Ultrashort and progressive 4sU-tagging reveals key characteristics of RNA processing at nucleotide resolution.

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.

Real-time transcriptional profiling of cellular and viral gene expression during lytic cytomegalovirus infection.

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.

Inferring gene regulatory networks by ANOVA.

MOTIVATION: To improve the understanding of molecular regulation events, various approaches have been developed for deducing gene regulatory networks from mRNA expression data. RESULTS: We present a new score for network inference, η(2), that is derived from an analysis of variance. Candidate transcription factor:target gene (TF:TG) relationships are assumed more likely if the expression of TF and TG are mutually dependent in at least a subset of the examined experiments. We evaluate this dependency by η(2), a non-parametric, non-linear correlation coefficient. It is fast, easy to apply and does not require the discretization of the input data. In the recent DREAM5 blind assessment, the arguably most comprehensive evaluation of inference methods, our approach based on η(2) was rated the best performer on real expression compendia. It also performs better than methods tested in other recently published comparative assessments. About half of our predicted novel predictions are true interactions as estimated from qPCR experiments performed for DREAM5. CONCLUSIONS: 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.

4-thiouridine inhibits rRNA synthesis and causes a nucleolar stress response.

High concentrations (> 100 µM) of the ribonucleoside analog 4-thiouridine (4sU) is widely used in methods for RNA analysis like photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) and nascent messenger (m)RNA labeling (4sU-tagging). Here, we show that 4sU-tagging at low concentrations ≤ 10 µM can be used to measure production and processing of ribosomal (r)RNA. However, elevated concentrations of 4sU (> 50 µM), which are usually used for mRNA labeling experiments, inhibit production and processing of 47S rRNA. The inhibition of rRNA synthesis is accompanied by nucleoplasmic translocation of nucleolar nucleophosmin (NPM1), induction of the tumor suppressor p53, and inhibition of proliferation. We conclude that metabolic labeling of RNA by 4sU triggers a nucleolar stress response, which might influence the interpretation of results. Therefore, functional ribosome biogenesis, nucleolar integrity, and cell cycle should be addressed in 4sU labeling experiments.

Strain-specific genes of Helicobacter pylori: genome evolution driven by a novel type IV secretion system and genomic island transfer.

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.

Refining ensembles of predicted gene regulatory networks based on characteristic interaction sets.

Different ensemble voting approaches have been successfully applied for reverse-engineering of gene regulatory networks. They are based on the assumption that a good approximation of true network structure can be derived by considering the frequencies of individual interactions in a large number of predicted networks. Such approximations are typically superior in terms of prediction quality and robustness as compared to considering a single best scoring network only. Nevertheless, ensemble approaches only work well if the predicted gene regulatory networks are sufficiently similar to each other. If the topologies of predicted networks are considerably different, an ensemble of all networks obscures interesting individual characteristics. Instead, networks should be grouped according to local topological similarities and ensemble voting performed for each group separately. We argue that the presence of sets of co-occurring interactions is a suitable indicator for grouping predicted networks. A stepwise bottom-up procedure is proposed, where first mutual dependencies between pairs of interactions are derived from predicted networks. Pairs of co-occurring interactions are subsequently extended to derive characteristic interaction sets that distinguish groups of networks. Finally, ensemble voting is applied separately to the resulting topologically similar groups of networks to create distinct group-ensembles. Ensembles of topologically similar networks constitute distinct hypotheses about the reference network structure. Such group-ensembles are easier to interpret as their characteristic topology becomes clear and dependencies between interactions are known. The availability of distinct hypotheses facilitates the design of further experiments to distinguish between plausible network structures. The proposed procedure is a reasonable refinement step for non-deterministic reverse-engineering applications that produce a large number of candidate predictions for a gene regulatory network, e.g. due to probabilistic optimization or a cross-validation procedure.

Experiment and mathematical modeling of gene expression dynamics in a cell-free system.

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.

Petri Nets with Fuzzy Logic (PNFL): reverse engineering and parametrization.

BACKGROUND: The 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 FINDINGS: We 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. CONCLUSIONS: The 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.