Biology subjects

O'Donnell, C.

Publications and source records attributed to O'Donnell, C..

15 recordsLinked to original sources

bioRxiv2026

Resource competition controls the length-dependence of synaptic protein synthesis

Synaptic plasticity is dependent on the availability of specific proteins at the synapse with distinct requirements for potentiation and depression. Specifically, experimental evidence suggests an upregulation of short proteins and a downregulation for long proteins during long-term depression (LTD). The mechanism of this length-dependent protein synthesis regulation is yet unknown. In this work, we propose an computational model of ribosomal regulation of protein synthesis than can reproduce the length-dependent shift in protein expression seen during ribosomal elevations. By analysing the model dynamics, we discover that this effect is due to a shift from initiation-restricted to elongation-restricted protein synthesis. This works offers a novel explanation for the loss of ribosomal control of protein synthesis due to saturation in pathological ribosome enriched conditions. This new theoretical model of ribosomal regulation of protein synthesis is capable of explaining length-dependent shifts in protein synthesis.

neuroscience↗

bioRxiv2026

Non-random brain connectome wiring enables robust and efficient neural network function under high sparsity

The connectivity of brain networks is extremely sparse due to metabolic, physical and spatial constraints. Although wiring sparsity can confer computational advantages for biological and artificial neural networks, sparse networks require fine parameter tuning and exhibit strong sensitivity to perturbations. How brains achieve their efficiency and robustness is unclear. Here we addressed this by analysing the dynamical properties of Echo State Networks with wiring based on the Drosophila melanogaster fruit fly connectome, compared with sparsity-matched random-wiring networks. We evaluated these networks on a set of eight cognitive tasks, and found that connectome-based neural networks (CoNNs) typically showed narrowly distributed task engagement across their neurons. The importance of a neuron for task performance correlated with its node degree, local clustering, and selfrecurrency, and these correlations were stronger in CoNNs than in random networks. CoNNs were more robust to neuronal loss, retaining their task performance and beneficial dynamical properties such as criticality and spectral radius better than random networks. Similarly, CoNNs were more robust to hyperparameter variations in both input and recurrent weight scaling. Using theoretical arguments and numerical simulations, we show that excess CoNN node self-recurrency is sufficient to explain this enhanced robustness. Overall, these results identify non-random features of connectome wiring that allow brains to reconcile extreme sparsity with reliable computation. SignificanceBrain networks support robust computation even though they operate under extreme wiring sparsity due to metabolic and spatial constraints. While sparse networks typically require fine-tuning and are sensitive to perturbations, we show that biological connectomes support specialised, efficient task engagement and remain robust to neuron loss and parameter variation. We identify excess neuronal selfrecurrency as a key structural feature underlying this stability. These results reveal how non-random connectivity stabilises computation in extremely sparse networks, providing principles for understanding brain function and designing robust, efficient artificial neural systems.

neuroscience↗

bioRxiv2025

Design of SARS-CoV-2 RBD Immunogens to Focus Immune Responses Towards Conserved Coronavirus Epitopes

SARS-CoV-2 continues to evolve, with new variants emerging that evade pre-existing immunity and limit the efficacy of existing vaccines. One approach towards developing superior, variant-proof vaccines is to engineer immunogens that preferentially elicit antibodies with broad cross-reactivity against SARS-CoV-2 and its variants by targeting conserved epitopes on spike. The inner and outer faces of the Receptor Binding Domain (RBD) are two such conserved regions targeted by antibodies that recognize diverse human and animal coronaviruses. To promote the elicitation of such antibodies by vaccination, we engineered "resurfaced" RBD immunogens that contained mutations at exposed RBD residues outside the target epitopes. In the context of pre-existing immunity, these vaccine candidates aim to disfavor the elicitation of strain-specific antibodies against the immunodominant Receptor Binding Motif (RBM) while boosting the induction of inner and outer face antibodies. The engineered resurfaced RBD immunogens were stable, lacked binding to monoclonal antibodies with limited breadth, and maintained strong interactions with target broadly neutralizing antibodies. When used as vaccines, they limited humoral responses against the RBM as intended. Multimerization on nanoparticles further increased the immunogenicity of the resurfaced RBDs immunogens, thus supporting resurfacing as a promising immunogen design approach to rationally shift natural immune responses to develop more protective vaccines.

immunology↗

bioRxiv2024

Hierarchical Bayesian modeling of multi-region brain cell count data

We can now collect cell-count data across whole animal brains quantifying recent neuronal activity, gene expression, or anatomical connectivity. This is a powerful approach since it is a multi-region measurement, but because the imaging is done post-mortem, each animal only provides one set of counts. Experiments are expensive and since cells are counted by imaging and aligning a large number of brain sections, they are time-intensive. The resulting datasets tend to be under-sampled with fewer animals than brain regions. As a consequence, these data are a challenge for traditional statistical approaches. We present a standard partially-pooled Bayesian model for multi-region cell-count data and apply it to two example datasets. These examples demonstrate that hierarchical Bayesian methods are well suited to these data. In both cases the Bayesian model outperformed standard parallel t-tests. Overall, inference for cell-count data is substantially improved by the ability of the Bayesian approach to capture nested data and by its rigorous handling of uncertainty in under-sampled data.

neuroscience↗

bioRxiv2024

Competition effects regulating the composition of the microRNA pool

MicroRNAS (miRNAs) are short non-coding RNAs that can repress mRNA translation to regulate protein synthesis. During their maturation, multiple types of pre-miRNAs compete for a shared pool of the enzyme Dicer. It is unknown how this competition for a shared resource influences the relative expression of mature miRNAs. We study this process in a computational model of pre-miRNA maturation, fitted to in vitro Drosophila S2 cell data. We find that those pre-miRNAs which efficiently interact with Dicer outcompete other pre-miRNAs, when Dicer is scarce. To test our model predictions, we re-analysed previously published ex vivo mouse striatum data with reduced Dicer1 expression. We calculated a proxy measure for pre-miRNA affinity to TRBP (a protein which loads pre-miRNAs to Dicer). This measure well-predicted mature miRNA levels in the data, validating our assumptions. We used this as a basis to test the the models predictions through further analysis of the data. We found that pre-miRNAs with strong TRBP association are over-represented in competition conditions, consistent with the modelling. Finally using further simulations, we discovered that pre-miRNAs with low maturation rates can affect the mature miRNA pool via competition among pre-miRNAs. Overall, this work presents evidence of pre-miRNA competition regulating the composition of mature miRNAs.

molecular biology↗

bioRxiv2024

Deep learning-based location decoding reveals that across-day representational drift is better predicted by rewarded experience than time

Neural representations of locational and spatial relations in the hippocampus and related brain areas change over timescales of days-weeks, even in familiar contexts and when behavior appears stable. It remains unclear how this representational drift is driven by combinations of the passage of time, general experience or specific features of experience. We present a novel deep-learning approach for measuring network-level representational drift, quantifying drift as the rate of change in decoder error of deep neural networks as a function of train-test lag. Using this method, we analyse a longitudinal dataset of 0.5-475 Hz broadband local field potential (LFP) data recorded from dorsal hippocampal CA1, medial prefrontal cortex and parietal cortex of six rats over [~]30 days, during learning of a spatial navigation task in an initially unfamiliar environment. All three brain regions contained clear spatial representations which evolve and drift over training sessions. We find that the rate of drift slows for later training sessions. Finally, we find that drift is statistically better explained by task-relevant experiences within the maze, rather than the passage of time or number of sessions the animal spent on the maze. While previous research has focused on drift as a measure of the changes in spiking activities of units, here we examine drift as a measure of change in oscillatory activity of local field potentials; our approach of using of deep neural networks to quantify drift in broadband neural time series unlocks new possibilities for defining the drivers and functional consequences of representational drift.

neuroscience↗

bioRxiv2023

Virome analysis of New Zealand's bats reveals cross-species viral transmission among the Coronaviridae

1.The lesser short-tailed bat (Mystacina tuberculata) and the long-tailed bat (Chalinolobus tuberculatus) are Aotearoa New Zealands only native extant terrestrial mammals and are believed to have migrated from Australia. Long-tailed bats arrived in New Zealand an estimated two million years ago and are closely related to other Australian bat species. Lesser short-tailed bats, in contrast, are the only extant species within the Mystacinidae and are estimated to have been living in isolation in New Zealand for the past 16-18 million years. Throughout this period of isolation, lesser short-tailed bats have become one of the most terrestrial bats in the world. Through a metatranscriptomic analysis of guano samples from eight locations across New Zealand we aimed to characterise the viromes of New Zealands bats and determine whether viruses have jumped between these species over the past two million years. High viral richness was observed among long-tailed bats with viruses spanning seven different viral families. In contrast, no bat-specific viruses were identified in lesser short-tailed bats. Both bat species harboured an abundance of likely dietary- and environmental-associated viruses. We also identified alphacoronaviruses in long-tailed bat guano that had previously been identified in lesser short-tailed bats, suggesting that these viruses had jumped the species barrier after long-tailed bats migrated to New Zealand. Of note, an alphacoronavirus species discovered here possessed a complete genome of only 22,416 nucleotides with entire deletions or truncations of several non-structural proteins, thereby representing what is possibly the shortest genome within the Coronaviridae identified to date. Overall, this study has revealed a diverse range of novel viruses harboured by New Zealands only native terrestrial mammals, in turn expanding our understanding of bat viral dynamics and evolution globally.

microbiology↗

bioRxiv2023

Dendritic spine neck plasticity controls synaptic expression of long-term potentiation

Dendritic spines host glutamatergic excitatory synapses and compartmentalize biochemical signalling underlying synaptic plasticity. The narrow spine neck that connects the spine head with its parent dendrite is the crucial structural element of this compartmentalization. Both neck morphology and its molecular composition differentially regulate exchange of molecular signals between the spine and rest of the neuron. Although these spine neck properties themselves show activity-dependent plasticity, it remains unclear what functional role spine neck plasticity plays in synaptic plasticity expression. To address this, we built a data-constrained biophysical computational model of AMPA receptor (AMPAR) trafficking and intracellular signalling involving Ca2+/calmodulin-dependent kinase II (CaMKII) and the phosphatase calcineurin in hippocampal CA1 neurons, which provides new mechanistic insights into spatiotemporal AMPAR dynamics during long-term potentiation (LTP). Using the model, we tested how plasticity of neck morphology and of neck septin7 barrier, which specifically restricts membrane protein diffusion, affect LTP. We found that spine neck properties control LTP by regulating the balance between AMPAR and calcineurin escape from the spine. Neck plasticity that increases spine-dendrite coupling reduces LTP by allowing more AMPA receptors to diffuse away from the synapse. Surprisingly, neck plasticity that decreases spine-dendrite coupling can also reduce LTP by trapping calcineurin, which dephosphorylates AMPARs. Further simulations showed that the precise timescale of neck plasticity, relative to AMPAR and enzyme diffusion and phosphorylation dynamics, critically regulates LTP. These results suggest a new mechanistic and experimentally-testable theory for how spine neck plasticity regulates synaptic plasticity.

neuroscience↗

bioRxiv2022

Fast-local and slow-global neural ensembles in the mouse brain

Ensembles of neurons are thought to be co-active when participating in brain computations. However, it is unclear what principles determine whether an ensemble remains localised within a single brain region, or spans multiple brain regions. To address this, we analysed electrophysiological neural population data from hundreds of neurons recorded simultaneously across nine brain regions in awake mice. At fast sub-second timescales, spike count correlations between pairs of neurons in the same brain region were stronger than for pairs of neurons spread across different brain regions. In contrast at slower timescales, within- and between-region spike count correlations were similar. Correlations between high-firing-rate neuron pairs showed a stronger dependence on timescale than low-firing-rate neuron pairs. We applied an ensemble detection algorithm to the neural correlation data and found that at fast timescales each ensemble was mostly contained within a single brain region, whereas at slower timescales ensembles spanned multiple brain regions. These results suggest that the mouse brain may perform fast-local and slow-global computations in parallel. AUTHOR SUMMARYIn this study we analysed publicly available neural population electrophysiology data from nine brain regions in awake mice. To discover neural ensembles, we applied community detection algorithms to the spike count correlation matrices estimated from the data. We repeated the analysis at different timescales, ranging from 10 milliseconds to 3 seconds. We found that at fast timescales < 1 s, neural ensembles tended to be localised within single brain regions. In contrast at slower timescales of > 1 s, ensembles tended to be include neurons spread across multiple brain regions. Most of this effect was due to high-firing-rate neurons.

neuroscience↗

bioRxiv2021

Reduced expression of the psychiatric risk gene DLG2 (PSD93) impairs hippocampal synaptic integration and plasticity

Background: Genetic variations indicating loss of function in the DLG2 gene have been associated with markedly increased risk for schizophrenia, autism spectrum disorder, and intellectual disability. DLG2 encodes the postsynaptic scaffolding protein DLG2 (PSD93) that interacts with NMDA receptors, potassium channels, and cytoskeletal regulators but the net impact of these interactions on synaptic plasticity, likely underpinning cognitive impairments associated with these conditions, remains unclear. Methods: Hippocampal CA1 neuronal excitability and synaptic function were investigated in a novel clinically relevant heterozygous Dlg2+/- rat model using ex vivo patch-clamp electrophysiology, pharmacology, and computational modelling. Results: Dlg2+/- rats had increased NMDA receptor-mediated synaptic currents and, conversely, impaired associative long-term potentiation. This impairment resulted from an increase in potassium channel function leading to a decrease in input resistance and reduced supra-linear dendritic integration during induction of associative long-term potentiation. Enhancement of dendritic excitability by blockade of potassium channels or activation of muscarinic M1 receptors with selective allosteric agonist 77-LH-28-1 reduced the threshold for dendritic integration and 77-LH-28-1 rescued the associative long-term potentiation impairment in the Dlg2+/- rats. Conclusions: Despite increasing synaptic NMDA receptor currents, the combined impact of reduced DLG2 impairs synaptic integration in dendrites resulting in disrupted associative synaptic plasticity. This biological phenotype can be reversed by compound classes used clinically such as muscarinic M1 receptor agonists and is therefore a potential target for therapeutic intervention.

neuroscience↗

bioRxiv2021

A stochastic model of hippocampal synaptic plasticity with geometrical readout of enzyme dynamics

Discovering the rules of synaptic plasticity is an important step for understanding brain learning. Existing plasticity models are either 1) top-down and interpretable, but not flexible enough to account for experimental data, or 2) bottom-up and biologically realistic, but too intricate to interpret and hard to fit to data. To avoid the shortcomings of these approaches, we present a new plasticity rule based on a geometrical readout mechanism that flexibly maps synaptic enzyme dynamics to predict plasticity outcomes. We apply this readout to a multi-timescale model of hippocampal synaptic plasticity induction that includes electrical dynamics, calcium, CaMKII and calcineurin, and accurate representation of intrinsic noise sources. Using a single set of model parameters, we demonstrate the robustness of this plasticity rule by reproducing nine published ex vivo experiments covering various spike-timing and frequency-dependent plasticity induction protocols, animal ages, and experimental conditions. Our model also predicts that in vivo-like spike timing irregularity strongly shapes plasticity outcome. This geometrical readout modelling approach can be readily applied to other excitatory or inhibitory synapses to discover their synaptic plasticity rules.

neuroscience↗

bioRxiv2021

Acetylcholine boosts dendritic NMDA spikes in a CA3 pyramidal neuron model

Acetylcholine has been proposed to facilitate the formation of memory ensembles within the hippocampal CA3 network, by enhancing plasticity at CA3-CA3 recurrent synapses. Regenerative NMDA receptor (NMDAR) activation in CA3 neuron dendrites (NMDA spikes) increase synaptic Ca2+ influx and can trigger this synaptic plasticity. Acetylcholine inhibits potassium channels which enhances dendritic excitability and therefore could facilitate NMDA spike generation. Here, we investigate NMDAR-mediated nonlinear synaptic integration in stratum radiatum (SR) and stratum lacunosum moleculare (SLM) dendrites in a reconstructed CA3 neuron computational model and study the effect of acetylcholine on this nonlinearity. We found that distal SLM dendrites, with a higher input resistance, had a lower threshold for NMDA spike generation compared to SR dendrites. Simulating acetylcholine by blocking potassium channels (M-type, A-type, Ca2+-activated, and inwardly-rectifying) increased dendritic excitability and reduced the number of synapses required to generate NMDA spikes, particularly in the SR dendrites. The magnitude of this effect was heterogeneous across different dendritic branches within the same neuron. These results predict that acetylcholine facilitates dendritic integration and NMDA spike generation in selected CA3 dendrites which could strengthen connections between specific CA3 neurons to form memory ensembles. Highlights- Using biophysical computational models of CA3 pyramidal neurons we estimated the quantitative effects of acetylcholine on nonlinear synaptic integration. - Nonlinear NMDA spikes can be triggered by fewer synapses in distal dendrites due to increased local input resistance. - Acetylcholine broadly reduces the number of synapses needed to trigger NMDA spikes, but the magnitude of the effect varies across dendrite branches within a single neuron. - No single potassium channel type is the dominant mediator of the excitability effects of acetylcholine.

neuroscience↗

bioRxiv2020

Biomarker model for cancer: Development of fast LC-MS/MS method for reduced and oxidized glutathione

In recent years, there have been significant efforts devoted to countering the challenge of detecting cancer in early stages. Reduced glutathione (GSH) plays an important role in the antioxidant system and is required for the maintenance of the redox status of the cell, defense against free radicals and detoxification of toxic compounds. GSH may be converted to oxidized glutathione (GSSG) during the oxidative stress that it regularly undergoes when combating cancer cells. Therefore, the ratio of GSH to the total amount of glutathione can be an extremely useful biomarker for detecting cancer. However, there has yet to be an effective method of detecting and quantifying glutathione in cells, making it extremely difficult to use as a biomarker. In this study, we have created an effective method of detecting both forms of glutathione, utilizing high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The analysis time took less than 1 minute, and we were able to quantify both GSH and GSSG in one method. The limit of quantitation is 1 ng/mL, and we ran three trials, each examining a range of concentrations, from 1 to 500 ng/ml of GSH and GSSG. Results were calculated using peak area ratios, using HPLC-MS/MS technology, we were able to determine both the amounts of GSH and GSSG in a single method, creating a fast, reliable, non-invasive, and cost-effective method of testing early stages of cancer.

biochemistry↗

bioRxiv2017

MEF2C phosphorylation is required for chemotherapy resistance in acute myeloid leukemia

HIGHLIGHTSO_LIMEF2C S222 phosphorylation is a specific marker of chemotherapy resistance in diagnostic AML patient specimens.\nC_LIO_LIMEF2C S222 phosphorylation is dispensable for normal hematopoiesis in mice, as established using genome editing in vivo, but is required for MLL-AF9 induced leukemogenesis.\nC_LIO_LIMARK kinases specifically phosphorylate MEF2C S222, potentiating its transcriptional activity.\nC_LIO_LIChemical inhibition of MARK-induced MEF2C phosphorylation overcomes chemotherapy resistance of and exhibits selectivity toxicity against MEF2C-activated human AML cells.\nC_LI\n\nSUMMARYIn acute myeloid leukemia, chemotherapy resistance remains prevalent and poorly understood. Using functional proteomics of patient AML specimens, we identified MEF2C S222 phosphorylation as a specific marker of primary chemoresistance. We found that Mef2cS222A/S222A knock-in mutant mice engineered to block MEF2C phosphorylation exhibited normal hematopoiesis, but were resistant to leukemogenesis induced by MLL-AF9. MEF2C phosphorylation was required for leukemia stem cell maintenance, and induced by MARK kinases in cells. Treatment with the selective MARK inhibitor MRT199665 caused apoptosis of MEF2C-activated human AML cell lines and primary patient specimens, but not those lacking MEF2C phosphorylation. These findings identify kinase-dependent dysregulation of transcription factor control as a determinant of therapy response in AML, with immediate potential for improved diagnosis and therapy for this disease.

cancer biology↗

bioRxiv2016

Multidimensional imbalances in cortical circuit activity in Fragile-X Syndrome mice

A leading theory for multiple brain disorders, such as schizophrenia and autism, is that they arise from developmental imbalances in excitatory and inhibitory (E/I) brain circuitry. However, it is unclear whether this simple 1-dimensional model is sufficiently rich to capture the set of key alterations of neural circuit processing in brain disorders. Here we used a combination of computational modeling and large-scale analysis of in vivo 2-photon Ca2+ imaging data from somatosensory cortex of wild-type and Fmr1 knock-out (KO) mice, a model of Fragile X syndrome, to test the E/I imbalance model. Our main findings were: 1) the E/I imbalance model was not rich enough to capture the alterations in neural activity statistics in Fmr1 KO mice; 2) in a computational model we found greatly varying magnitudes and directions of effects of synaptic and cellular properties on network activity; 3) evidence for opposite changes in circuit properties of Fmr1 KO mice at different stages of development; 4) a reduction in the entropy of circuit activity in young Fmr1 KO mice compared to wild-type, but an opposite increase in adult Fmr1 KO mice. These findings suggest qualitatively new strategies for developing treatments for Fragile-X Syndrome and related disorders.

neuroscience↗