Biology subjects

Brackston, R. D.

Publications and source records attributed to Brackston, R. D..

3 recordsLinked to original sources

bioRxiv2020

Molecular origins of transcriptional heterogeneity in diazotrophic K. oxytoca

Phenotypic heterogeneity in clonal bacterial batch cultures is an important adaptive strategy to changing environments, including in diazotrophs with the unique capacity to convert di-nitrogen into bio-available ammonium. In diazotrophic Klebsiella oxytoca we simultaneously measured mRNA levels of key regulatory (glnK-amtB, nifLA) and structural (nifHDK) operons required for establishing nitrogen fixation, using dual molecule, single cell RNA-FISH. Through stochastic transcription models and mutual information analysis we revealed likely molecular origins for heterogeneity in nitrogenase expression. In wildtype and regulatory variant strains we inferred contributions from intrinsic and extrinsic noise, finding that nifHDK transcription is inherently bursty, but that noise propagation through signalling is also significant. The regulatory gene glnK had the highest discernible effect on nifHDK variance, while noise from factors outside of the regulatory pathway were negligible. Results provide evidence that heterogeneity is a fundamental property of this regulatory system, indicating potential constraints for engineering homogeneous nitrogenase expression.

microbiology↗

bioRxiv2020

Exactly solvable models of stochastic gene expression

Stochastic models are key to understanding the intricate dynamics of gene expression. But the simplest models which only account for e.g. active and inactive states of a gene fail to capture common observations in both prokaryotic and eukaryotic organisms. Here we consider multistate models of gene expression which generalise the canonical Telegraph process, and are capable of capturing the joint effects of e.g. transcription factors, heterochromatin state and DNA accessibility (or, in prokaryotes, Sigma-factor activity) on transcript abundance. We propose two approaches for solving classes of these generalised systems. The first approach offers a fresh perspective on a general class of multistate models, and allows us to "decompose" more complicated systems into simpler processes, each of which can be solved analytically. This enables us to obtain a solution of any model from this class. We further show that these models cannot have a heavy-tailed distribution in the absence of extrinsic noise. Next, we develop an approximation method based on a power series expansion of the stationary distribution for an even broader class of multistate models of gene transcription. The combination of analytical and computational solutions for these realistic gene expression models also holds the potential to design synthetic systems, and control the behaviour of naturally evolved gene expression systems, e.g. in guiding cell-fate decisions.

systems biology↗

bioRxiv2019

Extrinsic noise and heavy-tailed laws in gene expression

Noise in gene expression is one of the hallmarks of life at the molecular scale. Here we derive analytical solutions to a set of models describing the molecular mechanisms underlying transcription of DNA into RNA. Our Ansatz allows us to incorporate the effects of extrinsic noise - encompassing factors external to the transcription of the individual gene - and discuss the ramifications for heterogeneity in gene product abundance that has been widely observed in single cell data. Crucially, we are able to show that heavy-tailed distributions of RNA copy numbers cannot result from the intrinsic stochasticity in gene expression alone, but must instead reflect extrinsic sources of variability.

systems biology↗