context of proposed models of RIP140-mediated repression.

Abstract Background The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the variance of gene expression measurements into the general empirical Bayes framework. Results We present a novel Bayesian moderated-T, which we show to perform favorably in simulations, with two real, dual-channel microarray experiments and in two controlled single-channel experiments. In simulations, the new method achieved greater power while correctly estimating the true proportion of false positives, and in the analysis of two publicly-available "spike-in" experiments, the new method performed favorably compared to all tested alternatives. We also applied our method to two experimental datasets and discuss the additional biological insights as revealed by our method in contrast to the others. The R-source code for implementing our algorithm is freely available at http://eh3.uc.edu/ibmt. Conclusion We use a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic (IBMT). The method is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. IBMT has the strength of balancing two important factors in the analysis of microarray data: the degree of independence of variances relative to the degree of identity (i.e. t-tests vs. equal variance assumption), and the relationship between variance and signal intensity. When this variance-intensity relationship is weak or does not exist, IBMT reduces to a previously described moderated t-statistic. Furthermore, our method may be directly applied to any array platform and experimental design. Together, these properties show IBMT to be a valuable option in the analysis of virtually any microarray experiment.