Biology subjects

Jones, N. S.

Publications and source records attributed to Jones, N. S..

At least 19 recordsLinked to original sources

bioRxiv2026

Epigenetic and brain age across development: Performance and associations in the MIND consortium

Understanding how biological age measures perform across development lays the groundwork for investigations into lifespan trajectories of healthy aging. We provide the most comprehensive assessment of epigenetic and brain age models across development (birth to 24 years; [≤]20,917 observations across 15 cohorts), evaluating how these models associate with chronological age and with each other, and how these associations change across development. Chronological age-prediction accuracy of epigenetic and brain age models was modest and varied substantially. Accuracy improved with age and stabilized by middle childhood. Few brain and fewer epigenetic clocks performed stably and well across all developmental stages. Performance was better when age range and tissue corresponded between training and testing data. Associations between epigenetic-brain age residuals were small, and changed little across development, tissues or clock generation. Given this developmentally dynamic system of epigenetic-brain age performances and associations, we give key recommendations to improve developmental research in this field.

neuroscience↗

bioRxiv2026

Large-scale single-cell mapping of concealed mitochondrial ageing

Mitochondrial DNA (mtDNA) mutations have been linked to ageing. We find that the mtDNA mutations detected in routine bulk sequencing are greatly outnumbered by a class of concealed mutations that, despite being in a proliferative tissue, show little negative selection. Combining new scATAC-seq experiments with existing scRNA-seq, we map mtDNA mutations across 2.7 million cells from 311 individuals, covering 11 proliferative cell types and 5 tissues. We find that low-prevalence mutations (in[≤] 1% of assayed cells), concealed from routine bulk sequencing, accumulate markedly with age, with mutations unique to single cells (cryptic) predominating. We find a near universal accumulation rate across cell types and tissues, with, by age 50, approximately 40% of cells having at least one mutation affecting 30% of cellular mtDNA or more. At a correlative level we find that human age can be predicted from these concealed mutations but there is evidence of, possibly physiological, inter-individual variation. These low-prevalence mutations (unlike mutations tracking larger clones) show limited negative selection and coincide with gene-expression changes marking dysfunction.

genomics↗

bioRxiv2026

Single-nucleoid imaging in whole cells defines the dynamics of the mtDNA life cycle

The mitochondrial genome (mtDNA) is essential for oxidative phosphorylation, and mammalian cells typically contain hundreds or thousands of copies. Although specific aspects of mtDNA replication and degradation have been explored, a complete account of the mtDNA life cycle has remained elusive, particularly for nondividing cells. Using super-resolution and 4D lattice light-sheet imaging, we quantified the full mtDNA life cycle in quiescent primary human cells. We show that cells maintain steady state by replicating and degrading 1.5 {+/-} 0.2% of total mtDNA content each hour, a remarkably rapid flux. Younger mtDNA molecules are closer to the nucleus, spared from degradation, and serve as foci for further replication events. Non-proliferative cells regulate nucleoid density within the mitochondrial network, and the mitochondrial membrane potential sustains mtDNA copy number. These findings provide a foundational understanding of the dynamics underlying mtDNA homeostasis and a mechanism explaining how mutations accumulate in aging and disease.

cell biology↗

bioRxiv2025

Early adversity potentiates prefrontal - amygdala communication through CRH+ glutamatergic neurons

Early life adversity (ELA) has lasting impacts on emotion circuit function1. Exposure to early maltreatment, neglect, or household instability is associated with enduring alterations in threat evaluation and response, hallmarks of mood and anxiety disorders2-4. These alterations often emerge during adolescence and are characterized by excessive avoidance that limits engagement in rewarding experiences5,6. However, the neurodevelopmental mechanisms linking ELA to increased threat responses remain unclear. We identified a stress-sensitive medial prefrontal cortex (mPFC)-basolateral amygdala pathway that co-expresses corticotropin-releasing hormone (CRH) and glutamate. We find that ELA strengthens this pathway and increases threat avoidance behavior during adolescence. Optogenetic inhibition and developmental CRH knockdown in this pathway both rescue ELA-induced elevations in adolescent avoidance, whereas broadly reducing mPFC CRH expression in typically reared mice mimics ELA phenotypes. The same manipulations produced no behavioral effects in adults. Together, our findings indicate that cell type-specific CRH signaling regulates developmental circuit plasticity, biasing emotional circuit function and potentially contributing to psychiatric vulnerability.

neuroscience↗

bioRxiv2023

Developmentally distinct architectures in top-down circuits

The medial prefrontal cortex (mPFC) plays a key role in learning, mood and decision making, including in how individuals respond to threats1-6. mPFC undergoes a uniquely protracted development, with changes in synapse density, cortical thickness, long-range connectivity, and neuronal encoding properties continuing into early adulthood7-21. Models suggest that before adulthood, the slow-developing mPFC cannot adequately regulate activity in faster-developing subcortical centers22,23. They propose that during development, the enhanced influence of subcortical systems underlies distinctive behavioural strategies of juveniles and adolescents and that increasing mPFC control over subcortical structures eventually allows adult behaviours to emerge. Yet it has remained unclear how a progressive strengthening of top-down control can lead to nonlinear changes in behaviour as individuals mature24,25. To address this discrepancy, here we monitored and manipulated activity in the developing brain as animals responded to threats, establishing direct causal links between frontolimbic circuit activity and the behavioural strategies of juvenile, adolescent and adult mice. Rather than a linear strengthening of mPFC synaptic connectivity progressively regulating behaviour, we uncovered multiple developmental switches in the behavioural roles of mPFC circuits targeting the basolateral amygdala (BLA) and nucleus accumbens (NAc). We show these changes are accompanied by axonal pruning coinciding with functional strengthening of synaptic connectivity in the mPFC-BLA and mPFC-NAc pathways, which mature at different rates. Our results reveal how developing mPFC circuits pass through distinct architectures that may make them optimally adapted to the demands of age-specific challenges.

neuroscience↗

bioRxiv2023

Cryptic mitochondrial ageing coincides with mid-late life and is pathophysiologically informative in single cells across tissues and species

Ageing is associated with a range of chronic diseases and has diverse hallmarks. Mitochondrial dysfunction is implicated in ageing, and mouse-models with artificially enhanced mitochondrial DNA (mtDNA) mutation rates show accelerated ageing. A scarcely studied aspect of ageing, because it is invisible in aggregate analyses, is the accumulation of somatic mtDNA mutations which are unique to single cells (cryptic mutations). We find evidence of cryptic mtDNA mutations from diverse single-cell datasets, from three species, and discover: cryptic mutations constitute the vast majority of mtDNA mutations in aged post-mitotic tissues, that they can avoid selection, that their accumulation is consonant with theory, hits high levels coinciding with species specific mid-late life, and that their presence covaries with a majority of the hallmarks of ageing including protein misfolding and ER stress. We identify mechanistic links to ER stress experimentally and further give evidence that aged brain cells with high levels of cryptic mutations show markers of neurodegeneration and that calorie restriction slows the accumulation of cryptic mutations.

bioinformatics↗

bioRxiv2020

Soundscapes predict species occurrence in tropical forests

O_LIAccurate occurrence data is necessary for the conservation of keystone or endangered species, but acquiring it is usually slow, laborious, and costly. Automated acoustic monitoring offers a scalable alternative to manual surveys, but identifying species vocalisations requires large manually annotated training datasets, and is not always possible (e.g., for silent species). A new, intermediate approach is needed that rapidly predicts species occurrence without requiring extensive labelled data. C_LIO_LIWe investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species across a tropical forest degradation gradient in Sabah, Malaysia. We developed a machine-learning based approach to characterise species indicative soundscapes, training our models on a coarsely labelled manual point-count dataset. C_LIO_LISoundscapes successfully predicted the occurrence of 34 out of the 39 species across the two taxonomic groups, with area under the curve (AUC) metrics of up to 0.87 (Bold-striped Tit-babbler Macronus bornensis). The highest accuracies were achieved for common species with strong temporal occurrence patterns. C_LIO_LISoundscapes were a better predictor of species occurrence than above-ground biomass - a metric often used to quantify habitat quality across forest degradation gradients. C_LIO_LISynthesis and applications: Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species. This provides a new direction for audio data to deliver large-scale, accurate assessments of habitat suitability using cheap and easily obtained field datasets. C_LI

ecology↗

bioRxiv2020

Survival of the densest accounts for the expansion of mitochondrial mutations in ageing

The expansion of deleted mitochondrial DNA molecules has been associated with ageing 1,2, particularly in skeletal muscle fibres 3-5; its mechanism has remained unclear for three decades. Previous accounts have assigned a replicative advantage to the deletions 6-8, but there is evidence that cells can, instead, selectively remove defective mitochondrial DNA 9. Here we present a spatial model that, without a replicative advantage, but instead through a combination of enhanced density for mutants and noise, produces a wave of expanding mutations with speeds consistent with experimental data 10. A standard model based on replicative advantage yields waves that are too fast. We provide a formula that predicts that wave-speed drops with copy number, consonant with experimental data. Crucially, our model yields travelling waves of mutants even if mutants are preferentially eliminated. Additionally, we predict that experimentally observed mutant loads can be produced by de novo mutation rates that are drastically lower than previously thought for neutral models 11. Given this exemplar of how noise, density and spatial structure affect muscle age-ing, we introduce the mechanism of stochastic survival of the densest, an alternative to replicative advantage, that may underpin other evolutionary phenomena.

genetics↗

bioRxiv2020

SAFE Acoustics: an open-source, real-time eco-acoustic monitoring network in the tropical rainforests of Borneo

O_LIAutomated monitoring approaches offer an avenue to deep, large-scale insight into how ecosystems respond to human pressures. Since sensor technology and data analyses are often treated independantly, there are no open-source examples of end-to-end, real-time ecological monitoring networks. C_LIO_LIHere, we present the complete implementation of an autonomous acoustic monitoring network deployed in the tropical rainforests of Borneo. Real-time audio is uploaded remotely from the field, indexed by a central database, and delivered via an API to a public-facing website. C_LIO_LIWe provide the open-source code and design of our monitoring devices, the central web2py database and the ReactJS website. Furthermore, we demonstrate an extension of this infrastructure to deliver real-time analyses of the eco-acoustic data. C_LIO_LIBy detailing a fully functional, open-source, and extensively tested design, our work will accelerate the rate at which fully autonomous monitoring networks mature from technological curiosities, and towards genuinely impactful tools in ecology. C_LI

ecology↗

bioRxiv2019

Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labour-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we developed a generalisable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed ecosystem soundscapes from a wide variety of biomes into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, paving the way for real-time detection of irregular environmental behaviour including illegal activity. Our highly generalisable approach, and the common set of features, will enable scientists to unlock previously hidden insights from eco-acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts.

ecology↗

bioRxiv2019

Bladder pressure encoding by near-independent fibre subpopulations - implications for decoding

ObjectiveWe aim at characterising the encoding of bladder pressure (intravesical pressure) by a population of sensory fibres. This research is motivated by the possibility to restore bladder function in elderly patients or after spinal cord injury using implanted devices, so called bioelectronic medicines. For these devices, nerve-based estimation of intravesical pressure can enable a personalized and on-demand stimulation paradigm, which will be more effective and efficient. In this context, a better understanding of the encoding strategies employed by the body might in the future be exploited by informed decoding algorithms that enable a precise and robust bladderpressure estimation. ApproachTo this end, we apply information theory to microelectrode-array recordings from the cat sacral dorsal root ganglion while filling the bladder, conduct surrogate data studies to augment the data we have, and finally decode pressure in a simple informed approach. Main resultsWe find an encoding scheme by different main bladder neuron types that we divide into three response types (slow tonic, phasic, and derivative fibres). We show that an encoding by different bladder neuron types, each represented by multiple cells, offers reliability through within-type redundancy and high information rates through near-independence of different types. Our subsequent decoding study shows a potentially more robust decoding from mean responses of homogeneous cell pools. SignificanceWe have here, for the first time, analysed the encoding of intravesical pressure by a population of sensory neurons in a principled way using information theory. We show that even a simple adapted decoder can exploit the redundancy in the population to be more robust against cell loss. This work thus paves the way towards principled encoding studies in the periphery and towards a new generation of informed peripheral nerve decoders for bioelectronic medicines.

bioengineering↗

bioRxiv2019

Interpreting, forecasting, and controlling feeding behaviour using high-resolution data

Better understanding of feeding behaviour will be vital in reducing obesity and metabolic syndrome, but we lack a standard model that captures the complexity of feeding behaviour. We construct an accurate stochastic model of rodent feeding at the bout level in order to perform quantitative behavioural analysis. Analysing the different effects on feeding behaviour of PYY3-36, lithium chloride, GLP-1 and leptin shows the precise behavioural changes caused by each anorectic agent. Our analysis demonstrates that the changes in feeding behaviour evoked by the anorectic agents investigated not mimic satiety. In the ad libitum fed state during the light period, meal initiation is governed by complete stomach emptying, whereas in all other conditions there is a graduated response. We show how robust homeostatic control of feeding thwarts attempts to reduce food intake, and how this might be overcome. In silico experiments suggest that introducing a minimum intermeal interval or modulating gastric emptying can be as effective as anorectic drug administration.

animal behavior and cognition↗

bioRxiv2019

catch22: CAnonical Time-series CHaracteristics

Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147 000 time series, including biomedical datasets) and using a filtered version of the hctsa feature library (4791 features), we introduce a generically useful set of 22 CAnonical Time-series CHaracteristics, catch22. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties.

bioinformatics↗

bioRxiv2018

Efficient peripheral nerve firing characterisation through massive feature extraction

Peripheral nerve decoding algorithms form an important component of closed-loop bioelectronic medicines devices. For any decoding method, meaningful properties need to be extracted from the peripheral nerve signal as the first step. Simple measures such as signal amplitude and features of the Fourier power spectrum are most typically used, leaving open whether important information is encoded in more subtle properties of the dynamics. We here propose a feature-based analysis method that identifies changes in firing characteristics across recording sections by unsupervised dimensionality reduction in a high-dimensional feature-space and selects single efficiently implementable estimators for each characteristic to be used as the basis for a better decoding in future bioelectronic medicines devices.

bioengineering↗

bioRxiv2018

Precision identification and prediction of high mortality phenotypes and disease progression pathways in severe malaria without requiring longitudinal data

The parasite Plasmodium falciparum is the main cause of severe malaria (SM). Despite treatment with antimalarial drugs, more than 450,000 SM deaths are reported every year, mainly in African children. The diversity of clinical presentations associated with SM indicate important differences in disease pathogenesis that often require specific treatment, and this clinical heterogeneity of SM is largely unresolved. In this study, we apply new machine learning and inference tools for large-scale data analysis to dissect the heterogeneity in patterns of clinical features associated with SM in 2,695 Gambian children admitted to hospital with Plasmodium falciparum malaria. This quantitative analysis, including the powerful HyperTraPS algorithm for inference of progressive processes, reveals pathways of SM symptom progression and features predicting the severity of individual patient outcomes. Notably, our approach allows the identification and dissection of disease progression pathways without the need for longitudinal observations. Learning these pathways and features from this rich dataset allows us to construct several quantitative measures of the mortality risk associated with a patient presenting with a given set of symptoms. By independently surveying expert practitioners, we show that this data-driven approach agrees with and expands the current state of knowledge on malaria progression, while simultaneously providing a data-supported framework for predicting clinical risk.

pathology↗

bioRxiv2018

Mitochondrial network fragmentation modulates mutant mtDNA accumulation independently of absolute fission-fusion rates

Mitochondrial DNA (mtDNA) mutations cause severe congenital diseases but may also be associated with healthy aging. MtDNA is stochastically replicated and degraded, and exists within organelles which undergo dynamic fusion and fission. The role of the resulting mitochondrial networks in the time evolution of the cellular proportion of mutated mtDNA molecules (heteroplasmy), and cell-to-cell variability in heteroplasmy (heteroplasmy variance), remains incompletely understood. Heteroplasmy variance is particularly important since it modulates the number of pathological cells in a tissue. Here, we provide the first wide-reaching theoretical framework which bridges mitochondrial network and genetic states. We show that, under a range of conditions, the (genetic) rate of increase in heteroplasmy variance and de novo mutation are proportionally modulated by the (physical) fraction of unfused mitochondria, independently of the absolute fission-fusion rate. In the context of selective fusion, we show that intermediate fusion/fission ratios are optimal for the clearance of mtDNA mutants. Our findings imply that modulating network state, mitophagy rate and copy number to slow down heteroplasmy dynamics when mean heteroplasmy is low could have therapeutic advantages for mitochondrial disease and healthy aging.

cell biology↗

bioRxiv2017

Robust, real-time and autonomous monitoring of ecosystems with an open, low-cost, networked device

O_LIAutomated methods of monitoring ecosystems provide a cost-effective way to track changes in natural systems dynamics across temporal and spatial scales. However, methods of recording and storing data captured from the field still require significant manual effort.\nC_LIO_LIHere we introduce an open source, inexpensive, fully autonomous ecosystem monitoring unit for capturing and remotely transmitting continuous data streams from field sites over long time-periods. We provide a modular software framework for deploying various sensors, together with implementations to demonstrate proof of concept for continuous audio monitoring and time-lapse photography.\nC_LIO_LIWe show how our system can outperform comparable technologies for fractions of the cost, provided a local mobile network link is available. The system is robust to unreliable network signals and has been shown to function in extreme environmental conditions, such as in the tropical rainforests of Sabah, Borneo.\nC_LIO_LIWe provide full details on how to assemble the hardware, and the open-source software. Paired with appropriate automated analysis techniques, this system could provide spatially dense, near real-time, continuous insights into ecosystem and biodiversity dynamics at a low cost.\nC_LI

ecology↗

bioRxiv2017

Energetic Costs Of Cellular And Therapeutic Control Of Stochastic mtDNA Populations

Mitochondrial DNA (mtDNA) copy numbers fluctuate over time due to stochastic cellular dynamics. Understanding mtDNA dynamics and the accumulation of mutations is vital for understanding mitochondrial-related diseases. Here, we use stochastic modelling to derive general results for the impact of cellular control on mtDNA populations, the cost to the cell of different mtDNA states, and the optimisation of therapeutic control of mtDNA populations. We provide theoretical evidence that an increasing mtDNA variance can increase the energetic cost of maintaining a tissue, that intermediate levels of heteroplasmy can be more detrimental than ho-moplasmy even for a dysfunctional mutant, that het-eroplasmy distribution (not mean alone) is crucial for the success of gene therapies, and that long-term rather than short intense gene therapies are more likely to beneficially impact mtDNA populations. New experiments validate our predictions on heteroplasmy dependence of therapeutic outcomes.

cell biology↗