Bacteria express many small RNAs for which the regulatory roles in pathogenesis have remained poorly understood due to a paucity of robust phenotypes in standard virulence assays. Here we use a generic 'dual RNA-seq' approach to profile RNA expression simultaneously in pathogen and host during Salmonella enterica serovar Typhimurium infection and reveal the molecular impact of bacterial riboregulators. We identify a PhoP-activated small RNA, PinT, which upon bacterial internalization temporally controls the expression of both invasion-associated effectors and virulence genes required for intracellular survival. This riboregulatory activity causes pervasive changes in coding and noncoding transcripts of the host. Interspecies correlation analysis links PinT to host cell JAK-STAT signalling, and we identify infection-specific alterations in multiple long noncoding RNAs. Our study provides a paradigm for a sensitive RNA-based analysis of intracellular bacterial pathogens and their hosts without physical separation, as well as a new discovery route for hidden functions of pathogen genes.

BackgroundSuperbubbles are distinctive subgraphs in direct graphs that play an important role in assembly algorithms for high-throughput sequencing (HTS) data. Their practical importance derives from the fact they are connected to their host graph by a single entrance and a single exit vertex, thus allowing them to be handled independently. Efficient algorithms for the enumeration of superbubbles are therefore of important for the processing of HTS data. Superbubbles can be identified within the strongly connected components of the input digraph after transforming them into directed acyclic graphs. The algorithm by Sung et al. (IEEE ACM Trans Comput Biol Bioinform 12:770–777, 2015) achieves this task in -time. The extraction of superbubbles from the transformed components was later improved to by Brankovic et al. (Theor Comput Sci 609:374–383, 2016) resulting in an overall -time algorithm.ResultsA re-analysis of the mathematical structure of superbubbles showed that the construction of auxiliary DAGs from the strongly connected components in the work of Sung et al. missed some details that can lead to the reporting of false positive superbubbles. We propose an alternative, even simpler auxiliary graph that solved the problem and retains the linear running time for general digraph. Furthermore, we describe a simpler, space-efficient -time algorithm for detecting superbubbles in DAGs that uses only simple data structures.ImplementationWe present a reference implementation of the algorithm that accepts many commonly used formats for the input graph and provides convenient access to the improved algorithm. https://github.com/Fabianexe/Superbubble.

Background CT artifacts induced by orthopedic implants can limit image quality and diagnostic yield. As a number of different strategies to reduce artifact extent exist, the aim of this study was to systematically compare ex vivo the impact of different CT metal artifact reduction (MAR) strategies on spine implants made of either standard titanium or carbon-fiber-reinforced-polyetheretherketone (CFR-PEEK). Methods Spine surgeons fluoroscopically-guided prepared six sheep spine cadavers with pedicle screws and rods of either titanium or CFR-PEEK. Samples were subjected to single- and dual-energy (DE) CT-imaging. Different tube voltages (80, DE mixed, 120 and tin-filtered 150 kVp) at comparable radiation dose and iterative reconstruction versus monoenergetic extrapolation (ME) techniques were compared. Also, the influence of image reconstruction kernels (soft vs. bone tissue) was investigated. Qualitative (Likert scores) and quantitative parameters (attenuation changes induced by implant artifact, implant diameter and image noise) were evaluated by two independent radiologists. Artifact degree of different MAR-strategies and implant materials were compared by multiple ANOVA analysis. Results CFR-PEEK implants induced markedly less artifacts than standard titanium implants (p < .001). This effect was substantially larger than any other tested MAR technique. Reconstruction algorithms had small impact in CFR-PEEK implants and differed significantly in MAR efficiency (p < .001) with best MAR performance for DECT ME 130 keV (bone kernel). Significant differences in image noise between reconstruction kernels were seen (p < .001) with minor impact on artifact degree. Conclusions CFR-PEEK spine implants induce significantly less artifacts than standard titanium compositions with higher MAR efficiency than any alternate scanning or image reconstruction strategy. DECT ME 130 keV image reconstructions showed least metal artifacts. Reconstruction kernels primarily modulate image noise with minor impact on artifact degree.

The increasing availability of large digital corpora of cross-linguistic data is revolutionizing many branches of linguistics. Overall, it has triggered a shift of attention from detailed questions about individual features to more global patterns amenable to rigorous, but statistical, analyses. This engenders an approach based on successive approximations where models with simplified assumptions result in frameworks that can then be systematically refined, always keeping explicit the methodological commitments and the assumed prior knowledge. Therefore, they can resolve disputes between competing frameworks quantitatively by separating the support provided by the data from the underlying assumptions. These methods, though, often appear as a ‘black box’ to traditional practitioners. In fact, the switch to a statistical view complicates comparison of the results from these newer methods with traditional understanding, sometimes leading to misinterpretation and overly broad claims. We describe here this evolving methodological shift, attributed to the advent of big, but often incomplete and poorly curated data, emphasizing the underlying similarity of the newer quantitative to the traditional comparative methods and discussing when and to what extent the former have advantages over the latter. In this review, we cover briefly both randomization tests for detecting patterns in a largely model-independent fashion and phylolinguistic methods for a more model-based analysis of these patterns. We foresee a fruitful division of labor between the ability to computationally process large volumes of data and the trained linguistic insight identifying worthy prior commitments and interesting hypotheses in need of comparison.