The EPI bioassay identifies natural compounds with estrogenic activity that are potent inhibitors of androgenic pathways in human prostate stromal and epithelial cells

The reactive stromal phenotype is an important factor for prostate cancer progression and may be a new target for treatment and prevention. A new high efficiency preclinical protocol, the EPI bioassay, reflects the interaction of endocrine, paracrine and immune, (EPI) factors on induced androgen metabolism in human prostate reactive stroma. The bioassay is based on co-culturing human primary prostate stromal cells and LAPC-4 prostatic adenocarcinoma cells in a downscaled format of 96-well-plates for testing multiple doses of multiple target compounds. Metabolism of dehydroepiandrosterone (DHEA) with or without TGFβ1-induced stimulation (D+T) of the reactive stroma phenotype was assessed by increased testosterone in the media and PSA production of the epithelial prostate cancer cells. Using the non-metabolizable androgen R1881, effects from direct androgen action were distinguished from stromal androgen production from DHEA. Stromal cell androgenic bioactivity was confirmed using conditioned media from D+T-treated stromal cell monocultures in an androgen-inducible AR screening assay. We further showed that both agonists to estrogen receptor (ER), DPN (ERβ) and PPT (ERα), as well as estrogenic natural compounds including soy isoflavones attenuated D+T-induced PSA production. Studies with the pure ER agonists showed that activating either ERα or ERβ could inhibit both D+T-mediated and R1881-mediated PSA production with the D+T effect being more pronounced. In conclusion, natural compounds with estrogenic activity and pure ER agonists are very potent inhibitors of stromal conversion of DHEA to androgenic metabolites. More studies are needed to characterize the mechanisms involved in estrogenic modulation of the endocrine-immune-paracrine balance of the prostate microenvironment.     

Molecular mechanisms involved in the regulation of neuritogenesis by estradiol: Recent advances

This review analyzes the signaling mechanisms activated by estradiol to regulate neuritogenesis in several neuronal populations. Estradiol regulates axogenesis by the activation of the mitogen activated protein kinase (MAPK) cascade through estrogen receptor α located in the plasma membrane. In addition, estradiol regulates MAPK signaling via the activation of protein kinase C and by increasing the expression of brain derived neurotrophic factor and tyrosine kinase receptor B. Estradiol also interacts with the signaling of insulin-like growth factor-I receptor through estrogen receptor α, modulating the phosphoinositide-3 kinase signaling pathway, which contributes to the stabilization of microtubules. Finally, estradiol modulates dendritogenesis by the inhibition of Notch signaling, by a mechanism that, at least in hippocampal neurons, is mediated by G-protein coupled receptor 30. This article is part of a Special Issue entitled 'Neurosteroids'.     

Development of isoform selective PI3-kinase inhibitors as pharmacological tools for elucidating the PI3K pathway

Using a parallel synthesis approach to target a non-conserved region of the PI3K catalytic domain a pan-PI3K inhibitor 1 was elaborated to provide alpha, delta and gamma isoform selective Class I PI3K inhibitors 21, 24, 26 and 27. The compounds had good cellular activity and were selective against protein kinases and other members of the PI3K superfamily including mTOR and DNA-PK.     

New stochastic carcinogenesis model with covariates: an approach involving intracellular barrier mechanisms

In this paper we present a new multiple-pathway stochastic model of carcinogenesis with potential of predicting individual incidence risks on the basis of biomedical measurements. The model incorporates the concept of intracellular barrier mechanisms in which cell malignization occurs due to an inefficient operation of barrier cell mechanisms, such as antioxidant defense, repair systems, and apoptosis. Mathematical formalism combines methodological innovations of mechanistic carcinogenesis models and stochastic process models widely used in studying biodemography of aging and longevity. An advantage of the modeling approach is in the natural combining of two types of measures expressed in terms of model parameters: age-specific hazard rate and means of barrier states. Results of simulation studies allow us to conclude that the model parameters can be estimated in joint analyses of epidemiological data and newly collected data on individual biomolecular measurements of barrier states. Respective experimental designs for such measurements are suggested and discussed. An analytical solution is obtained for the simplest design when only age-specific incidence rates are observed. Detailed comparison with TSCE model reveals advantages of the approach such as the possibility to describe decline in risk at advanced ages, possibilities to describe heterogeneous system of intermediate cells, and perspectives for individual prognoses of cancer risks. Application of the results to fit the SEER data on cancer risks demonstrates a strong predictive power of the model. Further generalizations of the model, opportunities to measure barrier systems, biomedical and mathematical aspects of the new model are discussed.     

Homologous recombination is facilitated in starving populations of Pseudomonas putida by phenol stress and affected by chromosomal location of the recombination target

Homologous recombination (HR) has a major impact in bacterial evolution. Most of the knowledge about the mechanisms and control of HR in bacteria has been obtained in fast growing bacteria. However, in their natural environment bacteria frequently meet adverse conditions which restrict the growth of cells. We have constructed a test system to investigate HR between a plasmid and a chromosome in carbon-starved populations of the soil bacterium Pseudomonas putida restoring the expression of phenol monooxygenase gene pheA. Our results show that prolonged starvation of P. putida in the presence of phenol stimulates HR. The emergence of recombinants on selective plates containing phenol as an only carbon source for the growth of recombinants is facilitated by reactive oxygen species and suppressed by DNA mismatch repair enzymes. Importantly, the chromosomal location of the HR target influences the frequency and dynamics of HR events. In silico analysis of binding sites of nucleoid-associated proteins (NAPs) revealed that chromosomal DNA regions which flank the test system in bacteria exhibiting a lower HR frequency are enriched in binding sites for a subset of NAPs compared to those which express a higher frequency of HR. We hypothesize that the binding of these proteins imposes differences in local structural organization of the genome that could affect the accessibility of the chromosomal DNA to HR processes and thereby the frequency of HR.     

Do pollination syndromes cause modularity and predict interactions in a pollination network in tropical high‐altitude grasslands?

The concept of pollination syndromes has been widely questioned, since plant–pollinator interactions have proved to be more generalist than was previously thought. We examined whether the network of a tropical high‐altitude grassland contained groups of plants and pollinators that interact preferentially with each other. A general binary matrix was created. To assess the robustness of myophily, in all analyses we considered: 1) the whole network, 2) the network after the wasps were removed, and 3) the network after the flies were removed. For each network we evaluated whether: 1) the observed interactions were more related to syndromes than expected by chance, compared to an expected matrix; 2) there was a modular structure; 3) the modules found were more related to syndromes than expected by chance, compared to another expected matrix; 4) the syndromes were equally robust. For this analysis, the general matrix was subdivided into smaller matrices that included each pollination syndrome separately. To test the influence of the functional groups of pollinators and the phylogeny of plants, in addition to the general matrix, we also considered the first expected matrix, a quantitative functional group and a plant phylogeny matrix. The pollination syndromes determined the pattern of interactions in the network: 69% of the total interactions resulted from the functional group of pollinators predicted by the plant syndrome. The network showed greater modularity (13 modules) than expected by chance, mostly consisting of the expected functional groups of pollinators and plant syndromes. The modules were associated with pollination syndromes more than was predicted by chance. Most of the variation in interactions was explained by functional groups of pollinators or by plant syndromes. Plant phylogeny did not account for a significant amount of variation in the interactions. Our findings support the concept of pollination syndromes. However, the interactions were not equally predicted by different pollination syndromes, and the accuracy of the prediction was strongest for ornithophily and melittophily.