Gene expression analysis in Mayak workers with prolonged occupational radiation exposure.

The authors evaluated gene expression in the peripheral blood in relation to occupational exposure in Mayak workers to find out about the existence of a permanent post exposure signature. Workers were exposed to combined incorporated ²³9Pu and external gamma rays (n = 82) or to external gamma rays only (n = 18), and 50 unexposed individuals served as controls. Peripheral blood was taken from workers older than 70 y. RNA was isolated, converted into cDNA, and stored at -20°C. A two-stage study design was performed focusing on examinations on the transcriptional (mRNA) and post-transcriptional level (microRNA). In the first stage, 40 samples were identified for screening purposes and selection of candidate genes. For examinations on the transcriptional level, whole genome microarrays and qRT-PCR were employed on the post-transcriptional level (667 microRNAs). Candidate genes were assessed by (1) introducing a twofold difference in gene expression over the reference group and (2) showing a significant p-value using the Kruskal-Wallis test. From 42,545 transcripts of the whole genome microarray, 376 candidate genes (80 up-regulated and 296 down-regulated relative to the reference group) were selected. Expression of almost all of these genes (70-98%) appeared significantly associated with internal ²³9Pu and to a lesser extent were associated with external gamma-ray exposure (2-30%). Associations in the same direction were found for 45 microRNAs. Although both exposures led to modulations of different gene sets in different directions, the authors could detect no differences in gene set enrichment analysis.

Bayesian model selection validates a biokinetic model for zirconium processing in humans.

BACKGROUND: In radiation protection, biokinetic models for zirconium processing are of crucial importance in dose estimation and further risk analysis for humans exposed to this radioactive substance. They provide limiting values of detrimental effects and build the basis for applications in internal dosimetry, the prediction for radioactive zirconium retention in various organs as well as retrospective dosimetry. Multi-compartmental models are the tool of choice for simulating the processing of zirconium. Although easily interpretable, determining the exact compartment structure and interaction mechanisms is generally daunting. In the context of observing the dynamics of multiple compartments, Bayesian methods provide efficient tools for model inference and selection. RESULTS: We are the first to apply a Markov chain Monte Carlo approach to compute Bayes factors for the evaluation of two competing models for zirconium processing in the human body after ingestion. Based on in vivo measurements of human plasma and urine levels we were able to show that a recently published model is superior to the standard model of the International Commission on Radiological Protection. The Bayes factors were estimated by means of the numerically stable thermodynamic integration in combination with a recently developed copula-based Metropolis-Hastings sampler. CONCLUSIONS: In contrast to the standard model the novel model predicts lower accretion of zirconium in bones. This results in lower levels of noxious doses for exposed individuals. Moreover, the Bayesian approach allows for retrospective dose assessment, including credible intervals for the initially ingested zirconium, in a significantly more reliable fashion than previously possible. All methods presented here are readily applicable to many modeling tasks in systems biology.

Tissue-specific target analysis of disease-associated microRNAs in human signaling pathways.

MicroRNAs are a large class of post-transcriptional regulators that bind to the 3' untranslated region of messenger RNAs. They play a critical role in many cellular processes and have been linked to the control of signal transduction pathways. Recent studies indicate that microRNAs can function as tumor suppressors or even as oncogenes when aberrantly expressed. For more general insights of disease-associated microRNAs, we analyzed their impact on human signaling pathways from two perspectives. On a global scale, we found a core set of signaling pathways with enriched tissue-specific microRNA targets across diseases. The function of these pathways reflects the affinity of microRNAs to regulate cellular processes associated with apoptosis, proliferation or development. Comparing cancer and non-cancer related microRNAs, we found no significant differences between both groups. To unveil the interaction and regulation of microRNAs on signaling pathways locally, we analyzed the cellular location and process type of disease-associated microRNA targets and proteins. While disease-associated proteins are highly enriched in extracellular components of the pathway, microRNA targets are preferentially located in the nucleus. Moreover, targets of disease-associated microRNAs preferentially exhibit an inhibitory effect within the pathways in contrast to disease proteins. Our analysis provides systematic insights into the interaction of disease-associated microRNAs and signaling pathways and uncovers differences in cellular locations and process types of microRNA targets and disease-associated proteins.

PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes.

In recent years, microRNAs have been shown to play important roles in physiological as well as malignant processes. The PhenomiR database http://mips.helmholtz-muenchen.de/phenomir provides data from 542 studies that investigate deregulation of microRNA expression in diseases and biological processes as a systematic, manually curated resource. Using the PhenomiR dataset, we could demonstrate that, depending on disease type, independent information from cell culture studies contrasts with conclusions drawn from patient studies.