Thymineless death is inhibited by CsrA in Escherichia coli lacking the SOS response

Thymineless death (TLD) is the rapid loss of colony-forming ability in bacterial, yeast and human cells starved for thymine, and is the mechanism of action of common chemotherapeutic drugs. In Escherichia coli, significant loss of viability during TLD requires the SOS replication-stress/DNA-damage response, specifically its role in inducing the inhibitor of cell division, SulA. An independent RecQ- and RecJ-dependent TLD pathway accounts for a similarly large additional component of TLD, and a third SOS- and RecQ/J-independent TLD pathway has also been observed. Although two groups have implicated the SOS-response in TLD, an SOS-deficient mutant strain from an earlier study was found to be sensitive to thymine deprivation. We performed whole-genome resequencing on that SOS-deficient strain and find that, compared with the SOS-proficient control strain, it contains five mutations in addition to the SOS-blocking lexA(Ind⁻) mutation. One of the additional mutations, csrA, confers TLD sensitivity specifically in SOS-defective strains. We find that CsrA, a carbon storage regulator, reduces TLD in SOS- or SulA-defective cells, and that the increased TLD that occurs in csrA⁻ SOS-defective cells is dependent on RecQ. We consider a hypothesis in which the modulation of nucleotide pools by CsrA might inhibit TLD specifically in SOS-deficient (SulA-deficient) cells.     

The IKK Inhibitor Bay 11-7082 Induces Cell Death Independent from Inhibition of Activation of NFκB Transcription Factors

Multiple myeloma (MM) displays an NFκB activity-related gene expression signature and about 20% of primary MM samples harbor genetic alterations conducive to intrinsic NFκB signaling activation. The relevance of blocking the classical versus the alternative NFκB signaling pathway and the molecular execution mechanisms involved, however, are still poorly understood. Here, we comparatively tested NFκB activity abrogation through TPCA-1 (an IKK2 inhibitor), BAY 11-7082 (an IKK inhibitor poorly selective for IKK1 and IKK2), and MLN4924 (an NEDD8 activating enzyme (NAE)-inhibitor), and analyzed their anti-MM activity. Whereas TPCA-1 interfered selectively with activation of the classical NFκB pathway, the other two compounds inhibited classical and alternative NFκB signaling without significant discrimination. Noteworthy, whereas TPCA-1 and MLN4924 elicited rather mild anti-MM effects with slight to moderate cell death induction after 1 day BAY 11-7082 was uniformly highly toxic to MM cell lines and primary MM cells. Treatment with BAY 11-7082 induced rapid cell swelling and its initial effects were blocked by necrostatin-1 or the ROS scavenger BHA, but a lasting protective effect was not achieved even with additional blockade of caspases. Because MLN4924 inhibits the alternative NFκB pathway downstream of IKK1 at the level of p100 processing, the quite discordant effects between MLN4924 and BAY 11-7082 must thus be due to blockade of IKK1-mediated NFκB-independent necrosis-inhibitory functions or represent an off-target effect of BAY 11-7082. In accordance with the latter, we further observed that concomitant knockdown of IKK1 and IKK2 did not have any major short-term adverse effect on the viability of MM cells.     

Complementing the Eukaryotic Protein Interactome

Protein interaction networks are important for the understanding of regulatory mechanisms, for the explanation of experimental data and for the prediction of protein functions. Unfortunately, most interaction data is available only for model organisms. As a possible remedy, the transfer of interactions to organisms of interest is common practice, but it is not clear when interactions can be transferred from one organism to another and, thus, the confidence in the derived interactions is low. Here, we propose to use a rich set of features to train Random Forests in order to score transferred interactions. We evaluated the transfer from a range of eukaryotic organisms to S. cerevisiae using orthologs. Directly transferred interactions to S. cerevisiae are on average only 24% consistent with the current S. cerevisiae interaction network. By using commonly applied filter approaches the transfer precision can be improved, but at the cost of a large decrease in the number of transferred interactions. Our Random Forest approach uses various features derived from both the target and the source network as well as the ortholog annotations to assign confidence values to transferred interactions. Thereby, we could increase the average transfer consistency to 85%, while still transferring almost 70% of all correctly transferable interactions. We tested our approach for the transfer of interactions to other species and showed that our approach outperforms competing methods for the transfer of interactions to species where no experimental knowledge is available. Finally, we applied our predictor to score transferred interactions to 83 targets species and we were able to extend the available interactome of B. taurus, M. musculus and G. gallus with over 40,000 interactions each. Our transferred interaction networks are publicly available via our web interface, which allows to inspect and download transferred interaction sets of different sizes, for various species, and at specified expected precision levels. Availability: http://services.bio.ifi.lmu.de/coin-db/.     

Disentangling the Spatio-Environmental Drivers of Human Settlement: An Eigenvector Based Variation Decomposition

The relative importance of deterministic and stochastic processes driving patterns of human settlement remains controversial. A main reason for this is that disentangling the drivers of distributions and geographic clustering at different spatial scales is not straightforward and powerful analytical toolboxes able to deal with this type of data are largely deficient. Here we use a multivariate statistical framework originally developed in community ecology, to infer the relative importance of spatial and environmental drivers of human settlement. Using Moran’s eigenvector maps and a dataset of spatial variation in a set of relevant environmental variables we applied a variation partitioning procedure based on redundancy analysis models to assess the relative importance of spatial and environmental processes explaining settlement patterns. We applied this method on an archaeological dataset covering a 15 km² area in SW Turkey spanning a time period of 8000 years from the Late Neolithic/Early Chalcolithic up to the Byzantine period. Variation partitioning revealed both significant unique and commonly explained effects of environmental and spatial variables. Land cover and water availability were the dominant environmental determinants of human settlement throughout the study period, supporting the theory of the presence of farming communities. Spatial clustering was mainly restricted to small spatial scales. Significant spatial clustering independent of environmental gradients was also detected which can be indicative of expansion into unsuitable areas or an unexpected absence in suitable areas which could be caused by dispersal limitation. Integrating historic settlement patterns as additional predictor variables resulted in more explained variation reflecting temporal autocorrelation in settlement locations.     

S-adenosylmethionine and methylthioadenosine boost cellular productivities of antibody forming Chinese hamster ovary cells

The improvement of cell specific productivities for the formation of therapeutic proteins is an important step towards intensified production processes. Among others, the induction of the desired production phenotype via proper media additives is a feasible solution provided that said compounds adequately trigger metabolic and regulatory programs inside the cells. In this study, S-(5′-adenosyl)- l -methionine (SAM) and 5′-deoxy-5′-(methylthio)adenosine (MTA) were found to stimulate cell specific productivities up to approx. 50% while keeping viable cell densities transiently high and partially arresting the cell cycle in an anti-IL-8-producing CHO-DP12 cell line. Noteworthy, MTA turned out to be the chemical degradation product of the methyl group donor SAM and is consumed by the cells.     

The Antiretroviral Protease Inhibitor Ritonavir Accelerates Glutathione Export from Cultured Primary Astrocytes

Antiretroviral protease inhibitors are a class of important drugs that are used for the treatment of human immunodeficiency virus infections. Among those compounds, ritonavir is applied frequently in combination with other antiretroviral protease inhibitors, as it has been reported to boost their therapeutic efficiency. To test whether ritonavir affects the viability and the glutathione (GSH) metabolism of brain cells, we have exposed primary astrocyte cultures to this protease inhibitor. Application of ritonavir in low micromolar concentrations did not compromise cell viability, but caused a time- and concentration-dependent loss of GSH from the cells which was accompanied by a matching increase in the extracellular GSH content. Half-maximal effects were observed for ritonavir in a concentration of 3 μM. The ritonavir-induced stimulated GSH export from astrocytes was completely prevented by MK571, an inhibitor of the multidrug resistance protein 1. In addition, continuous presence of ritonavir was essential to maintain the stimulated GSH export, since removal of ritonavir terminated the stimulated GSH export. Ritonavir was more potent to stimulate GSH export from astrocytes than the antiretroviral protease inhibitors indinavir and nelfinavir, but combinations of ritonavir with indinavir or nelfinavir did not further stimulate astrocytic GSH export compared to a treatment with ritonavir alone. The strong effects of ritonavir and other antiretroviral protease inhibitors on the GSH metabolism of astrocytes suggest that a chronic treatment of patients with such compounds may affect their brain GSH metabolism.