Using topology to tame the complex biochemistry of genetic networks

A window into lysogeny: revealing temperate phage biology with transcriptomics

Prophages are integrated phage elements that are a pervasive feature of bacterial genomes. The fitness of bacteria is enhanced by prophages that confer beneficial functions such as virulence, stress tolerance or phage resistance, and these functions are encoded by 'accessory' or 'moron' loci. Whilst the majority of phage-encoded genes are repressed during lysogeny, accessory loci are often highly expressed. However, it is challenging to identify novel prophage accessory loci from DNA sequence data alone. Here, we use bacterial RNA-seq data to examine the transcriptional landscapes of five Salmonella prophages. We show that transcriptomic data can be used to heuristically enrich for prophage features that are highly expressed within bacterial cells and represent functionally important accessory loci. Using this approach, we identify a novel antisense RNA species in prophage BTP1, STnc6030, which mediates superinfection exclusion of phage BTP1. Bacterial transcriptomic datasets are a powerful tool to explore the molecular biology of temperate phages.

Logical-continuous modelling of post-translationally regulated bistability of curli fiber expression in Escherichia coli

BACKGROUND

Bacteria have developed a repertoire of signalling mechanisms that enable adaptive responses to fluctuating environmental conditions. The formation of biofilm, for example, allows persisting in times of external stresses, e.g. induced by antibiotics or a lack of nutrients. Adhesive curli fibers, the major extracellular matrix components in Escherichia coli biofilms, exhibit heterogeneous expression in isogenic cells exposed to identical external conditions. The dynamical mechanisms underlying this heterogeneity remain poorly understood. In this work, we elucidate the potential role of post-translational bistability as a source for this heterogeneity.

RESULTS

We introduce a structured modelling workflow combining logical network topology analysis with time-continuous deterministic and stochastic modelling. The aim is to evaluate the topological structure of the underlying signalling network and to identify and analyse model parameterisations that satisfy observations from a set of genetic knockout experiments. Our work supports the hypothesis that the phenotypic heterogeneity of curli expression in biofilm cells is induced by bistable regulation at the post-translational level. Stochastic modelling suggests diverse noise-induced switching behaviours between the stable states, depending on the expression levels of the c-di-GMP-producing (diguanylate cyclases, DGCs) and -degrading (phosphodiesterases, PDEs) enzymes and reveals the quantitative difference in stable c-di-GMP levels between distinct phenotypes. The most dominant type of behaviour is characterised by a fast switching from curli-off to curli-on with a slow switching in the reverse direction and the second most dominant type is a long-term differentiation into curli-on or curli-off cells. This behaviour may implicate an intrinsic feature of the system allowing for a fast adaptive response (curli-on) versus a slow transition to the curli-off state, in line with experimental observations.

CONCLUSION

The combination of logical and continuous modelling enables a thorough analysis of different determinants of bistable regulation, i.e. network topology and biochemical kinetics, and allows for an incorporation of experimental data from heterogeneous sources. Our approach yields a mechanistic explanation for the phenotypic heterogeneity of curli fiber expression. Furthermore, the presented work provides a detailed insight into the interactions between the multiple DGC- and PDE-type enzymes and the role of c-di-GMP in dynamical regulation of cellular decisions.

An infection-relevant transcriptomic compendium for Salmonella enterica Serovar Typhimurium

Bacterial transcriptional networks consist of hundreds of transcription factors and thousands of promoters. However, the true complexity of transcription in a bacterial pathogen and the effect of the environments encountered during infection remain to be established. We present a simplified approach for global promoter identification in bacteria using RNA-seq-based transcriptomic analyses of 22 distinct infection-relevant environmental conditions. Individual RNA samples were combined to identify most of the 3,838 Salmonella enterica serovar Typhimurium promoters in just two RNA-seq runs. Individual in vitro conditions stimulated characteristic transcriptional signatures, and the suite of 22 conditions induced transcription of 86% of all S. Typhimurium genes. We highlight the environmental conditions that induce the Salmonella pathogenicity islands and present a small RNA expression landscape of 280 sRNAs. This publicly available compendium of environmentally controlled expression of every transcriptional feature of S. Typhimurium constitutes a useful resource for the bacterial research community.

Breast cancer causes and treatment: where are we going wrong?

This discussion paper seeks to provoke thoughts about cancer research in general, and why breast cancer in particular is not yet "curable". It asks the question - are we looking at the disease in the right way? Should we regard cancer as a progressive state, which is part of aging? Should we tailor treatment to "reset" the system or slow progression rather than try using toxic and aggressive therapy to kill every cancer cell (and sometimes also the patient)? The thesis is presented that we need to revisit our fundamental beliefs about the disease and then ask why we cling to beliefs that clearly are no longer valid. The paper also questions the role of ethics boards in hampering research and discusses the concept that breast cancer is an industry with vested interests involving profiteering by preventive, diagnostic, and therapeutic players. Finally, the paper suggests some ways forward based on emerging concepts in system biology and epigenetics.

Inference for the physical sciences

There is a disconnect between developments in modern data analysis and some parts of the physical sciences in which they could find ready use. This introduction, and this issue, provides resources to help experimental researchers access modern data analysis tools and exposure for analysts to extant challenges in physical science. We include a table of resources connecting statistical and physical disciplines and point to appropriate books, journals, videos and articles. We conclude by highlighting the relevance of each of the articles in the associated issue.