Biology subjects

Fernandez-Villegas, A.

Publications and source records attributed to Fernandez-Villegas, A..

10 recordsLinked to original sources

bioRxiv2026

α-Synuclein Facilitates Spontaneous Dopamine Release in a Calcium- and Phosphorylation-Dependent Manner

-Synuclein (aSyn) is central to Parkinsons disease pathogenesis, yet its native physiological role at the presynapse remain poorly defined. Here, super-resolution imaging in dopaminergic neurons reveals that endogenous aSyn localises within nanometres of L-type voltage-gated calcium channels (LTCC), with closer proximity under both spontaneous neuronal activity and stimulated conditions compared to when extracellular calcium is chelated. Blocking Ca2+/calmodulin-dependent kinase II (CaMKII) reduces aSyn clustering at LTCC under spontaneous activity, suggesting that calcium entry and downstream calcium-dependent kinase activity contribute to aSyn localisation. Moreover, quantitative single-molecule analyses indicate that calcium increases the abundance of both total and serine129 phosphorylated (pS129) aSyn in synaptosomes under spontaneous conditions, and NMR analysis reveals that both calcium and S129 phosphorylation increase the binding affinity of aSyn to synaptic vesicles. Functional assays further demonstrate that LTCC blockade elevates intracellular DA levels exclusively in the presence of aSyn under spontaneous but not stimulated conditions. Finally, biochemical fractionation and multi-colour single-molecule imaging reveal that aSyn preferentially associates with small vesicles that are not obligately coupled to full-fusion associated recycling pools. These results suggest that aSyn acts as a calcium- and phosphorylation-regulated modulator of spontaneous DA release through pathways that are largely independent of full-fusion recycling mechanisms, and that pS129 aSyn is not solely a pathological marker but may also reflects physiological regulation. Together, these insights provide a framework for understanding how therapeutic strategies targeting aSyn may impact its normal synaptic functions.

neuroscience↗

bioRxiv2026

Discovery of TDP-43 aggregation inhibitors via a hybrid machine learning framework

TAR DNA-binding protein 43 (TDP-43) aggregation is a hallmark of several neurodegenerative diseases, including amyotrophic lateral sclerosis and frontotemporal dementia. Recent therapeutic efforts have highlighted the potential of small molecules capable of inhibiting TDP-43 aggregation; however, no effective treatments currently exist. Here, we developed a hybrid machine learning approach combining graph neural network (GNN) embeddings with traditional chemical descriptors and biological target annotations. Using XGBoost as the final classifier enabled model interpretability through SHAP analysis, allowing the identification of key chemical features and target annotations associated with TDP-43 anti-aggregation activity. Complementary Monte Carlo Tree Search analysis highlighted specific chemical substructures linked to predicted activity. By screening an external library of 3,853 small molecules, the model identified two compounds not previously evaluated against TDP-43 aggregation, namely berberrubine and PE859. Molecular docking analysis revealed that both compounds interact favourably with the TDP-43 RNA recognition motif (RRM) domain through distinct binding modes. Experimental validation showed that both compounds significantly reduced TDP-43 aggregation in HEK cells. Further testing in Caenorhabditis elegans expressing human TDP-43 demonstrated that PE859 significantly rescued locomotor defects, while berberrubine showed partial improvement. This work establishes a hybrid machine learning approach for accelerating small molecule drug discovery, yielding two promising therapeutic candidates for TDP-43 proteinopathies.

bioinformatics↗

bioRxiv2025

Brain cholesterol metabolites cause significant neurodegeneration in human iPSC-derived neurons

Disrupted cholesterol metabolism is increasingly recognised as a contributing factor in neurodegeneration; however, the specific effects of key brain-derived cholesterol metabolites, 24S-hydroxycholesterol (24S-HC) and 27-hydroxycholesterol (27-HC), remain poorly understood. Using human iPSC-derived i3 cortical neurons, we demonstrate that both 24S-HC and 27-HC significantly impair neuronal calcium signalling by elevating resting calcium levels, reducing spike amplitude, and disrupting network synchrony. These functional deficits are accompanied by widespread organelle dysfunction. Both oxysterols induce mitochondrial fragmentation, decrease spare respiratory capacity, and impair lysosomal degradation. Notably, 27-HC uniquely triggers lysosomal swelling and membrane permeabilisation. Additional signs of cellular stress, including axonal swellings and elevated endoplasmic reticulum calcium levels, were also observed. Furthermore, both 24S-HC and 27-HC were found to directly interact with alpha-synuclein (aSyn), promoting its accumulation in cellular models. In contrast, cholesterol itself had minimal impact, highlighting the distinct toxicity of its hydroxylated metabolites. Together, these findings reveal a mechanistic link between oxysterol accumulation and neuronal dysfunction, supporting the hypothesis that elevated levels of 24S-HC and 27-HC, commonly observed in Parkinsons and Alzheimers disease, may actively drive neurodegenerative processes. Targeting oxysterol metabolism may therefore represent a promising therapeutic avenue for intervention in neurodegenerative disorders.

neuroscience↗

bioRxiv2025

Deep learning for fluorescence lifetime predictions enables high-throughput in vivo imaging

Fluorescence lifetime imaging microscopy (FLIM) is a powerful optical tool widely used in biomedical research to study changes in a samples microenvironment. However, data collection and interpretation are often challenging, and traditional methods such as exponential fitting and phasor plot analysis require a high number of photons per pixel for reliably measuring the fluorescence lifetime of a fluorophore. To satisfy this requirement, prolonged data acquisition times are needed, which makes FLIM a low-throughput technique with limited capability for in vivo applications. Here, we introduce FLIMngo, a deep learning model capable of quantifying FLIM data obtained from photon-starved environments. FLIMngo outperforms other deep learning approaches and phasor plot analyses, yielding accurate fluorescence lifetime predictions from decay curves obtained with fewer than 50 photons per pixel by leveraging both time and spatial information present in raw FLIM data. Thus, FLIMngo reduces FLIM data acquisition times to a few seconds, thereby, lowering phototoxicity related to prolonged light exposure and turning FLIM into a higher throughput tool suitable for analysis of live specimens. Following the characterisation and benchmarking of FLIMngo on simulated data, we highlight its capabilities through applications in live, dynamic samples. Examples include the quantification of disease-related protein aggregates in non-anaesthetised Caenorhabditis (C.) elegans, which significantly improves the applicability of FLIM by opening avenues to continuously assess C. elegans throughout their lifespan. Finally, FLIMngo is open-sourced and can be easily implemented across systems without the need for model retraining.

biophysics↗

bioRxiv2024

Local ionic conditions modulate the aggregation propensity and influence the structural polymorphism of alpha-synuclein

Parkinsons Disease (PD) is characterized by the aggregation of alpha-synuclein (aSyn), a presynaptic protein that transitions from a disordered monomer into beta-sheet rich amyloid fibrils. The precise triggers and mechanisms underlying aSyn misfolding and aggregation remain unclear, hindering the development of effective therapeutics. Monomeric aSyn is an intrinsically disordered protein (IDP) with high conformational flexibility. Local environmental factors, such as ion concentrations, can influence the conformational ensemble of aSyn, impacting its aggregation propensity and resulting in fibril polymorphism. In this study, we explore the impact of physiologically relevant ions, mainly Ca2+ and Na+, on the aggregation kinetics, monomer structural dynamics, and fibril polymorphism of aSyn. Using ThT fluorescence assays, we demonstrate that all ions accelerate aSyn aggregation, with Ca2+ having the most significant effect. Using Heteronuclear Single Quantum Correlation Nuclear Magnetic Resonance (1H-15NHSQC NMR) spectroscopy, we validate the specific binding of Ca2+ ions at the C-terminus, whereas Na+ ions display non-specific interactions along the sequence of aSyn. Small-angle neutron scattering (SANS) and hydrogen-deuterium exchange mass spectrometry (HDX-MS) further reveal that Na+ and Ca2+ induce distinct conformational changes in the aSyn monomer, with Na+ leading to more extended structures and Ca2+ promoting a moderate extension of the protein. Molecular dynamics simulations (MD) corroborate these findings, showing that Na+ ions increase the proteins extension, particularly between the non-amyloid beta component (NAC) region and the C-terminus, whereas Ca2+ ions bias the ensemble towards a more moderately elongated structure. Using MD, we further investigate the local environment and in particular the solvent effect and show the water persistence times in the hydration shell are also increased in the presence of Ca2+ ions, indicating that the aggregation propensity of the monomer is due to a combination of conformational bias of the monomer and solvent mobility. Atomic force microscopy (AFM) of aSyn fibrils formed under these different ionic conditions reveal distinct fibril polymorphs, suggesting that ion-induced conformational biases in the monomer contribute to the diversity of fibril structures. Collectively, these findings underscore the pivotal influence of the local ionic milieu in shaping the structure and aggregation propensity of aSyn, thus offering valuable insights into the molecular underpinnings of PD and potential therapeutic avenues aimed at manipulating aSyn conformational dynamics.

biophysics↗

bioRxiv2024

Machine learning-based spike sorting reveals how subneuronal concentrations of monomeric Tau cause a loss in excitatory postsynaptic currents in hippocampal neurons

Extracellular recordings of neuronal activity constitute a powerful tool for investigating the intricate dynamics of neural networks and the activity of individual neurons. Microelectrode arrays (MEAs) allow for recordings with a high electrode count, ranging from 10s to 1000s, generating extensive datasets of neuronal information. Furthermore, MEAs capture extracellular field potentials from cultured cells, resulting in highly complex neuronal signals that necessitate precise spike sorting for meaningful data extraction. Nevertheless, conventional spike sorting methods face limitations in recognising diverse spike shapes, thereby constraining the full utilisation of the rich dataset acquired from MEA recordings. To overcome these limitations, we have developed a machine learning algorithm, named PseudoSort, which employs advanced self-supervised learning techniques, a distinctive density-based pseudo-labelling strategy, and an iterative fine-tuning process to enhance spike sorting accuracy. Through extensive benchmarking on large-scale simulated datasets, we demonstrate the superior performance of PseudoSort compared to recently developed machine learning-based (ML) spike sorting algorithms. We showcase the practical application of PseudoSort by utilising MEA recordings from hippocampal neurons exposed to subneuronal concentrations of monomeric Tau, a protein associated with Alzheimers disease (AD). Our results, validated against patch clamp experiments, unveil that monomeric Tau at subneuronal concentrations induces stimulation-dependent disruptions in both local and global activity of hippocampal neurons. Remarkably, patch clamp electrophysiology highlights the effect of combined Tau and neuronal stimulation treatment on excitatory postsynaptic currents, whereas PseudoSort excels in identifying neuronal clusters that exhibit diminished firing capacity following Tau treatment alone, i.e., in the absence of stimulation. This comprehensive approach validates the prowess of PseudoSort and unravels the intricate effects of Tau on neuronal activity, particularly in the context of AD.

neuroscience↗

bioRxiv2024

Graphene microelectrode arrays, 4D structured illumination microscopy, and a machine learning-based spike sorting algorithm permit the analysis of ultrastructural neuronal changes during neuronal signalling in a model of Niemann-Pick disease type C

Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use graphene microelectrode arrays (G-MEAs) to address these challenges, as they are compatible with high spatial resolution imaging across various scales as well as high temporal resolution electrophysiological recordings. Furthermore, alongside G-MEAs, we deploy an easy-to-implement machine learning-based algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML)-based spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables the monitoring of the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC) and show that synaptic boutons, compared to untreated controls, significantly increase in size, which leads to a loss in neuronal signalling capacity.

neuroscience↗

bioRxiv2022

α-synuclein fibril and synaptic vesicle interactions lead to vesicle destruction and increased uptake into neurons

Monomeric alpha-synuclein (aSyn) is a well characterised as a lipid binding protein. aSyn is known to form amyloid fibrils which are also localised with lipids and organelles in so called Lewy bodies, insoluble structures found in Parkinsons disease patients brains. It is still unclear under which conditions the aSyn-lipid interaction can start to become pathological. Previous work to address pathological interactions has focused on using synthetic lipid membranes, which lack the complexity of physiological lipid membranes which not only have a more complex lipid composition, but also contain lipid interacting proteins. Here, we investigate how either monomeric or fibrillar aSyn interact with physiological synaptic vesicles (SV) isolated from rodent brain. Using small angle neutron scattering and high-resolution imaging we observe that aSyn fibrils disintegrate SV, whereas aSyn monomers cause clustering of SV. Furthermore, SV enhance the aggregation rate of aSyn, however increasing the SV:aSyn ratio causes a reduction in aggregation propensity. SV lipids appear as an integrated part of aSyn fibrils and while the fibril morphology differs to aSyn fibrils alone, the core fibril structure remains the same. We finally demonstrate that lipid-associated aSyn fibrils are more easily taken up into cortical i3Neurons derived from induced pluripotent stem cells. Our study sheds light on differences between interactions of aSyn with synthetic lipid vesicles and physiological SV. We show how aSyn fibrils may enhance pathology by disintegrating SV, which in turn may have fatal consequences for neurons. Furthermore, disease burden may additionally be impacted by an increased uptake of lipid-associated aSyn by neurons, leading to more SV damage and enhancing aSyn aggregation.

neuroscience↗

bioRxiv2021

The SARS-CoV-2 nucleocapsid protein associates with the replication organelles before viral assembly at the Golgi/ERGIC and lysosome-mediated egress

Despite being the target of extensive research efforts due to the COVID-19 pandemic, relatively little is known about the dynamics of SARS-CoV-2 replication within cells. We investigate and characterise the tightly orchestrated sequence of events during different stages of the infection cycle by visualising the spatiotemporal dynamics of the four structural proteins of SARS-CoV-2 at high resolution. The nucleoprotein is expressed first and accumulates around folded ER membranes in convoluted layers that connect to viral RNA replication foci. We find that of the three transmembrane proteins, the membrane protein appears at the Golgi apparatus/ERGIC before the spike and envelope proteins. Relocation of the lysosome marker LAMP1 towards the assembly compartment and its detection in transport vesicles of viral proteins confirm an important role of lysosomes in SARS-CoV-2 egress. These data provide new insights into the spatiotemporal regulation of SARS-CoV-2 assembly, and refine current understanding of SARS-CoV-2 replication.

microbiology↗

bioRxiv2019

Intra-mitochondrial protein degradationalleviates alpha-synuclein seeding and Abeta 42 aggregation

Mitochondria have long been implicated in Parkinsons disease (PD), however, it is not clear how mitochondrial impairment and alpha-synuclein pathology are coupled. We report here that intra-mitochondrial protein homeostasis plays a major role in alpha-synuclein fibril elongation, as interference with intra-mitochondrial proteases and mitochondrial protein import significantly aggravate alpha-synuclein aggregation. In contrast, direct inhibition of mitochondrial complex I, increase in intracellular calcium concentration or formation of reactive oxygen species (ROS), all of which have been associated with mitochondrial stress, did not affect alpha-synuclein pathology. We further demonstrate that similar mechanisms are involved in Amyloid {beta} 1-42 (A{beta}42) aggregation, suggesting that mitochondria are directly capable of influencing cytosolic protein homeostasis of aggregation-prone proteins.

neuroscience↗