Julian R. E. Davis

Gene transcription occurs in short bursts interspersed with silent periods, and these kinetics can be altered by promoter structure. The effect of alternate promoter architecture on transcription bursting is not known. We studied the human prolactin (hPRL) gene that contains 2 promoters, a pituitary-specific promoter that requires the transcription factor Pit-1 and displays dramatic transcriptional bursting activity and an alternate upstream promoter that is active in nonpituitary tissues. We studied large hPRL genomic fragments with luciferase reporters, and used bacterial artificial chromosome recombineering to manipulate critical promoter regions. Stochastic switch mathematical modelling of single-cell time-lapse luminescence image data revealed that the Pit-1–dependent promoter showed longer, higher-amplitude transcriptional bursts. Knockdown studies confirmed that the presence of Pit-1 stabilized and prolonged periods of active transcription. Pit-1 therefore plays an active role in establishing the timing of transcription cycles, in addition to its cell-specific functions.

Prolactin is a major hormone product of the pituitary gland, the central endocrine regulator. Despite its physiological importance, the cell-level mechanisms of prolactin production are not well understood. Having significantly improved the resolution of real-time-single-cell-GFP-imaging, the authors recently revealed that prolactin gene transcription is highly dynamic and stochastic yet shows space-time coordination in an intact tissue slice. However, it still remains an open question as to what kind of cellular communication mediates the observed space-time organization. To determine the type of interaction between cells we developed a statistical model. The degree of similarity between two expression time series was studied in terms of two distance measures, Euclidean and geodesic, the latter being a network-theoretic distance defined to be the minimal number of edges between nodes, and this was used to discriminate between juxtacrine from paracrine signalling. The analysis presented here suggests that juxtacrine signalling dominates. To further determine whether the coupling is coordinating transcription or post-transcriptional activities we used stochastic switch modelling to infer the transcriptional profiles of cells and estimated their similarity measures to deduce that their spatial cellular coordination involves coupling of transcription via juxtacrine signalling. We developed a computational model that involves an inter-cell juxtacrine coupling, yielding simulation results that show space-time coordination in the transcription level that is in agreement with the above analysis. The developed model is expected to serve as the prototype for the further study of tissue-level organised gene expression for epigenetically regulated genes, such as prolactin. Prolactin is a major hormone product of the pituitary gland, the central endocrine regulator found underneath the brain. In mammals it is crucial for milk production and reproductive function. Production of such an important protein needs to be regulated tightly, and therefore one might imagine that its gene expression is largely static. However, recent experiments using real-time imaging techniques at a single-cell resolution have revealed prolactin gene transcription to be highly dynamic and stochastic in nature, while displaying clear tissue-scale space-time coordination. This discovery raised a new question, namely, what kind of cellular communication mediates such a space-time organization? In this study, by developing a statistical method that involves network theory, we show that such unexpected behaviour involves contact-driven inter-cell signalling. The study develops a mathematical model that can reproduce realistic levels of space-time coordinated gene expression. The method and model developed here are generic and can be used in the study of other signalling systems that show space-time coordinated behaviour.

Communication is complex in endocrine care, particularly during transition from paediatric to adult services. The aims of this study were to examine the feasibility of interventions to support young people to interact with clinicians. Development and evaluation of a complex intervention in 2 phases: Pre-intervention observational study; Intervention feasibility study. Purposive sample of recordings of 62 consultations with 58 young people aged 11–25 years with long-term endocrine conditions in two paediatric and two adult endocrine clinics. Proportion of time talked during consultations, number and direction of questions asked; Paediatric Consultation Assessment Tool (PCAT); OPTION shared decision making tool; Medical Information Satisfaction Scale (MISS- 21). Young people were invited to use one or more of: a prompt sheet to help them influence consultation agendas and raise questions; a summary sheet to record key information; and the www.explain.me.uk website. Nearly two thirds of young people (63%) chose to use at least one communication intervention. Higher ratings for two PCAT items (95% CI 0.0 to 1.1 and 0.1 to 1.7) suggest interventions can support consultation skills. A higher proportion of accompanying persons (83%) than young people (64%) directed questions to clinicians. The proportion of young people asking questions was higher (84%) in the intervention phase than in the observation phase (71%). Interventions were acceptable and feasible. The Intervention phase was associated with YP asking more questions, which implies that the availability of interventions could promote interactivity.

Transcription at individual genes in single cells is often pulsatile and stochastic. A key question emerges regarding how this behaviour contributes to tissue phenotype, but it has been a challenge to quantitatively analyse this in living cells over time, as opposed to studying snap-shots of gene expression state. We have used imaging of reporter gene expression to track transcription in living pituitary tissue. We integrated live-cell imaging data with statistical modelling for quantitative real-time estimation of the timing of switching between transcriptional states across a whole tissue. Multiple levels of transcription rate were identified, indicating that gene expression is not a simple binary ‘on-off’ process. Immature tissue displayed shorter durations of high-expressing states than the adult. In adult pituitary tissue, direct cell contacts involving gap junctions allowed local spatial coordination of prolactin gene expression. Our findings identify how heterogeneous transcriptional dynamics of single cells may contribute to overall tissue behaviour.

The use of bacterial artificial chromosome (BAC) reporter constructs in molecular physiology enables the inclusion of large sections of flanking DNA, likely to contain regulatory elements and enhancers regions that contribute to the transcriptional output of a gene. Using BAC recombineering, we have manipulated a 160-kb human prolactin luciferase (hPRL-Luc) BAC construct and mutated the previously defined proximal estrogen response element (ERE) located −1189 bp relative to the transcription start site, to assess its involvement in the estrogen responsiveness of the entire hPRL locus. We found that GH3 cell lines stably expressing Luc under control of the ERE-mutated hPRL promoter (ERE-Mut) displayed a dramatically reduced transcriptional response to 17β-estradiol (E2) treatment compared with cells expressing Luc from the wild-type (WT) ERE hPRL-Luc promoter (ERE-WT). The −1189 ERE controls not only the response to E2 treatment but also the acute transcriptional response to TNFα, which was abolished in ERE-Mut cells. ERE-WT cells displayed a biphasic transcriptional response after TNFα treatment, the acute phase of which was blocked after treatment with the estrogen receptor antagonist 4-hydroxy-tamoxifen. Unexpectedly, we show the oscillatory characteristics of hPRL promoter activity in individual living cells were unaffected by disruption of this crucial response element, real-time bioluminescence imaging showed that transcription cycles were maintained, with similar cycle lengths, in ERE-WT and ERE-Mut cells. These data suggest the −1189 ERE is the dominant response element involved in the hPRL transcriptional response to both E2 and TNFα and, crucially, that cycles of hPRL promoter activity are independent of estrogen receptor binding.

Gene expression is made up of inherently stochastic processes within single cells and can be modeled through stochastic reaction networks (SRNs). In particular, SRNs capture the features of intrinsic variability arising from intracellular biochemical processes. We extend current models for gene expression to allow the transcriptional process within an SRN to follow a random step or switch function which may be estimated using reversible jump Markov chain Monte Carlo (MCMC). This stochastic switch model provides a generic framework to capture many different dynamic features observed in single cell gene expression. Inference for such SRNs is challenging due to the intractability of the transition densities. We derive a model-specific birth–death approximation and study its use for inference in comparison with the linear noise approximation where both approximations are considered within the unifying framework of state-space models. The methodology is applied to synthetic as well as experimental single cell imaging data measuring expression of the human prolactin gene in pituitary cells.

A central challenge in computational modeling of dynamic biological systems is parameter inference from experimental time course measurements. However, one would not only like to infer kinetic parameters but also study their variability from cell to cell. Here we focus on the case where single-cell fluorescent protein imaging time series data are available for a population of cells. Based on van Kampen’s linear noise approximation, we derive a dynamic state space model for molecular populations which is then extended to a hierarchical model. This model has potential to address the sources of variability relevant to single-cell data, namely, intrinsic noise due to the stochastic nature of the birth and death processes involved in reactions and extrinsic noise arising from the cell-to-cell variation of kinetic parameters. In order to infer such a model from experimental data, one must also quantify the measurement process where one has to allow for nonmeasurable molecular species as well as measurement noise of unknown level and variance. The availability of multiple single-cell time series data here provides a unique testbed to fit such a model and quantify these different sources of variation from experimental data.

Seasonal mammals integrate changes in the duration of nocturnal melatonin secretion to drive annual physiologic cycles. Melatonin receptors within the proximal pituitary region, the pars tuberalis (PT), are essential in regulating seasonal neuroendocrine responses. In the ovine PT, melatonin is known to influence acute changes in transcriptional dynamics coupled to the onset (dusk) and offset (dawn) of melatonin secretion, leading to a potential interval-timing mechanism capable of decoding changes in day length (photoperiod). Melatonin offset at dawn is linked to cAMP accumulation, which directly induces transcription of the clock gene Per1. The rise of melatonin at dusk induces a separate and distinct cohort, including the clock-regulated genes Cry1 and Nampt, but little is known of the up-stream mechanisms involved. Here, we used next-generation sequencing of the ovine PT transcriptome at melatonin onset and identified Npas4 as a rapidly induced basic helix-loop-helix Per-Arnt-Sim domain transcription factor. In vivo we show nuclear localization of NPAS4 protein in presumptive melatonin target cells of the PT (α-glycoprotein hormone-expressing cells), whereas in situ hybridization studies identified acute and transient expression in the PT of Npas4 in response to melatonin. In vitro, NPAS4 forms functional dimers with basic helix loop helix-PAS domain cofactors aryl hydrocarbon receptor nuclear translocator (ARNT), ARNT2, and ARNTL, transactivating both Cry1 and Nampt ovine promoter reporters. Using a combination of 5′-deletions and site-directed mutagenesis, we show NPAS4-ARNT transactivation to be codependent upon two conserved central midline elements within the Cry1 promoter. Our data thus reveal NPAS4 as a candidate immediate early-response gene in the ovine PT, driving molecular responses to melatonin.