Biology subjects

Connolly, M.

Publications and source records attributed to Connolly, M..

7 recordsLinked to original sources

bioRxiv2025

Impaired Macrophage DNase Secretion Promotes Neutrophil Extracellular Trap Mediated Defective Efferocytosis in Atherosclerosis

Neutrophil extracellular traps (NETs) drive atherosclerosis progression and are associated with adverse clinical outcomes like myocardial infarction and stroke. While the triggers of NETosis in atherosclerotic plaque are well-characterized, the mechanisms underlying NET degradation and clearance remain unclear. Moreover, the impact of impaired NET clearance on atherosclerosis progression have not been elucidated. Here we show that macrophages are critical for the release of DNases, which degrade NETs. We identified endoplasmic reticulum (ER) stress mediated activation of PERK-ATF4 pathway as a key driver of impaired macrophage DNase secretion, leading to delayed NET clearance and their persistence. Elevated NET levels trigger cleavage of the efferocytosis receptor Mertk leading to defective macrophage efferocytosis and exacerbation of plaque necrosis. Human atherosclerotic plaques and Ldlr-/- mice treated with ISRIB, a PERK inhibitor, show enhanced DNase secretion and clearance of NETs. Together, the identification of key mechanisms of NET clearance in atherosclerosis offers new therapeutic strategies to stabilize plaques.

pathology↗

bioRxiv2024

SAFE-OPT: A Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints

To treat neurological and psychiatric diseases with deep brain stimulation, a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects. In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searching in silico. We then deploy both SAFE-OPT and conventional Bayesian optimization in new subjects in vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subjects safety threshold. The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of deep brain stimulation.

bioengineering↗

bioRxiv2023

Integrating Patient Metadata and Genetic Pathogen Data: Advancing Pandemic Preparedness with a Multi-Parametric Simulator

Training and practice are needed to handle an unusual crisis quickly, safely, and effectively. Functional and table-top exercises simulate anticipated CBRNe (Chemical, Biological, Radiological, Nuclear, and Explosive) and public health crises with complex scenarios based on realistic epidemiological, clinical, and biological data from affected populations. For this reason, the use of anonymized databases, such as those from ECDC or NCBI, are necessary to run meaningful exercises. Creating a training scenario requires connecting different datasets that characterise the population groups exposed to the simulated event. This involves interconnecting laboratory, epidemiological, and clinical data, alongside demographic information. The sharing and connection of data among EU member states currently face shortcomings and insufficiencies due to a variety of factors including variations in data collection methods, standardisation practices, legal frameworks, privacy, and security regulations, as well as resource and infrastructure disparities. During the H2020 project PANDEM-2 (Pandemic Preparedness and Response), we developed a multi-parametric training tool to artificially link together laboratory data and metadata. We used SARS-CoV-2 and ECDC and NCBI open-access databases to enhance pandemic preparedness. We developed a comprehensive training procedure that encompasses guidelines, scenarios, and answers, all designed to assist users in effectively utilising the simulator. Our tool empowers training managers and trainees to enhance existing datasets by generating additional variables through data-driven or random simulations. Furthermore, it facilitates the augmentation of a specific variables proportion within a given set, allowing for the customization of scenarios to achieve desired outcomes. Our multi-parameter simulation tool is contained in the R package Pandem2simulator, available at https://github.com/maous1/Pandem2simulator. A Shiny application, developed to make the tool easy to use, is available at https://uclouvain-ctma.Shinyapps.io/Multi-parametricSimulator/. The tool runs in seconds despite using large data sets. In conclusion, this multi-parametric training tool can simulate any crisis scenario, improving pandemic and CBRN preparedness and response. The simulator serves as a platform to develop methodology and graphical representations of future database-connected applications.

bioengineering↗

bioRxiv2023

The Entrainment of the Substantia Nigra During Temporal Lobe Seizures is Dependent on the Seizure Onset Pattern

Temporal lobe (TL) epilepsy is the most common form of drug-resistant epilepsy. A major focus of human and animal studies on TLE network has been the limbic circuit and the structures composing the temporal lobe. However, there is also evidence suggesting an active role of the basal ganglia in the propagation and control of temporal lobe seizures. Evidence suggests that the network involved in temporal lobe seizure may depend on their onset and offset pattern but studies on the relationship between the patterns and extralimbic activity are limited. Here, we characterize the involvement of the substantia nigra (SN) and somatosensory cortex (SI) during temporal lobe seizures induced in two nonhuman primates (NHP). The seizure onset and offset patterns were manually classified and spectral power and coherence were calculated. We then analyzed the three first and last seconds of the seizure as well as 3-second segments of recorded in pre-ictal and post-ictal periods and compared the changes based on the seizure onset and offset patterns. Our results demonstrated an involvement of the SN and SI dependent on the seizure onset and offset pattern. We found that seizures with both low amplitude fast activity (LAF) and high amplitude slow activity (HAS) onset patterns were associated with an increase in activity of the SN while the change in activity was limited to LAF seizures in the SI. However, the increase of HPC/SI coherence was similar for both type of onset, while the increase in HPC/SN coherence was specific to the farther-spreading LAF onset pattern. As for the role of the SN in seizure cessation, we observed that the coherence between the HPC/SN was reduced during burst suppression (BS) compared to other termination phases. Additionally, we found that this coherence returned to normal levels after the seizure ended, with no significant difference in post-ictal periods among the three types of seizure offsets. This result suggests that the SN might be involved differently in the termination of the BS seizure pattern. This study constitutes the first demonstration of temporal lobe seizures entraining the SN in the primate brain. Moreover, these findings provide evidence that this entrainment is dependent on the seizure onset pattern and support the hypothesis that the SN might play a role in the maintenance and termination of some specific temporal lobe seizure.

neuroscience↗

bioRxiv2021

Prediction of nanographene binding-scores to trout cellular receptors and cytochromes

To address the increasing concerns surrounding possible impacts of graphene-related materials on the aquatic environment, this study focused on computational predictions of binding between models of graphenes in the nm size range (nanographenes, nGs) and the aryl-hydrocarbon receptor (tAHR) and P450 cytochromes (tCYPs) of rainbow trout (Oncorhynchus mykiss). The tAHR plays a key role in the induction of detoxifying and early immune responses and tCYPs are essential for detoxifying planar hydrophobic chemicals such as nGs. After 3D modelling of those trout proteins, docking algorithms predicted the size-dependance profiles of nGs binding-scores to tAHR and tCYPs in the low nM range (high binding-affinities). Virtual oxidations of nGs to nGOs (carboxy-, epoxy-and/or hydroxy-oxidations) further lowered the corresponding binding-scores in level/type-oxidation manners. Among all the tCYPs, the tCYP3AR (the equivalent to human CYP3A4) was identified as a potential key interaction enzyme for nGs because of its lower binding-scores. These results implicate a possible processing pathway to be further probed through in vitro and in vivo experimentation. Together the information generated can be pivotal for the design of safer graphene-related materials for a variety of applications and help to understand their detoxification in aquatic vertebrates.

pharmacology and toxicology↗

bioRxiv2018

Static kinks or flexible hinges: conformational distributions of bent DNA bound to integration host factor mapped by fluorescence lifetime measurements

Gene regulation depends on proteins that bind to specific DNA sites. Such specific recognition often involves severe DNA deformations including sharp kinks. It has been unclear how rigid or flexible these protein-induced kinks are. Here, we investigated the dynamic nature of DNA in complex with integration host factor (IHF), a nucleoid-associated architectural protein known to bend one of its cognate sites (35 base pair H) into a U-turn by kinking DNA at two sites. We utilized fluorescence lifetime based FRET spectroscopy to map the distribution of bent conformations in various IHF-DNA complexes. Our results reveal a surprisingly dynamic specific complex: while 80% of the IHF-H population exhibited FRET efficiency consistent with the crystal structure, 20% exhibited FRET efficiency indicative of unbent or partially bent DNA. This conformational flexibility is modulated by sequence variations in the cognate site. In another site (H1) that lacks an A-tract of H on one side of the binding site, the population in the fully U-bent conformation decreased to 36%, as did the extent of bending. A similar decrease in the U-bent population was observed with a single base mutation in H in a consensus region on the other side. Taken together, these results provide important insights into the finely tuned interactions between IHF and its cognate sites that keep the DNA bent (or not), and yield quantitative data on the dynamic equilibrium between different DNA conformations (kinked or not kinked) that depend sensitively on DNA sequence and deformability. Notably, the difference in dynamics between IHF-H and IHF-H1 reflects the different roles of these complexes in their natural context, in the phage lambda \"intasome\" (the complex that integrates phage lambda into the E. coli chromosome).

biophysics↗

bioRxiv2017

Dynamical origins of heat capacity changes in enzyme catalysed reactions

Heat capacity changes are emerging as essential for explaining the temperature dependence of enzyme-catalysed reaction rates. This has important implications for enzyme kinetics, thermoadaptation and evolution, but the physical basis of these heat capacity changes is unknown. Here we show by a combination of experiment and simulation, for two quite distinct enzymes (dimeric ketosteroid isomerase and monomeric alpha-glucosidase), that the activation heat capacity change for the catalysed reaction can be predicted through atomistic molecular dynamics simulations. The simulations reveal subtle and surprising underlying dynamical changes: tightening of loops around the active site is observed as expected, but crucially, changes in energetic fluctuations are evident across the whole enzyme including important contributions from oligomeric neighbours and domains distal to the active site. This has general implications for understanding enzyme catalysis and demonstrating a direct connection between functionally important microscopic dynamics and macroscopically measurable quantities.

biochemistry↗