Nonmalignant AR-positive prostate epithelial cells and cancer cells respond differently to androgen

Prostate cancer research suffers from the lack of suitable models to study the role of normal cells in prostate carcinogenesis. To address this challenge, we developed a cell line model mimicking luminal prostate epithelial cells by modifying the immortalized prostate epithelial cell line RWPE-1 to constitutively express the androgen receptor (AR). RWPE-1-AR cells express known AR target genes, and exhibit coexpression of luminal and basal markers characteristic of transient amplifying cells, and an RNA signature resembling prostate luminal progenitor cells. Under unstimulated conditions, constitutive AR expression does not have a biologically significant effect on the proliferation of RWPE-1 cells, but when stimulated by androgens, growth is retarded. The transcriptional response of RWPE-1-AR cells to androgen stimulation involves suppression of the growth-related KRAS pathway and is thus markedly different from that of the prostate cancer cell line LNCaP and its derivative AR-overexpressing LNCaP-ARhi cells, in which growth- and cancer-related pathways are upregulated. Hence, the nonmalignant AR-positive RWPE-1-AR cell line model could be used to study the transformation of the prostate epithelium.publishedVersionPeer reviewe

Functional human cell-based vascularised cardiac tissue model for biomedical research and testing

Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC) are widely used in in vitro biomedical research and testing. However, fully matured, adult cardiomyocyte characteristics have not been achieved. To improve the maturity and physiological relevance of hiPSC-derived cardiomyocytes, we co-cultured them with preconstructed vascular-like networks to form a functional, human cell-based cardiac tissue model. The morphology and gene expression profiles indicated advanced maturation in the cardiac tissue model compared to those of a cardiomyocyte monoculture. The cardiac tissue model’s functionality was confirmed by measuring the effects of 32 compounds with multielectrode array and comparing results to human data. Our model predicted the cardiac effects with a predictive accuracy of 91%, sensitivity of 90% and specificity of 100%. The correlation between the effective concentration (EC50) and the reported clinical plasma concentrations was 0.952 (R2 = 0.905). The developed advanced human cell-based cardiac tissue model showed characteristics and functionality of human cardiac tissue enabling accurate transferability of gained in vitro data to human settings. The model is standardized and thus, it would be highly useful in biomedical research and cardiotoxicity testing.publishedVersionPeer reviewe

Balance between Noise and Information Flow Maximizes Set Complexity of Network Dynamics

<div><p>Boolean networks have been used as a discrete model for several biological systems, including metabolic and genetic regulatory networks. Due to their simplicity they offer a firm foundation for generic studies of physical systems. In this work we show, using a measure of context-dependent information, set complexity, that prior to reaching an attractor, random Boolean networks pass through a transient state characterized by high complexity. We justify this finding with a use of another measure of complexity, namely, the statistical complexity. We show that the networks can be tuned to the regime of maximal complexity by adding a suitable amount of noise to the deterministic Boolean dynamics. In fact, we show that for networks with Poisson degree distributions, all networks ranging from subcritical to slightly supercritical can be tuned with noise to reach maximal set complexity in their dynamics. For networks with a fixed number of inputs this is true for near-to-critical networks. This increase in complexity is obtained at the expense of disruption in information flow. For a large ensemble of networks showing maximal complexity, there exists a balance between noise and contracting dynamics in the state space. In networks that are close to critical the intrinsic noise required for the tuning is smaller and thus also has the smallest effect in terms of the information processing in the system. Our results suggest that the maximization of complexity near to the state transition might be a more general phenomenon in physical systems, and that noise present in a system may in fact be useful in retaining the system in a state with high information content.</p> </div

Poisson networks can be set a noise level that maximizes the steady-state set complexity.

<p>The color of the plot shows the steady-state set complexity of Boolean network dynamics for both Poisson networks (left) and fixed- networks with (right) as functions of sensitivity and flip probability . For each simulation, a median of set complexities is taken over time steps . Further averaged, the color shows the median of simulations, smoothened with bilinear interpolation. The lower panels show the maximum of the plane, taken over the flip probability.</p

The propagation of NCD distributions explains the time course of the set complexity.

<p>The panels show the distributions of NCD values on interval in noiseless (left), moderately noisy (middle) and highly noisy (right) Poisson networks with . The time instant of observation grows downwards with the figures plotted: The curve plotted for corresponds to the distribution of off-diagonal elements of NCD matrix , while the curve for corresponds to , and so forth. The distributions are pooled across 100 network realizations and smoothened with a Gaussian filter with standard deviation 0.02. The mean of the NCD distribution in noiseless critical networks (left) passes 0.5 around time instant , as expected from the complexity peak at in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056523#pone-0056523-g001" target="_blank">Fig. 1</a>. The small peaks of noiseless networks in the regime of low NCD correspond to point-attractors. In these attractors the state remains constant, and since the Kolmogorov complexity of a dublicated string is not much higher than that of the original (), the resulting NCD values are very small. The mean of the NCD distribution in Poisson networks with moderate noise (middle) approaches 0.5 as time passes, accounting for the high set complexity values in the regime of large in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056523#pone-0056523-g002" target="_blank">Fig. 2</a>. In highly noisy networks (right) the NCD distributions have only values that are notably higher than 0.5 due to the excess of randomness, and hence the low set complexity value for these networks in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056523#pone-0056523-g002" target="_blank">Fig. 2</a>.</p