Data of transcriptional effects of the merbarone-mediated inhibition of TOP2.

Type II DNA topoisomerases relax topological stress by transiently gating DNA passage in a controlled cut-and-reseal mechanism that affects both DNA strands. Therefore, they are essential to overcome topological problems associated with DNA metabolism. Their aberrant activity results in the generation of DNA double-strand breaks, which can seriously compromise cell survival and genome integrity. Here, we profile the transcriptome of human-telomerase-immortalized retinal pigment epithelial 1 (RPE-1) cells when treated with merbarone, a drug that catalytically inhibits type II DNA topoisomerases. We performed RNA-Seq after 4 and 8 h of merbarone treatment and compared transcriptional profiles versus untreated samples. We report raw sequencing data together with lists of gene counts and differentially expressed genes.

Role of a cryptic tRNA gene operon in survival under translational stress.

As compared to eukaryotes, bacteria have a reduced tRNA gene set encoding between 30 and 220 tRNAs. Although in most bacterial phyla tRNA genes are dispersed in the genome, many species from distinct phyla also show genes forming arrays. Here, we show that two types of arrays with distinct evolutionary origins exist. This work focuses on long tRNA gene arrays (L-arrays) that encompass up to 43 genes, which disseminate by horizontal gene transfer and contribute supernumerary tRNA genes to the host. Although in the few cases previously studied these arrays were reported to be poorly transcribed, here we show that the L-array of the model cyanobacterium Anabaena sp. PCC 7120, encoding 23 functional tRNAs, is largely induced upon impairment of the translation machinery. The cellular response to this challenge involves a global reprogramming of the transcriptome in two phases. tRNAs encoded in the array are induced in the second phase of the response, directly contributing to cell survival. Results presented here show that in some bacteria the tRNA gene set may be partitioned between a housekeeping subset, which constantly sustains translation, and an inducible subset that is generally silent but can provide functionality under particular conditions.

Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks.

Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs. In this work, gene co-expression networks were reconstructed from RNA-Seq expression data with the aim of analyzing the time-resolved effects of gene Ly6E in the immune response against the coronavirus responsible for murine hepatitis (MHV). Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E Δ H S C compared to wild type animals. Results show that Ly6E ablation at hematopoietic stem cells (HSCs) leads to a progressive impaired immune response in both liver and spleen. Specifically, depletion of the normal leukocyte mediated immunity and chemokine signaling is observed in the liver of Ly6E Δ H S C mice. On the other hand, the immune response in the spleen, which seemed to be mediated by an intense chromatin activity in the normal situation, is replaced by ECM remodeling in Ly6E Δ H S C mice. These findings, which require further experimental characterization, could be extrapolated to other coronaviruses and motivate the efforts towards novel antiviral approaches.

Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach.

Gene Networks (GN), have emerged as an useful tool in recent years for the analysis of different diseases in the field of biomedicine. In particular, GNs have been widely applied for the study and analysis of different types of cancer. In this context, Lung carcinoma is among the most common cancer types and its short life expectancy is partly due to late diagnosis. For this reason, lung cancer biomarkers that can be easily measured are highly demanded in biomedical research. In this work, we present an application of gene co-expression networks in the modelling of lung cancer gene regulatory networks, which ultimately served to the discovery of new biomarkers. For this, a robust GN inference was performed from microarray data concomitantly using three different co-expression measures. Results identified a major cluster of genes involved in SRP-dependent co-translational protein target to membrane, as well as a set of 28 genes that were exclusively found in networks generated from cancer samples. Amongst potential biomarkers, genes N C K A P 1 L and D M D are highlighted due to their implications in a considerable portion of lung and bronchus primary carcinomas. These findings demonstrate the potential of GN reconstruction in the rational prediction of biomarkers.