A single transgene locus triggers both transcriptional and post-transcriptional silencing through double-stranded RNA production

Silencing of a target locus by an unlinked silencing locus can result from transcription inhibition (transcriptional gene silencing; TGS) or mRNA degradation (post-transcriptional gene silencing; PTGS), owing to the production of double-stranded RNA (dsRNA) corresponding to promoter or transcribed sequences, respectively. The involvement of distinct cellular components in each process suggests that dsRNA-induced TGS and PTGS likely result from the diversification of an ancient common mechanism. However, a strict comparison of TGS and PTGS has been difficult to achieve because it generally relies on the analysis of distinct silencing loci. We describe a single transgene locus that triggers both TGS and PTGS, owing to the production of dsRNA corresponding to promoter and transcribed sequences of different target genes. We describe mutants and epigenetic variants derived from this locus and propose a model for the production of dsRNA. Also, we show that PTGS, but not TGS, is graft-transmissible, which together with the sensitivity of PTGS, but not TGS, to RNA viruses that replicate in the cytoplasm, suggest that the nuclear compartmentalization of TGS is responsible for cell-autonomy. In contrast, we contribute local and systemic trafficking of silencing signals and sensitivity to viruses to the cytoplasmic steps of PTGS and to amplification steps that require high levels of target mRNAs

In de studeervertrekken van de Statenvertalers: Het inwendige wordingsproces van het Nieuwe Testament van de Statenvertaling

Hollander, A.A. den [Promotor]Vries, L.J. de [Promotor

Response of spontaneously hypertensive rats to inhalation of fine and ultrafine particles from traffic: experimental controlled study

BACKGROUND: Many epidemiological studies have shown that mass concentrations of ambient particulate matter (PM) are associated with adverse health effects in the human population. Since PM is still a very crude measure, this experimental study has explored the role of two distinct size fractions: ultrafine (<0.15 μm) and fine (0.15- 2.5 μm) PM. In a series of 2-day inhalation studies, spontaneously hypersensitive (SH) rats were exposed to fine, concentrated, ambient PM (fCAP) at a city background location or a combination of ultrafine and fine (u+fCAP) PM at a location dominated by traffic. We examined the effect on inflammation and both pathological and haematological indicators as markers of pulmonary and cardiovascular injury. Exposure concentrations ranged from 399 μg/m(3 )to 3613 μg/m(3 )for fCAP and from 269μg/m(3 )to 556 μg/m(3 )for u+fCAP. RESULTS: Ammonium, nitrate, and sulphate ions accounted for 56 ± 16% of the total fCAP mass concentrations, but only 17 ± 6% of the u+fCAP mass concentrations. Unambiguous particle uptake in alveolar macrophages was only seen after u+fCAP exposures. Neither fCAP nor u+fCAP induced significant changes of cytotoxicity or inflammation in the lung. However, markers of oxidative stress (heme oxygenase-1 and malondialdehyde) were affected by both fCAP and u+fCAP exposure, although not always significantly. Additional analysis revealed heme oxygenase-1 (HO-1) levels that followed a nonmonotonic function with an optimum at around 600 μg/m(3 )for fCAP. As a systemic response, exposure to u+fCAP and fCAP resulted in significant decreases of the white blood cell concentrations. CONCLUSION: Minor pulmonary and systemic effects are observed after both fine and ultrafine + fine PM exposure. These effects do not linearly correlate with the CAP mass. A greater component of traffic CAP and/or a larger proportion ultrafine PM does not strengthen the absolute effects

Individual Risk

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90540/1/j.1751-7176.2012.00592.x.pd

Molecular Signature of Asthma-Enhanced Sensitivity to CuO Nanoparticle Aerosols from 3D Cell Model

More than 5% of any population suffers from asthma, and there are indications that these individuals are more sensitive to nanoparticle aerosols than the healthy population. We used an air-liquid interface model of inhalation exposure to investigate global transcriptomic responses in reconstituted three-dimensional airway epithelia of healthy and asthmatic subjects exposed to pristine (nCuO) and carboxylated (nCuO(COOH)) copper oxide nanoparticle aerosols. A dose-dependent increase in cytotoxicity (highest in asthmatic donor cells) and pro-inflammatory signaling within 24 h confirmed the reliability and sensitivity of the system to detect acute inhalation toxicity. Gene expression changes between nanoparticle-exposed versus air-exposed cells were investigated. Hierarchical clustering based on the expression profiles of all differentially expressed genes (DEGs), cell-death-associated DEGs (567 genes), or a subset of 48 highly overlapping DEGs categorized all samples according to "exposure severity", wherein nanoparticle surface chemistry and asthma are incorporated into the dose-response axis. For example, asthmatics exposed to low and medium dose nCuO clustered with healthy donor cells exposed to medium and high dose nCuO, respectively. Of note, a set of genes with high relevance to mucociliary clearance were observed to distinctly differentiate asthmatic and healthy donor cells. These genes also responded differently to nCuO and nCuO(COOH) nanoparticles. Additionally, because response to transition-metal nanoparticles was a highly enriched Gene Ontology term (FDR 8 X 10(-13)) from the subset of 48 highly overlapping DEGs, these genes may represent biomarkers to a potentially large variety of metal/metal oxide nanoparticles.Peer reviewe

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer