Ingeborg Meijer

Differential effects of the adenovirus E1A oncogene on members of the AP-1 transcription factor family.

The adenovirus early region 1A (E1A) oncogene interferes with the expression level and activity of the AP-1 transcription factor family. E1A abolished the transactivating function of AP-1 (Jun/Fos), which binds to the 12-O-tetradecanoylphorbol-13-acetate-responsive element of the collagenase gene (collTRE). In contrast, the activity of another member of the AP-1 family that binds to the c-junTRE was not repressed. The mRNA expression of the c-jun gene was, in fact, strongly elevated in various cell types expressing the E1A gene of either adenovirus type 5 (Ad5) or Ad12. The regulation of the junB gene by adenovirus E1A, on the other hand, depended both on the cell type and on the transforming adenovirus serotype. The fact that E1A-induced alterations in the repertoire of AP-1 transcription factors depend on its transforming domain in conserved region 1 suggests that the effects are relevant for the transformation process.

Reduced binding activity of transcription factor NF-κB accounts for MHC class I repression in adenovirus type 12 E1-transformed cells

The early region 1 (E1) of human adenovirus (Ad) type 12 represses the expression of major histocompatibility (MHC) Class I genes in transformed primary rodent cells. In this paper we show that both NF-kappa B and KBF1 (p50 dimer) binding activity to the H2TF1 element in the Class I promoter is reduced in Ad12-13S-E1A-transformed cells compared to Ad5E1- or Ad12-12S-E1A-transformed cells. Consistently, in Ad12E1A-13S-transformed cells the H2TF1 element does not contribute to transcriptional activity in transient expression assays, whereas it does contribute in Ad12E1A-12S-transformed cells. Therefore, the most likely explanation is that reduced binding of NF-kappa B and KBF1 to the H2TF1 element accounts for the down-regulation of MHC Class I expression in Ad12E1- and Ad12E1A-13S-transformed cells.

Europe's plans for responsible science

Drug addiction is a major global health issue, and the opioid crisis is a notable example of its catastrophic effects (1). In his News In Depth story “Chemists seek antiaddiction drugs to battle hijacked brain” (13 April, p. 139), R. F. Service discusses some promising ways to treat drug addiction, including vigabatrin and a more effective version of that drug named OV329. However, the optimistic tone of the article should be tempered by the potential side effects of these treatments. The gamma-aminobutyric acid aminotransferase (GABA-AT) enzyme plays a key role in brain signaling by inactivating GABA (2). Currently, the only licensed drug that targets this enzyme is vigabatrin, an antiepileptic that is usually reserved for severe intractable seizures (2). OV329 is mechanistically similar to vigabatrin, but—as Service explains in the News story—it binds more tightly to GABA-AT. Although more potent GABA-AT inhibitors may reduce the dopaminergic signaling that is responsible for reinforcing addiction, we should be aware of the possibility that these drugs may themselves cause physical and/or psychological dependence. Edited by Jennifer Sills LETTERS

Improving the evaluation of worldwide biomedical research output: classification method and standardised bibliometric indicators by disease

Objective Since most biomedical research focuses on a specific disease, evaluation of research output requires disease-specific bibliometric indicators. Currently used methods are insufficient. The aim of this study is to develop a method that enables detailed analysis of worldwide biomedical research output by disease. Design We applied text mining techniques and analysis of author keywords to link publications to disease groups. Fractional counting was used to quantify disease-specific biomedical research output of an institution or country. We calculated global market shares of research output as a relative measure of publication volume. We defined ‘top publications’ as the top 10% most cited publications per disease group worldwide. We used the percentage of publications from an institution or country that were top publications as an indicator of research quality. Results We were able to classify 54% of all 6.5 million biomedical publications in our database (based on Web of Science) to a disease group. We could classify 78% of these publications to a specific institution. We show that between 2000 and 2012,‘other infectious diseases’ were the largest disease group with 337 485 publications. Lifestyle diseases, cancers and mental disorders have grown most in research output. The USA was responsible for the largest number of top 10% most cited publications per disease group, with a global share of 45%. Iran (+3500%) and China (+700%) have grown most in research volume. Conclusions The proposed method provides a tool to assess biomedical research output in new ways. It can be used for evaluation of historical research performance, to support decision-making in management of research portfolios, and to allocate research funding. Furthermore, using this method to link disease-specific research output to burden of disease can contribute to a better understanding of the societal impact of biomedical research.