In Firmicutes bacteria, ATP-binding cassette (ABC) transporters have been recognized as important resistance determinants against antimicrobial peptides. Together with neighboring two-component systems (TCSs), which regulate their expression, they form specific detoxification modules. Both the transport permease and sensor kinase components show unusual domain architecture: the permeases contain a large extracellular domain, while the sensor kinases lack an obvious input domain. One of the best-characterized examples is the bacitracin resistance module BceRS-BceAB of Bacillus subtilis. Strikingly, in this system, the ABC transporter and TCS have an absolute mutual requirement for each other in both sensing of and resistance to bacitracin, suggesting a novel mode of signal transduction in which the transporter constitutes the actual sensor. We identified over 250 such BceAB-like ABC transporters in the current databases. They occurred almost exclusively in Firmicutes bacteria, and 80% of the transporters were associated with a BceRS-like TCS. Phylogenetic analyses of the permease and sensor kinase components revealed a tight evolutionary correlation. Our findings suggest a direct regulatory interaction between the ABC transporters and TCSs, mediating communication between both components. Based on their observed coclustering and conservation of response regulator binding sites, we could identify putative corresponding two-component systems for transporters lacking a regulatory system in their immediate neighborhood. Taken together, our results show that these types of ABC transporters and TCSs have coevolved to form self-sufficient detoxification modules against antimicrobial peptides, widely distributed among Firmicutes bacteria.

The score η(2) has a number of interesting features that enable the efficient detection of gene regulatory interactions. For most experimental setups, it is an interesting alternative to other measures of dependency such as Pearson's correlation or mutual information.

BackgroundSomatic cell nuclear transfer (SCNT) using genetically engineered donor cells is currently the most widely used strategy to generate tailored pig models for biomedical research. Although this approach facilitates a similar spectrum of genetic modifications as in rodent models, the outcome in terms of live cloned piglets is quite variable. In this study, we aimed at a comprehensive analysis of environmental and experimental factors that are substantially influencing the efficiency of generating genetically engineered pigs. Based on a considerably large data set from 274 SCNT experiments (in total 18,649 reconstructed embryos transferred into 193 recipients), performed over a period of three years, we assessed the relative contribution of season, type of genetic modification, donor cell source, number of cloning rounds, and pre-selection of cloned embryos for early development to the cloning efficiency.Results109 (56%) recipients became pregnant and 85 (78%) of them gave birth to offspring. Out of 318 cloned piglets, 243 (76%) were alive, but only 97 (40%) were clinically healthy and showed normal development. The proportion of stillborn piglets was 24% (75/318), and another 31% (100/318) of the cloned piglets died soon after birth. The overall cloning efficiency, defined as the number of offspring born per SCNT embryos transferred, including only recipients that delivered, was 3.95%. SCNT experiments performed during winter using fetal fibroblasts or kidney cells after additive gene transfer resulted in the highest number of live and healthy offspring, while two or more rounds of cloning and nuclear transfer experiments performed during summer decreased the number of healthy offspring.ConclusionAlthough the effects of individual factors may be different between various laboratories, our results and analysis strategy will help to identify and optimize the factors, which are most critical to cloning success in programs aiming at the generation of genetically engineered pig models.

BackgroundThe recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations.Methodology and Principal FindingsWe inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets.ConclusionsThe inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters.

Supplementary data are available at Bioinformatics online.