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Abstract Secondary activation of the endothelin system is thought to be involved in toxic liver injury. This study tested the hypothesis that dual endothelin-converting enzyme / neutral endopeptidase blockade might be able to attenuate acute toxic liver injury. Male Sprague-Dawley rats were implanted with subcutaneous minipumps to deliver the novel compound SLV338 (10 mg/kg*d) or vehicle. Four days later they received two intraperitoneal injections of D-galactosamine (1.3 g/kg each) or vehicle at an interval of 12 hours. The animals were sacrificed 48 hours after the first injection. Injection of D-galactosamine resulted in very severe liver injury, reflected by strongly elevated plasma liver enzymes, hepatic necrosis and inflammation, and a mortality rate of 42.9 %. SLV338 treatment did not show any significant effect on the extent of acute liver injury as judged from plasma parameters, hepatic histology and mortality. Plasma measurements of SLV338 confirmed adequate drug delivery. Plasma concentrations of big endothelin-1 and endothelin-1 were significantly elevated in animals with liver injury (5-fold and 62-fold, respectively). Plasma endothelin-1 was significantly correlated with several markers of liver injury. SLV338 completely prevented the rise of plasma big endothelin-1 (p < 0.05) and markedly attenuated the rise of endothelin-1 (p = 0.055). In conclusion, dual endothelin-converting enzyme / neutral endopeptidase blockade by SLV338 did not significantly attenuate D-galactosamine-induced acute liver injury, although it largely prevented the activation of the endothelin system. An evaluation of SLV338 in a less severe model of liver injury would be of interest, since very severe intoxication might not be relevantly amenable to pharmacological interventions.

The debate about possible adverse effects of bisphenol A (BPA) has been ongoing for decades.BisphenolF (BPF) and S (BPS) have been suggested as “safer” alternatives. In the present study we used hepatocyte-like cells (HLCs) derived from the human embryonic stem cell lines Man12 and H9 to compare the three bisphenol derivatives. Stem cell-derived progenitors were produced using an established system andwere exposed to BPA, BPF and BPS for 8 days during their transition to HLCs. Subsequently, we examined cell viability, inhibition of cytochrome P450 (CYP) activity, and genome-wide RNA profiles. Sub-cytotoxic, inhibitory concentrations (IC50) of CYP3A were 20, 9.5 and 25 μM for BPA, BPF and BPS in Man12 derived HLCs, respectively. The corresponding concentrations for H9-derived HLCs were 19, 29 and 31 μM. These IC50 concentrations were used to study global expression changes in this in vitrostudy and are higher than unconjugated BPA in serum of the general population. A large overlap of up-as well as downregulated genes induced by the three bisphenol derivatives was seen. This is at least 28-fold higher compared to randomly expected gene expression changes. Moreover, highly significant correlations of expression changes induced by the three bisphenol derivatives were obtained in pairwise comparisons. Dysregulated genes were associated with reduced metabolic function, cellular differentiation, embryonic development, cell survival and apoptosis. In conclusion, no major differences in cytochrome inhibitory activities of BPA, BPF and BPS were observed and gene expression changes showed a high degree of similarity. [ABSTRACT FROM AUTHOR]

Objectives: We introduce methods for the exploratory analysis of microarray data, especially focusing on cluster algorithms. Benefits and problems are discussed.Methods: We describe application and suitability of unsupervised learning methods for the classification of gene expression data. Cluster algorithms are treated in more detail, including assessment of cluster quality.Results: When dealing with microarray data, most cluster algorithms must be applied with caution. As long as the structure of the true generating models of such data is not fully understood, the use of simple algorithms seems to be more appropriate than the application of complex black-box algorithms. New methods explicitly targeted to the analysis of microarray data are increasingly being developed in order to increase the amount of useful information extracted from the experiments.Conclusions: Unsupervised methods can be a helpful tool for the analysis of microarray data, but a critical choice of the algorithm and a careful interpretation of the results are required in order to avoid false conclusions. [ABSTRACT FROM AUTHOR]

Many different models for the analysis of high-dimensional survival data have been developed over the past years. While some of the models and implementations come with an internal parameter tuning automatism, others require the user to accurately adjust defaults, which often feels like a guessing game. Exhaustively trying out all model and parameter combinations will quickly become tedious or infeasible in computationally intensive settings, even if parallelization is employed. Therefore, we propose to use modern algorithm configuration techniques, e.g. iterated F-racing, to efficiently move through the model hypothesis space and to simultaneously configure algorithm classes and their respective hyperparameters. In our application we study four lung cancer microarray data sets. For these we configure a predictor based on five survival analysis algorithms in combination with eight feature selection filters. We parallelize the optimization and all comparison experiments with theBatchJobsandBatchExperimentsR packages. [ABSTRACT FROM AUTHOR]

Abstract Background Inhibitors targeting the cell cycle-regulated aurora kinase A (AURKA) are currently being developed. Here, we examine the prognostic impact of AURKA in node-negative breast cancer patients without adjuvant systemic therapy (n = 766). Methods AURKA was analyzed using microarray-based gene-expression data from three independent cohorts of node-negative breast cancer patients. In multivariate Cox analyses, the prognostic impact of age, histological grade, tumor size, estrogen receptor (ER), and HER2 were considered. Results Patients with higher AURKA expression had a shorter metastasis-free survival (MFS) in the Mainz (HR 1.93; 95% CI 1.34 – 2.78; P < 0.001), Rotterdam (HR 1.95; 95% CI 1.45– 2.63; P Conclusions AURKA is associated with worse prognosis in estrogen receptor positive breast carcinomas. Patients with the highest AURKA expression (>75% percentile) have a particularly bad prognosis and may profit from therapy with AURKA inhibitors.

Abstract Background An important application of high dimensional gene expression measurements is the risk prediction and the interpretation of the variables in the resulting survival models. A major problem in this context is the typically large number of genes compared to the number of observations (individuals). Feature selection procedures can generate predictive models with high prediction accuracy and at the same time low model complexity. However, interpretability of the resulting models is still limited due to little knowledge on many of the remaining selected genes. Thus, we summarize genes as gene groups defined by the hierarchically structured Gene Ontology (GO) and include these gene groups as covariates in the hazard regression models. Since expression profiles within GO groups are often heterogeneous, we present a new method to obtain subgroups with coherent patterns. We apply preclustering to genes within GO groups according to the correlation of their gene expression measurements. Results We compare Cox models for modeling disease free survival times of breast cancer patients. Besides classical clinical covariates we consider genes, GO groups and preclustered GO groups as additional genomic covariates. Survival models with preclustered gene groups as covariates have similar prediction accuracy as models built only with single genes or GO groups. Conclusions The preclustering information enables a more detailed analysis of the biological meaning of covariates selected in the final models. Compared to models built only with single genes there is additional functional information contained in the GO annotation, and compared to models using GO groups as covariates the preclustering yields coherent representative gene expression profiles.

Abstract Background The cortical cytoskeleton network connects the actin cytoskeleton to various membrane proteins, influencing cell adhesion, polarity, migration and response to extracellular signals. Previous studies have suggested changes in the expression of specific components in prostate cancer, especially of 4.1 proteins (encoded by EPB41 genes) which form nodes in this network. Methods Expression of EPB41L1, EPB41L2, EPB41L3 (protein: 4.1B), EPB41L4B (EHM2), EPB41L5, EPB49 (dematin), VIL2 (ezrin), and DLG1 (summarized as „cortical cytoskeleton" genes) as well as ERG was measured by quantitative RT-PCR in a well-characterized set of 45 M0 prostate adenocarcinoma and 13 benign tissues. Hypermethylation of EPB41L3 and GSTP1 was compared in 93 cancer tissues by methylation-specific PCR. Expression of 4.1B was further studied by immunohistochemistry. Results EPB41L1 and EPB41L3 were significantly downregulated and EPB41L4B was upregulated in cancer tissues. Low EPB41L1 or high EPB41L4B expression were associated with earlier biochemical recurrence. None of the other cortical cytoskeleton genes displayed expression changes, in particular EPB49 and VIL2, despite hints from previous studies. EPB41L3 downregulation was significantly associated with hypermethylation of its promoter and strongly correlated with GSTP1 hypermethylation. Protein 4.1B was detected most strongly in the basal cells of normal prostate epithelia. Its expression in carcinoma cells was similar to the weaker one in normal luminal cells. EPB41L3 downregulation and EPB41L4B upregulation were essentially restricted to the 22 cases with ERG overexpression. Expression changes in EPB41L3 and EPB41L4B closely paralleled those previously observed for the extracellular matrix genes FBLN1 and SPOCK1, respectively. Conclusions Specific changes in the cortical cytoskeleton were observed during prostate cancer progression. They parallel changes in the expression of extracellular matrix components and all together appear to be associated with oncogenic ERG overexpression. We hypothesize that these alterations may contribute to the increased invasivity conferred to prostate cancer cells by ERG deregulation.