Biology subjects

Barnes, B. M.

Publications and source records attributed to Barnes, B. M..

4 recordsLinked to original sources

bioRxiv2025

Transcriptional Circuitry in HGSOC: A Dynamic Three-State Model Informed by a Living Biobank of Purified Tumour Fractions

High-grade serous ovarian cancer (HGSOC) is a heterogeneous disease, but efforts to define transcriptional subtypes using bulk RNA sequencing have been confounded by the presence of non-malignant cells. As a result, it remains unclear whether tumour-cell-intrinsic states exist, and whether these represent stable disease subtypes or are dynamically remodelled during disease progression and treatment. Here, we address this question using a living biobank of patient-derived ovarian cancer models (OCMs) cultured as purified tumour-cell populations under uniform conditions. RNA sequencing followed by unsupervised non-negative matrix factorisation (NMF) revealed a robust, hierarchical architecture comprising three core tumour-cell-intrinsic subtypes: the Alpha cluster, marked by cell-cycle deregulation and E2F-driven replication stress; the Beta cluster, defined by tumour-cell-intrinsic immune mimicry and inflammatory signalling; and the Gamma cluster, characterised by epithelial identity, extracellular matrix engagement, and metabolic adaptation. At higher clustering resolution, a fourth cluster, Delta, emerged as a Gamma sub-lineage distinguished by a vesicle-oriented, neuronal-like secretory programme. By projecting cluster labels onto a subset of matched longitudinal OCMs using non-negative least squares, we show that while some tumours retain stable subtype identities, others display transcriptional plasticity, including transitions from epithelial-like Gamma states to more proliferative or secretory phenotypes. Together, these findings define the core architecture and dynamic potential of tumour-cell-intrinsic transcriptional states within HGSOC, thereby bridging legacy bulk classifications with emerging single-cell insights, establishing a framework for more precise patient stratification.

cancer biology↗

bioRxiv2025

Exploring CDK4/6-Dependencies in ex vivo Ovarian Cancer Models

Ovarian cancer (OC) is a clinically and molecularly heterogeneous disease with limited treatment options for the majority of patients, particularly those with homologous-recombination-proficient high-grade serous ovarian cancer (HGSOC) and rarer subtypes such as low-grade serous ovarian cancer. Deregulation of the G1/S cell cycle network is common across all subtypes, suggesting subtype-agnostic vulnerabilities. Here, we assessed CDK4/6 dependency using the selective inhibitor palbociclib across 20 patient-derived ex vivo OC models. A subset of models, including four HGSOC and six rarer subtypes, exhibited marked sensitivity to palbociclib, characterised by low CDKN2A/CDKN2B expression, Rb hypophosphorylation, and G1 cell cycle arrest. In contrast, resistant models showed high CDKN2A expression and reduced or absent RB1. Notably, ABCB1 overexpression--a known resistance mechanism in OC--did not mediate palbociclib resistance. Analysis of longitudinal models revealed diminished CDK4/6 dependency following treatment, accompanied by increased CDKN2A expression. These findings support a model of G1/S control in which tumours diverge into CDK4/6- or CDK2-driven proliferation states, with CDKN2A as a potential biomarker to guide patient selection. The predominance of CDK4/6-inhibitor-resistant HGSOC highlights a priority population for CDK2-targeted therapies, offering new treatment strategies for patients with otherwise limited options.

cancer biology↗

bioRxiv2025

Automated sleep scoring in hibernating and non-hibernating American black bears

Hibernating bears show remarkable metabolic suppression. Their decline in core body temperature (Tb) is moderate (from 38{degrees}C to 30-35{degrees}C), but their metabolism declines as much as 75%. To understand the role of sleep in this hypometabolic state, we recorded biotelemetrically EEG, EOG and EMG data over 3500 days from 16 captive American black bears in and out of hibernation under semi-natural conditions. This data set is too large to score manually for Wake, REM- and NREM sleep, so we tested two machine learning classifiers: (1) Somnotate trained on multiple one-day recordings, and (2) Somnivore, trained on a small subset from each recording. As automated scoring methods have not been applied to hibernating species before, a major concern is the effect changing brain temperature has on the EEG and on the machine learning based detection. Therefore, we selected reference data using consensus by 3 manual sleep scorers from each of 6 bears, two one-day recordings at the highest and lowest body temperatures during hibernation when Tb was oscillating in multiday cycles, and a non-hibernating one-day recording in summer. Somnotate results were excellent when trained separately for hibernating and non-hibernating data. Training Somnotate separately for high and low Tb within hibernation did not improve results further. Sleep times in hibernation were about 2x that in summer for both automated scores and manual scores (p<0.0001). There were no significant differences in occupancy of vigilance states between automated and manual scores in hibernation (p>0.05), but a small overestimate of sleep time in summer (p<0.05). Both applications yielded F-measures against manual scores in the 0.90-0.98 range. Outliers in the 0.67-0.88 range were correlated between the two applications, indicating that specific files are more challenging to annotate. We conclude that both applications have accuracies approaching that of manual scorers when trained on high quality data.

neuroscience↗

bioRxiv2023

Remodelling of Skeletal Muscle Myosin Metabolic States in Hibernating Mammals

Hibernation is a period of metabolic suppression utilized by many small and large mammal species to survive during winter periods. As the underlying cellular and molecular mechanisms remain incompletely understood, our study aimed to determine whether skeletal muscle myosin and its metabolic efficiency undergo alterations during hibernation to optimize energy utilization. We isolated muscle fibers from small hibernators, Ictidomys tridecemlineatus and Eliomys quercinus and larger hibernators, Ursus arctos and Ursus americanus. We then conducted loaded Mant-ATP chase experiments alongside X-ray diffraction to measure resting myosin dynamics and its ATP demand. In parallel, we performed multiple proteomics analyses. Our results showed a preservation of myosin structure in U. arctos and U. americanus during hibernation, whilst in I. tridecemlineatus and E. quercinus, changes in myosin metabolic states during torpor unexpectedly led to higher levels in energy expenditure of type II, fast-twitch muscle fibers at ambient lab temperatures (20{degrees}C). Upon repeating loaded Mant-ATP chase experiments at 8{degrees}C (near the body temperature of torpid animals), we found that myosin ATP consumption in type II muscle fibers was reduced by 77-107% during torpor compared to active periods. Additionally, we observed Myh2 hyper-phosphorylation during torpor in I. tridecemilineatus, which was predicted to stabilize the myosin molecule. This may act as a potential molecular mechanism mitigating myosin-associated increases in skeletal muscle energy expenditure during periods of torpor in response to cold exposure. Altogether, we demonstrate that resting myosin is altered in hibernating mammals, contributing to significant changes to the ATP consumption of skeletal muscle. Additionally, we observe that it is further altered in response to cold exposure and highlight myosin as a potentially contributor to skeletal muscle non-shivering thermogenesis.

physiology↗