Biology subjects

Hunter, H.

Publications and source records attributed to Hunter, H..

4 recordsLinked to original sources

bioRxiv2025

BR-Bodies Facilitate Adaptive Responses and Survival During Copper Stress in Caulobacter crescentus

Microbes must rapidly adapt to environmental stresses, including toxic heavy metals like copper, by sensing and mitigating their harmful effects. Here, we demonstrate that the phase separation properties of bacterial ribonucleoprotein bodies (BR-bodies) enhance Caulobacter crescentus fitness under copper stress. To uncover the underlying mechanism, we identified two key interactions between copper and the central scaffold of BR-bodies, RNase E. First, biochemical assays and fluorescence microscopy experiments show that reductive chelation of Cu{superscript 2} leads to cysteine oxidation, driving the transition of BR-bodies into more solid-like condensates. Second, tryptophan fluorescence and EPR assays reveal that RNase E binds Cu{superscript 2} at histidine sites, creating a protective microenvironment that prevents mismetallation and preserves PNPase activity. More broadly, this example highlights how metal-condensate interactions can regulate condensate material properties and establish specialized chemical environments that safeguard enzyme function.

microbiology↗

bioRxiv2023

Deep learning pipeline reveals key moments in human embryonic development predictive of live birth in IVF

Demand for IVF treatment is growing, however success rates remain low partly due to difficulty in selecting the best embryo to be transferred. Current manual assessments are subjective and may not take advantage of the most informative moments in embryo development. Here, we apply convolutional neural networks (CNNs) to identify key windows in preimplantation human development that can be linked to embryo viability and are therefore suitable for the early grading of IVF embryos. We show how machine learning models trained at these developmental time-points can be used to refine overall embryo viability assessment. Exploiting the well-known capabilities of transfer learning, we illustrate the performance of CNN models for very limited data sets, paving the way for the use on a clinic-by-clinic basis, catering for local data heterogeneity.

developmental biology↗

bioRxiv2020

A systematic analysis of rodent models implicates adipogenesis and innate immunity in pathogenesis of fatty liver disease

Animal models of human disease are a key component of translational research and yet there is often no consensus on which model is optimal for a particular disease. Here, we generated a database of 3,920 rodent models of non-alcoholic fatty liver disease (NAFLD). Study designs were highly heterogeneous therefore few models had been cited more than once. Analysis of genetic models provided evidence for the role of adipose dysfunction and perturbation of the innate immune system in the progression of NAFLD. We identified that high-fat, high-fructose diets most closely recapitulate the human phenotype of NAFLD. There was substantial variability in the nomenclature of animal models; a consensus on terminology of specialist diets is needed. More broadly, this analysis demonstrates the variability in preclinical study design, which has implications for the reproducibility of in vivo experiments.

physiology↗

bioRxiv2019

What influences treatment response in animal models of non-alcoholic fatty liver disease? A meta-analysis with meta-regression.

The classical drug development pipeline necessitates studies using animal models of human disease to gauge future efficacy in humans, however, there is a comparatively low conversion rate from success in animals to in humans. Non-alcoholic fatty liver disease (NAFLD) is a complex chronic disease without any licensed therapies and hence a major field of animal research. We performed a meta-analysis of 414 interventional rodent studies (6,575 animals) in NAFLD to assess the mean difference in hepatic triglyceride content. 20 of 21 studied drug classes had similar efficacy with a mean difference of -30% hepatic triglyceride. However, when publication bias was accounted for, this reduced to -16% difference. Study characteristics were only able to account for a minority of variability on meta-regression, and we replicated previous findings of high risk of bias across 82% of cohorts. These findings build on previous work in preclinical neuroscience and help to explain the challenge of reproducibility and translation within the field of metabolism.

physiology↗