Latest Publications
Modulation of stress-related behaviour by preproglucagon neurons and hypothalamic projections to the nucleus of the solitary tract
Marie K. Holt, Natalia Valderrama, Maria J. Polanco, Imogen Hayter, Ellena G. Badenoch, Stefan Trapp, Linda Rinaman
Stress-induced behaviours are driven by complex neural circuits and some neuronal populations concurrently modulate diverse behavioural and physiological responses to stress. Glucagon-like peptide-1 (GLP-1)-producing preproglucagon (PPG) neurons within the lower brainstem caudal nucleus of the solitary tract (cNTS) are particularly sensitive to stressful stimuli and are implicated in multiple physiological and behavioural responses to interoceptive and psychogenic threats. However, the afferent inputs driving stress-induced activation of PPG neurons are largely unknown, and the role of PPG neurons in anxiety-like behaviour is controversial. Through chemogenetic manipulations we reveal that cNTS PPG neurons have the ability to moderately increase anxiety-like behaviours in mice in a sex-dependent manner. Our findings reveal sex differences in behavioural responses to PPG neural activation and highlight a hypothalamic-brainstem pathway in stress-induced hypophagia.
Jeremy Keown publications
Structure of the Nipah virus polymerase complex
Esra Balıkçı, Franziska Günl, Loïc Carrique, Jeremy R Keown, Ervin Fodor, Jonathan M Grimes
Structural characterization of the full-length Hantaan virus polymerase
Hantaviridae are a family of segmented negative-sense RNA viruses that contain important human and animal pathogens. Hantaviridae contain a viral RNA-dependent RNA polymerase that replicates and transcribes the viral genome. Here we establish the expression and purification of the polymerase from the Old World Hantaan virus and characterise the structure using Cryo-EM. The insights gained here guide future mechanistic studies of both the transcription and replication activities of the hantavirus polymerase and for the development of therapeutic targets.
The influence of farm connectedness on foot-and-mouth disease outbreaks in livestock
Jean B. Contina, Rachel L. Seibel, Bhim Chaulagain, Karasi B. Mills, Michael J. Tildesley, Christopher C. Mundt
We applied a previously published livestock foot-and-mouth disease (FMD) model to estimate host connectivity using a transmission kernel based on contact tracing and measured subsequent to an animal movement ban in the 2001 United Kingdom epidemic. Connectivity within county-level farm landscapes were evaluated by considering the transmission kernel, host species composition, farm-level susceptibility, farm-level transmissibility, and distances between farms. Connectivity of the initially infected farm and mean connectivity among all farms in a county were strongly associated with effects of cull size, with disease control more effective at lower levels of farm connectivity. Host connectivity provides early information on the host-pathogen landscape and could be used as an assessment tool for predicting epidemic risks, as well as enabling preemptive control strategies to limit the size of disease outbreaks.
Regional scale diversity and distribution of soil inhabiting Tetracladium
Anna Lazar, Robert I. Griffiths, Tim Goodall, Lisa R. Norton, Ryan M. Mushinski & Gary D. Bending
The genus Tetracladium has historically been regarded as an aquatic hyphomycete. However, sequencing of terrestrial ecosystems has shown that Tetracladium species might also be terrestrial soil and plant-inhabiting fungi. The diversity of Tetracladium species, their distribution across ecosystems, and the factors that shape community composition remain largely unknown. Using internal transcribed spacer (ITS) amplicon sequencing, we investigated the spatial distribution of Tetracladium in 970 soil samples representing the major ecosystems found across the British landscape. Overall, this study provides insights into the community composition patterns of Tetracladium in terrestrial ecosystems and highlights the importance of vegetation characteristics in shaping Tetracladium communities.
Balancing selfing and outcrossing : the genetics and cell biology of nematodes with three sexual morphs
Adams, Sally, Tandonnet, Sophie and Pires-da Silva, André Francisco
Trioecy, a rare reproductive system where hermaphrodites, females, and males coexist, is found in certain algae, plants, and animals. Though it has evolved independently multiple times, its rarity suggests it may be an unstable or transitory evolutionary strategy. In the well-studied Caenorhabditis elegans, attempts to engineer a trioecious strain have reverted to the hermaphrodite/male system, reinforcing this view. However, these studies did not consider the sex-determination systems of naturally stable trioecious species. The discovery of free-living nematodes of the Auanema genus, which have naturally stable trioecy, provides an opportunity to study these systems. In Auanema, females produce only oocytes, while hermaphrodites produce both oocytes and sperm for self-fertilization. Crosses between males and females primarily produce daughters (XX hermaphrodites and females), while male-hermaphrodite crosses result in sons only. These skewed sex ratios are due to X-chromosome drive during spermatogenesis, where males produce only X-bearing sperm through asymmetric cell division. The stability of trioecy in Auanema is influenced by maternal control over sex determination and environmental cues. These factors offer insights into the genetic and environmental dynamics that maintain trioecy, potentially explaining its evolutionary stability in certain species.
Speeding up Inference of Homologous Recombination in Bacteria
Felipe J Medina-Aguayo, Xavier Didelot, Richard G Everitt
Bacteria reproduce clonally but most species recombine frequently, so that the ancestral process is best captured using an ancestral recombination graph. This graph model is often too complex to be used in an inferential setup, but it can be approximated for example by the ClonalOrigin model. Inference in the ClonalOrigin model is performed via a Reversible-Jump Markov Chain Monte Carlo algorithm, however this often performs poorly due to the complexity of the target distribution since it needs to explore spaces of different dimensions. Recent developments in Bayesian computation methodology have provided ways to improve existing methods and code, but are not well-known outside the statistics community. We show how exploiting one of these new computational methods can lead to faster inference under the ClonalOrigin model.