The availability of multiple bacterial genome sequences has revealed a surprising extent of variability among strains of the same species. The human gastric pathogen Helicobacter pylori is known as one of the most genetically diverse species. We have compared the genome sequence of the duodenal ulcer strain P12 and six other H. pylori genomes to elucidate the genetic repertoire and genome evolution mechanisms of this species. In agreement with previous findings, we estimate that the core genome comprises about 1200 genes and that H. pylori possesses an open pan-genome. Strain-specific genes are preferentially located at potential genome rearrangement sites or in distinct plasticity zones, suggesting two different mechanisms of genome evolution. The P12 genome contains three plasticity zones, two of which encode type IV secretion systems and have typical features of genomic islands. We demonstrate for the first time that one of these islands is capable of self-excision and horizontal transfer by a conjugative process. We also show that excision is mediated by a protein of the XerD family of tyrosine recombinases. Thus, in addition to its natural transformation competence, conjugative transfer of genomic islands has to be considered as an important source of genetic diversity in H. pylori.

Cell-free in vitro expression is increasingly important for high-throughput expression screening, high yield protein production and synthetic biology applications. Yet its potential for quantitative investigation of gene expression and regulatory circuits is limited by the availability of data on composition, kinetic rate constants and standardized computational tools for modeling. Here we report on calibration measurements and mathematical modeling of a reconstituted in vitro expression system. We measured a series of GFP expression and mRNA transcription time courses under various initial conditions and established the translation step as the bottle neck of in vitro protein synthesis. Cell-free translation was observed to expire after 3 h independent of initial template DNA concentration. We developed a minimalistic rate equation model and optimized its parameters by performing a concurrent fit to measured time courses. The model predicts the dependence of protein yield not only on template DNA concentration, but also on experimental timing and hence is a valuable tool to optimize yield strategies.

RNA synthesis and decay rates determine the steady-state levels of cellular RNAs. Metabolic tagging of newly transcribed RNA by 4-thiouridine (4sU) can reveal the relative contributions of RNA synthesis and decay rates. The kinetics of RNA processing, however, had so far remained unresolved. Here, we show that ultrashort 4sU-tagging not only provides snapshot pictures of eukaryotic gene expression but, when combined with progressive 4sU-tagging and RNA-seq, reveals global RNA processing kinetics at nucleotide resolution. Using this method, we identified classes of rapidly and slowly spliced/degraded introns. Interestingly, each class of splicing kinetics was characterized by a distinct association with intron length, gene length, and splice site strength. For a large group of introns, we also observed long lasting retention in the primary transcript, but efficient secondary splicing or degradation at later time points. Finally, we show that processing of most, but not all small nucleolar (sno)RNA-containing introns is remarkably inefficient with the majority of introns being spliced and degraded rather than processed into mature snoRNAs. In summary, our study yields unparalleled insights into the kinetics of RNA processing and provides the tools to study molecular mechanisms of RNA processing and their contribution to the regulation of gene expression.

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During viral infections cellular gene expression is subject to rapid alterations induced by both viral and antiviral mechanisms. In this study, we applied metabolic labeling of newly transcribed RNA with 4-thiouridine (4sU-tagging) to dissect the real-time kinetics of cellular and viral transcriptional activity during lytic murine cytomegalovirus (MCMV) infection. Microarray profiling on newly transcribed RNA obtained at different times during the first six hours of MCMV infection revealed discrete functional clusters of cellular genes regulated with distinct kinetics at surprising temporal resolution. Immediately upon virus entry, a cluster of NF-κB- and interferon-regulated genes was induced. Rapid viral counter-regulation of this coincided with a very transient DNA-damage response, followed by a delayed ER-stress response. Rapid counter-regulation of all three clusters indicated the involvement of novel viral regulators targeting these pathways. In addition, down-regulation of two clusters involved in cell-differentiation (rapid repression) and cell-cycle (delayed repression) was observed. Promoter analysis revealed all five clusters to be associated with distinct transcription factors, of which NF-κB and c-Myc were validated to precisely match the respective transcriptional changes observed in newly transcribed RNA. 4sU-tagging also allowed us to study the real-time kinetics of viral gene expression in the absence of any interfering virion-associated-RNA. Both qRT-PCR and next-generation sequencing demonstrated a sharp peak of viral gene expression during the first two hours of infection including transcription of immediate-early, early and even well characterized late genes. Interestingly, this was subject to rapid gene silencing by 5–6 hours post infection. Despite the rapid increase in viral DNA load during viral DNA replication, transcriptional activity of some viral genes remained remarkably constant until late-stage infection, or was subject to further continuous decline. In summary, this study pioneers real-time transcriptional analysis during a lytic herpesvirus infection and highlights numerous novel regulatory aspects of virus-host-cell interaction.

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.

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.

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