Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression

The large variability in mRNA and protein levels found from both static and dynamic measurements in single cells has been largely attributed to random periods of transcription, often occurring in bursts. The cell cycle has a pronounced global role in affecting transcriptional and translational output, but how this influences transcriptional statistics from noisy promoters is unknown and generally ignored by current stochastic models. Here we show that variable transcription from the synthetic tetO promoter in S. cerevisiae is dominated by its dependence on the cell cycle. Real-time measurements of fluorescent protein at high expression levels indicate tetO promoters increase transcription rate ~2-fold in S/G2/M similar to constitutive genes. At low expression levels, where tetO promoters are thought to generate infrequent bursts of transcription, we observe random pulses of expression restricted to S/G2/M, which are correlated between homologous promoters present in the same cell. The analysis of static, single-cell mRNA measurements at different points along the cell cycle corroborates these findings. Our results demonstrate that highly variable mRNA distributions in yeast are not solely the result of randomly switching between periods of active and inactive gene expression, but instead largely driven by differences in transcriptional activity between G1 and S/G2/M.GM095733BBBE 103316MIT Startup Fun

A geometric analysis of fast-slow models for stochastic gene expression

Stochastic models for gene expression frequently exhibit dynamics on several different scales. One potential time-scale separation is caused by significant differences in the lifetimes of mRNA and protein; the ratio of the two degradation rates gives a natural small parameter in the resulting chemical master equation, allowing for the application of perturbation techniques. Here, we develop a framework for the analysis of a family of ‘fast-slow’ models for gene expression that is based on geometric singular perturbation theory. We illustrate our approach by giving a complete characterisation of a standard two-stage model which assumes transcription, translation, and degradation to be first-order reactions. In particular, we present a systematic expansion procedure for the probability-generating function that can in principle be taken to any order in the perturbation parameter, allowing for an approximation of the corresponding propagator probabilities to that same order. For illustrative purposes, we perform this expansion explicitly to first order, both on the fast and the slow time-scales; then, we combine the resulting asymptotics into a composite fast-slow expansion that is uniformly valid in time. In the process, we extend, and prove rigorously, results previously obtained by Shahrezaei and Swain (Proc Natl Acad Sci USA 105(45):17256–17261, 2008) and Bokes et al. (J Math Biol 64(5):829–854, 2012; J Math Biol 65(3):493–520, 2012). We verify our asymptotics by numerical simulation, and we explore its practical applicability and the effects of a variation in the system parameters and the time-scale separation. Focussing on biologically relevant parameter regimes that induce translational bursting, as well as those in which mRNA is frequently transcribed, we find that the first-order correction can significantly improve the steady-state probability distribution. Similarly, in the time-dependent scenario, inclusion of the first-order fast asymptotics results in a uniform approximation for the propagator probabilities that is superior to the slow dynamics alone. Finally, we discuss the generalisation of our geometric framework to models for regulated gene expression that involve additional stages

Patient- and physician-related risk factors for hyperkalaemia in potassium-increasing drug-drug interactions

PURPOSE: Hyperkalaemia due to potassium-increasing drug-drug interactions (DDIs) is a clinically important adverse drug event. The purpose of this study was to identify patient- and physician-related risk factors for the development of hyperkalaemia. METHODS: The risk for adult patients hospitalised in the University Hospital Zurich between 1 December 2009 and 31 December 2011 of developing hyperkalaemia was correlated with patient characteristics, number, type and duration of potassium-increasing DDIs and frequency of serum potassium monitoring. RESULTS: The 76,467 patients included in this study were prescribed 8,413 potentially severe potassium-increasing DDIs. Patient-related characteristics associated with the development of hyperkalaemia were pulmonary allograft [relative risk (RR) 5.1; p 48 h: RR 1.6; p < 0.01). CONCLUSION: Strategies for reducing the risk of hyperkalaemia during potassium-increasing DDIs should consider both patient- and physician-related risk factors

Black root rot: a long known but little understood disease

Table S1. Hosts reported to be susceptible to black root rot infection.Table S2. Variation in host susceptibility to black root rot infection by the fungus formally known as Thielaviopsis basicola.Black root rot caused by the pathogen Thielaviopsis basicola has been known since the mid 1800s. The disease is important on many agricultural and ornamental plant species and has been found in at least 31 countries. Since its description, the pathogen has had a complex taxonomic history that has resulted in a confused literature. A recent revision of the Ceratocystidaceae following the advent of DNA sequencing technology has made it possible to resolve this confusion. Importantly, it has also shown that there are two pathogens in the Ceratocystidaceae that cause black root rot. They reside in the newly established genus Berkeleyomyces and are now known as B. basicola and B. rouxiae. This review considers the taxonomic history of the black root rot pathogens, and their global distribution. Prospects relating to the serious diseases that they cause and the likely impact that the era of genomics will have on our understanding of the pathogens are also highlighted.The University of Pretoria, the members of Tree Protection Co‐operative Programme (TPCP), the DST‐NRF Centre of Excellence in Tree Health Biotechnology (CTHB) and the National Research Foundation.https://onlinelibrary.wiley.com/journal/136530592020-06-01hj2019BiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant Patholog