Biology subjects

O'Leary, S.

Publications and source records attributed to O'Leary, S..

8 recordsLinked to original sources

bioRxiv2025

Preclinical immunogenicity of the LP.8.1-adapted BNT162b2 COVID-19 vaccine

SARS-CoV-2 evolution toward antigenically distinct lineages drives escape from host immunity. JN.1 lineage derivatives have recently dominated the global epidemiologic landscape. In preclinical models, an LP.8.1-adapted BNT162b2 vaccine elicited higher serum neutralizing antibody responses against contemporary, circulating JN.1 sublineages, including the currently dominant XFG, as compared to JN.1-, KP.2- and XEC-adapted vaccines. These findings supported the selection of an LP.8.1-adapted vaccine for the composition of the 2025-26 COVID-19 vaccine formula.

immunology↗

bioRxiv2025

Building an Ecosystem of Seizure Localization Methods: Neural Fragility as the First Step

The current treatment for drug-resistant epilepsy (DRE) is surgical intervention, which relies on accurate identification of the seizure onset zone (SOZ) using intracranial EEG (iEEG) data. iEEG analysis with computational epileptogenic zone identification algorithms (CEZIAs) is a promising step towards better SOZ localization and surgical outcomes. A key step in validation and adoption of CEZIAs is to allow for widespread shared development and validation of code and data. We describe a set of three R packages to achieve this goal. Our ecosystem of seizure localization methods involves a straightforward analysis pipeline, standardized data formatting and storage, and completely documented and open-source code. The TableContainer package allows for easy storage and manipulation of table data, serving as groundwork for the Epoch package, which is specifically geared towards iEEG data. The Epoch package allows for cropping, resampling, and visualization of iEEG data and provides publicly downloadable iEEG data for reproducibility. Finally, the EZFragility package uses these two foundational packages to analyze iEEGs for SOZ localization using the Neural Fragility method described by Li et al. EZFragility was built using the same core principles as the original method but included several enhancements in computational efficiency and user experience. It accurately reproduces neural fragility results for both sample patients used in the original paper. This project serves as the first step towards building an open-source, reproducible ecosystem of seizure localization methods in R. Future steps include the addition of other CEZIAs using the framework and sample data already made available by these packages. Significance StatementLocalization of the seizure onset zone (SOZ) is a critical step in surgical treatment of drug-resistant epilepsy. Computational epileptogenic zone identification algorithms (CEZIAs) are promising potential tools to aid in clinical decision-making. However, shared development and verification of CEZIAs is difficult due to obscured source code, variable data structures, and limited data availability. The EZFragility software package1 is the first step in building a collaborative ecosystem of CEZIAs that can be downloaded, tested, and used without these roadblocks. EZFragility1 and its dependent packages TableContainer2 and Epoch3 are freely available on the Comprehensive R Archive Network (CRAN), with source code viewable on GitHub. They provide an open-source framework for CEZIA code, data formatting, and data access, all with extensive documentation.

neuroscience↗

bioRxiv2025

HIV inhibits Warburg metabolism in human macrophages infected with Mycobacterium tuberculosis.

Tuberculosis (TB)-associated mortality remains disproportionately high among people living with HIV (PLWH), with macrophage dysfunction representing a key mechanism of impaired host defence against Mycobacterium tuberculosis (Mtb) infection. Using the U1 chronically HIV-infected macrophage cell line model coupled with primary human monocyte-derived macrophages (MDMs) exposed to HIV-1 gp120, we systematically characterized immunometabolic perturbations during Mtb infection. Nanostring RNA analysis revealed that Mtb monoinfection upregulated glycolytic genes while suppressing oxidative phosphorylation (OXPHOS) transcripts, consistent with a Warburg-type metabolic shift. Conversely, HIV infection downregulated glycolytic enzymes and enhanced mitochondrial respiratory chain components. Coinfection studies demonstrated HIV-mediated suppression of Mtb-induced glycolytic reprogramming. Extracellular flux analysis demonstrated that gp120 exposure increased basal oxygen consumption rate while impairing spare respiratory capacity in Mtb-infected MDMs, effectively blocking the Warburg metabolic transition. Notably, gp120 concentrations equivalent to those observed in antiretroviral therapy (ART)-treated PLWH significantly disrupted metabolic plasticity and high-dose gp120 attenuated Mtb-induced TNF- secretion. ImportanceThis study provides mechanistic insight into HIV-associated susceptibility to TB by demonstrating that HIV-1 infection fundamentally alters macrophage immunometabolic responses to Mtb. We establish that HIV-1, through gp120-mediated signaling, subverts the critical glycolytic induction required for effective antimicrobial responses against Mtb. The persistence of this metabolic dysregulation at clinically relevant gp120 concentrations, comparable to those observed in virologically suppressed PLWH, suggests ongoing immunological vulnerability despite ART. These findings identify HIV-induced metabolic reprogramming as a potential contributor to the persistently elevated TB risk in ART-treated individuals and highlight macrophage immunometabolism as a promising therapeutic target for host-directed therapies in HIV/TB coinfection. The dissociation between metabolic and cytokine responses suggests complex, multifactorial mechanisms underlying HIV-associated impairment of anti-mycobacterial immunity, warranting further investigation into the molecular pathways connecting cellular metabolism and immune effector functions.

immunology↗

bioRxiv2025

Human Stress Response Specificity through Bioresonance Selectivity

Bold First ParagraphIn eukaryotes, the mitogen activated protein kinase (MAPK) cascade, a multilayered interconnected network of enzymes, connects external stimuli to gene regulation, determining cellular fate 1. Environmental stress sensed by a cell starts a complex chain of reactions between MAPK enzymes that ultimately activates the master stress response regulator protein p38 MAPK 2,3. Thus activated, p38 must then selectively activate targets from a pool of hundreds to initiate appropriate cellular responses 3. Mechanisms for how p38 performs this selection remain unclear 4,5. Here we show that human p38 target selectivity is based on the same principles as modern electronic telecommunications systems, except using waves of chemicals rather than electromagnetic fields or electric currents. p38 encodes information about stimuli as different frequency oscillations of its activation state, and targets are selected through frequency-dependent resonance of oscillating biochemical reactions between p38 and its targets. We demonstrate this mechanism by activating various genetic responses in human cells by applying only sugar at different frequencies. These results unify observations of oscillating signaling components and altered responses 6-19 into a coherent framework to understand and control human gene expression. As failures of this mechanism may contribute to some p38-associated diseases 2,20-28, these findings may have implications for pharmaceutical development and therapeutic strategies.

biophysics↗

bioRxiv2024

Integrating Data Across Oscillatory Power Bands Predicts the Epileptogenic Zone: the Frequency Range Explorer Epileptogenic Zone (FREEZ) Identification Algorithm

Accurate identification of the seizure onset zone (SOZ) using intracranial electroencephalography (iEEG) remains challenging. Although diverse methods have leveraged spectral features to classify patient outcomes, few approaches focus on identifying individual electrodes within the SOZ or integrate a broad spectrum of frequency ranges within a single model. We developed an interpretable machine learning model that integrates power across delta, theta, alpha, beta, gamma, and high-gamma frequencies over time to identify the SOZ. For 1,511 electrodes implanted across 21 patients, we computed the mean spectral power in each frequency band for the first 20 seconds after seizure onset and analyzed the differences in power between SOZ and non-SOZ electrodes. In patients who were seizure-free after surgery (n = 14), electrodes within the SOZ showed significantly higher area under the curve (AUC) for mean power over time in the first 20 seconds after seizure onset compared to electrodes outside the SOZ in the alpha (p = 0.0272), beta (p = 0.0263), gamma (p = 0.0013), and high gamma (p = 0.0086) ranges. Additionally, electrodes within the SOZ in patients that became seizure-free after surgery had significantly higher AUC compared to equivalent electrodes in patients who did not become seizure-free after surgery (n = 7) in the gamma (p = 0.0145) and high gamma (p = 0.0024) power ranges. We trained a stacked random forest ensemble model using these features over time to label electrodes within the SOZ. Leave-one-out patient cross validation of the machine learning model yielded a 96.6% positive predictive value and 99.9% specificity for identifying electrodes within the SOZ. Our dataset included a diverse array of seizure onset patterns, which were all classified accurately by the model. A second model was trained to predict post-operative seizure freedom, yielding 95.2% accuracy for predicting seizure outcome based on a planned resection. This two-model design mirrors clinical workflow, first localizing SOZ electrodes to support surgical planning, then predicting outcome based on a surgical plan. An advantage of our interpretable machine learning approach is the ability to interrogate our models to understand how predictions are made. For electrode classification, the model weighed beta (0.66 {+/-} 0.07), high gamma (0.54 {+/-} 0.06), and delta (0.51 {+/-} 0.06) power bands most heavily. Viewing the models frequency band weights over time reveals that the model identified a pattern resembling the "fingerprint of the epileptogenic zone", reinforcing the importance of this dominant fundamental neurophysiologic signature of seizure onset.

neuroscience↗

bioRxiv2023

Endocisternal interfaces for minimally invasive neural stimulation and recording of the brain and spinal cord

Minimally invasive neural interfaces can be used to diagnose, manage, and treat many disorders with substantially reduced risks of surgical complications. Endovascular neural interfaces implanted in the veins or arteries is one approach, but it requires prescriptions of anti-thrombotic medication and are likely not explantable after endothelialization. More critically, the approach is limited by the small size and location of blood vessels, such that many important cortical, subcortical, spinal targets cannot be reached. Here, we demonstrate a chronic endocisternal neural interface that approaches brain and spinal cord targets through inner and outer cerebral spinal fluid (CSF) spaces. These spaces surround the nervous system and lack the tortuosity of the circulatory system, giving us access to the entire brain convexity, deep brain structures within the ventricles, and the spinal cord from the spinal subarachnoid space. Combined with miniature magnetoelectric-powered bioelectronics, the entire wireless system is deployable through a percutaneous procedure. The flexible catheter electrodes can be freely navigated throughout the body from the spinal to cranial subarachnoid space, and from the cranial subarachnoid space to the ventricles. We show in a large animal model that we can also reposition the recording and stimulation electrodes or explant the neural interface after chronic implantation. This enables applications in therapies that require transient or permanent brain/machine interface such as stroke rehabilitation and epilepsy monitoring and opens a new class of minimally invasive endocisternal bioelectronics.

bioengineering↗

bioRxiv2019

Disrupting female flight in the vector Aedes aegypti

Aedes aegypti is a vector of dengue, chikungunya, and Zika viruses. Current vector control strategies such as community engagement, source reduction, and insecticides have not been sufficient to prevent viral outbreaks. Thus, interest in novel strategies involving genetic engineering is growing. Female mosquitoes rely on flight to mate with males and obtain a bloodmeal from a host. We hypothesized that knockout of genes specifically expressed in female mosquitoes associated with the indirect flight muscles would result in a flightless female mosquito. With the CRISPR-Cas9 system, we performed embryonic microinjections of Cas9 protein and guide RNAs specific to genes hypothesized to control flight in mosquitoes, and have obtained genetic knockouts in several genes specifically expressed in the flight-muscle, including those specific to female flight muscle. Analysis of the phenotype of these female-specific gene knockout mutants resulted in flightless females and flying males. While further assessment is required, this work lays the groundwork for a mechanism of population control that is female-specific for the Ae. aegypti vector.

genetics↗

bioRxiv2019

A novel antibody targeting ICOS increases intratumoural cytotoxic to regulatory T cell ratio and induces tumour regression.

The immunosuppressive tumour microenvironment constitutes a significant hurdle to the response to immune checkpoint inhibitors. Both soluble factors and specialised immune cells such as regulatory T cells (TReg) are key components of active intratumoural immunosuppression. Previous studies have shown that Inducible Co-Stimulatory receptor (ICOS) is highly expressed in the tumour microenvironment, especially on TReg, suggesting that it represents a relevant target for preferential depletion of these cells. Here, we used immune profiling of samples from tumour bearing mice and cancer patients to characterise the expression of ICOS in different tissues and solid tumours. By immunizing an Icos knockout transgenic mouse line expressing antibodies with human variable domains, we selected a fully human IgG1 antibody called KY1044 that binds ICOS from different species. Using KY1044, we demonstrated that we can exploit the differential expression of ICOS on T cell subtypes to modify the tumour microenvironment and thereby improve the anti-tumour immune response. We showed that KY1044 induces sustained depletion of ICOShigh TReg cells in mouse tumours and depletion of ICOShigh T cells in the blood of non-human primates, but was also associated with secretion of pro-inflammatory cytokines from ICOSlow TEFF cells. Altogether, KY1044 improved the intratumoural TEFF:TReg ratio and increased activation of TEFF cells, resulting in monotherapy efficacy or in synergistic combinatorial efficacy when administered with the immune checkpoint blocker anti-PD-L1. In summary, our data demonstrate that targeting ICOS with KY1044 can favourably alter the intratumoural immune contexture, promoting an anti-tumour response.

cancer biology↗