Tamoxifen-elicited uterotrophy: cross-species and cross-ligand analysis of the gene expression program

<p>Abstract</p> <p>Background</p> <p>Tamoxifen (TAM) is a well characterized breast cancer drug and selective estrogen receptor modulator (SERM) which also has been associated with a small increase in risk for uterine cancers. TAM's partial agonist activation of estrogen receptor has been characterized for specific gene promoters but not at the genomic level <it>in vivo</it>.Furthermore, reducing uncertainties associated with cross-species extrapolations of pharmaco- and toxicogenomic data remains a formidable challenge.</p> <p>Results</p> <p>A comparative ligand and species analysis approach was conducted to systematically assess the physiological, morphological and uterine gene expression alterations elicited across time by TAM and ethynylestradiol (EE) in immature ovariectomized Sprague-Dawley rats and C57BL/6 mice. Differential gene expression was evaluated using custom cDNA microarrays, and the data was compared to identify conserved and divergent responses. 902 genes were differentially regulated in all four studies, 398 of which exhibit identical temporal expression patterns.</p> <p>Conclusion</p> <p>Comparative analysis of EE and TAM differentially expressed gene lists suggest TAM regulates no unique uterine genes that are conserved in the rat and mouse. This demonstrates that the partial agonist activities of TAM extend to molecular targets in regulating only a subset of EE-responsive genes. Ligand-conserved, species-divergent expression of carbonic anhydrase 2 was observed in the microarray data and confirmed by real time PCR. The identification of comparable temporal phenotypic responses linked to related gene expression profiles demonstrates that systematic comparative genomic assessments can elucidate important conserved and divergent mechanisms in rodent estrogen signalling during uterine proliferation.</p

Minimal information about T cell assays: the process of reaching the community of T cell immunologists in cancer and beyond

Many assays to evaluate the nature, breadth, and quality of antigen-specific T cell responses are currently applied in human medicine. In most cases, assay-related protocols are developed on an individual laboratory basis, resulting in a large number of different protocols being applied worldwide. Together with the inherent complexity of cellular assays, this leads to unnecessary limitations in the ability to compare results generated across institutions. Over the past few years a number of critical assay parameters have been identified which influence test performance irrespective of protocol, material, and reagents used. Describing these critical factors as an integral part of any published report will both facilitate the comparison of data generated across institutions and lead to improvements in the assays themselves. To this end, the Minimal Information About T Cell Assays (MIATA) project was initiated. The objective of MIATA is to achieve a broad consensus on which T cell assay parameters should be reported in scientific publications and to propose a mechanism for reporting these in a systematic manner. To add maximum value for the scientific community, a step-wise, open, and field-spanning approach has been taken to achieve technical precision, user-friendliness, adequate incorporation of concerns, and high acceptance among peers. Here, we describe the past, present, and future perspectives of the MIATA project. We suggest that the approach taken can be generically applied to projects in which a broad consensus has to be reached among scientists working in fragmented fields, such as immunology. An additional objective of this undertaking is to engage the broader scientific community to comment on MIATA and to become an active participant in the project

Unsupervised assessment of microarray data quality using a Gaussian mixture model

<p>Abstract</p> <p>Background</p> <p>Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny.</p> <p>Results</p> <p>We show how an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and the naïve Bayes model can be used to automate microarray quality assessment. The method is flexible and can be easily adapted to accommodate alternate quality statistics and platforms. We evaluate our approach using Affymetrix 3' gene expression and exon arrays and compare the performance of this method to a similar supervised approach.</p> <p>Conclusion</p> <p>This research illustrates the efficacy of an unsupervised classification approach for the purpose of automated microarray data quality assessment. Since our approach requires only unannotated training data, it is easy to customize and to keep up-to-date as technology evolves. In contrast to other "black box" classification systems, this method also allows for intuitive explanations.</p

Leadership = Communication? The relations of leaders' communication styles with leadership styles, knowledge sharing and leadership outcomes

Purpose: The purpose of this study was to investigate the relations between leaders' communication styles and charismatic leadership, human-oriented leadership (leader's consideration), task-oriented leadership (leader's initiating structure), and leadership outcomes. Methodology: A survey was conducted among 279 employees of a governmental organization. The following six main communication styles were operationalized: verbal aggressiveness, expressiveness, preciseness, assuredness, supportiveness, and argumentativeness. Regression analyses were employed to test three main hypotheses. Findings: In line with expectations, the study showed that charismatic and human-oriented leadership are mainly communicative, while task-oriented leadership is significantly less communicative. The communication styles were strongly and differentially related to knowledge sharing behaviors, perceived leader performance, satisfaction with the leader, and subordinate's team commitment. Multiple regression analyses showed that the leadership styles mediated the relations between the communication styles and leadership outcomes. However, leader's preciseness explained variance in perceived leader performance and satisfaction with the leader above and beyond the leadership style variables. Implications: This study offers potentially invaluable input for leadership training programs by showing the importance of leader's supportiveness, assuredness, and preciseness when communicating with subordinates. Originality/value: Although one of the core elements of leadership is interpersonal communication, this study is one of the first to use a comprehensive communication styles instrument in the study of leadership. © 2009 The Author(s)

Development and Analysis of an Adverse Outcome Pathway Network for Human Neurotoxicity

An adverse outcome pathway (AOP) network is an attempt to represent the complexity of systems toxicology. This study illustrates how an AOP network can be derived and analysed in terms of its topological features to guide research and support chemical risk assessment. A four-step workflow describing general design principles and applied design principles were established and implemented. An AOP network linking nine linear AOPs was mapped and made available in AOPXplorer. The resultant AOP network was modelled and analysed in terms of its topological features, including level of degree, eccentricity and betweenness centrality. Several well connected KEs were identified, and cell injury/death was established as the most hyperlinked KE across the network. The derived network expands the utility of linear AOPs to better understand signalling pathways involved in developmental and adult/aging neurotoxicity. The results provide a solid basis to guide the development of in vitro test method batteries, as well as further quantitative modelling of key events (KEs) and key event relationships (KERs) in the AOP network, with an eventual aim to support hazard characterisation and chemical risk assessment