Biology subjects

Brea, J.

Publications and source records attributed to Brea, J..

8 recordsLinked to original sources

bioRxiv2026

A brain-penetrant P2X7R antagonist mitigates Alzheimer's disease pathology

The ATP-gated P2X7 receptor (P2X7R) activates inflammatory signaling pathways in the central nervous system. In particular, P2X7Rs drive chronic glia-mediated neuroinflammation, which is increasingly recognized as a key contributor to Alzheimers disease, a neurodegenerative disorder that lacks effective disease-modifying therapies. Here we identify a potent and selective negative allosteric modulator of P2X7Rs with therapeutic potential. We synthesize a series of small molecules based on a polycyclic scaffold and confirm blood-brain barrier penetration by testing a radiolabeled analogue using positron emission tomography imaging. Through a structure-guided medicinal chemistry campaign centered on our scaffold, we identify four promising P2X7R antagonists. Of these, UB-ALT-P2 exhibits the most favorable safety profile, high oral bioavailability and robust brain penetration. High-resolution cryo-EM structures of UB-ALT-P2 bound to human, mouse, and rat P2X7Rs reveal a conserved antagonist binding mode with steric features that favor prolonged binding to human receptors. In the 5xFAD mouse model of AD, oral UB-ALT-P2 blunts weight loss, improves short- and long-term memory, reduces amyloid-{beta} plaque burden, lowers hyperphosphorylated tau, and diminishes oxidative and inflammatory markers. These results establish UB-ALT-P2 as a potent and safe P2X7R antagonist that can mitigate core AD pathologies, providing a compelling foundation for further development.

neuroscience↗

bioRxiv2024

Systematic standardization of organ-on-a-chip under controlled flow conditions: use case with Caki-1 and A549 cell lines

New in vitro models are an urgent need for to improve both the research data and the preclinical development of new drugs. Current standardized cellular models are mainly based in 2D cell culture, which lacks flow conditions and with complex co-culture settings. In this way, advanced cell culture models, such as organ-on-a-chip (OoC), aim to solve these limitations. OoC systems are composed by a microfluidic chip functionalized with different combinations of extracellular matrixes, coatings and cell cultures to mimic the physiological conditions of human organs. Advantages of OoC include the possibility to add 3D structures, delimited regions for co-culture and dynamic flow conditions to cell cultures. However, to perform reproducible and controlled experiments with OoC, it is necessary to systematically standardize the cell culture conditions in the microfluidic channels. For this is necessary to test both the combination of flow and extracellular matrix (ECM) coating to reliably mimic the human organ physiology. In this work, we standardized both conditions, ECM coating and the flow conditions to functionalize OoC with cell lines from kidney (Caki-1) and from lung (A549) to develop OoC systems beyond the Vessel-on-a-chip setting. In this way, the protocol detailed in this work will allow to standardize cell culture on different optimized OoC types with different cell types from different origins.

cell biology↗

bioRxiv2024

Behavioral individuality is a consequence of experience, genetics and learning

Learning and memory are essential components of our individuality. While it is established that behaviour can vary across genetically identical individuals, it remains unknown how much of this variation stems from momentary experience during learning compared to genetics and its past interactions with the environment. To address this, we measured behaviour in thousands of flies from 90 genetic backgrounds while they performed tasks in conditions that either did or did not require learning. Flies that were genetically identical, raised under the same conditions and tested simultaneously in the same environment persistently modified the extent of expressed individuality when they could learn. This learning-induced residual expression of individuality and its dynamics were subdued or absent in innate, learning-independent behaviours. We could quantify and then recreate this phenomenon in computer simulations. The emergence of in silico behavioural individuality was most consistent with the individuality of real flies once we enabled reinforced learning in simulated agents. Moreover, we showed that minor differences in initial conditions of the experiment can exacerbate the expression of individuality within a genotype in a learning-dependent manner. Our results establish that besides the classical G x E interactions shared between individuals in the past, learning from individual momentary experience further extends the expression of individuality.

animal behavior and cognition↗

bioRxiv2024

Morphological Profiling Dataset of EU-OPENSCREEN Bioactive Compounds Over Multiple Imaging Sites and Cell Lines

Morphological profiling with the Cell Painting assay has emerged as a promising method in drug discovery research. The assay captures morphological changes across various cellular compartments enabling the rapid identification of the effect of compounds. We present a comprehensive morphological profiling dataset using the carefully curated and well-annotated EU-OPENSCREEN Bioactive Compound Set. Our profiling dataset was generated across multiple imaging sites with high-throughput confocal microscopes using the Hep G2 as well as the U2 OS cell line. We employed an extensive assay optimization process to achieve high data quality across the different imaging sites. An analysis of the four replicates validates the robustness of the generated data. We compare morphological features of the different cell lines and map the profiles to activity, toxicity, and basic compound targets to further describe the dataset as well as to demonstrate the potential of this dataset to be used for mechanism of action exploration.

cell biology↗

bioRxiv2024

Two-factor synaptic consolidation reconciles robust memory with pruning and homeostatic scaling

Memory consolidation involves a process of engram reorganization and stabilization that is thought to occur primarily during sleep through a combination of neural replay, homeostatic plasticity, synaptic maturation, and pruning. From a computational perspective, however, this process remains puzzling, as it is unclear how the underlying mechanisms can be incorporated into a common mathematical model of learning and memory. Here, we propose a solution by deriving a consolidation model that uses replay and two-factor synapses to store memories in recurrent neural networks with sparse connectivity and maximal noise robustness. The model offers a unified account of experimental observations of consolidation, such as multiplicative homeostatic scaling, task-driven synaptic pruning, increased neural stimulus selectivity, and preferential strengthening of weak memories. The model further predicts that intrinsic synaptic noise scales sublinearly with synaptic strength; this is supported by a meta-analysis of published synaptic imaging datasets.

neuroscience↗

bioRxiv2022

Remembering the "When": Hebbian Memory Models for the Time of Past Events

Humans and animals can remember how long ago specific events happened. Little is known about the neural mechanisms that enable remembering the "when" of memories stored for long durations in the episodic memory system - in contrast to interval-timing on the order of seconds and minutes. Based on a systematic exploration of neural coding, association and retrieval schemes, we develop model classes that span the space of possible mechanisms for the reconstruction of the time of past events. In concrete examples we show how network architecture, Hebbian plasticity, synaptic pruning or systems consolidation allow the retrieval of the time of past events. In a simulation, we demonstrate how these mechanisms would enable food caching animals like corvids to remember what they cached, where, and how long ago. To dissociate different hypotheses, we propose three kinds of novel, non-verbal experiments, that can be run with humans and animals. Our simulations predict the experimental results for different classes of models. Our study shows that remembering the "when" can be implemented by many biologically plausible mechanisms and that carefully designed experiments are needed to pin down the actual neural implementation of the memory for the time of past events in different species. Author summaryWas it yesterday, a week ago, or perhaps even longer since I last ate pizza? Questions like these are typically easy to answer, because humans have a good sense of how long ago specific events happened. Research involving animals, such as food-caching birds, has revealed that they, too, can remember the "when" of past events. So far, little is known about the neuronal mechanisms that enable remembering the "when". In this study, we explore systematically concrete hypotheses of how neural circuits, synaptic plasticity and ongoing restructuring of neural circuits allow remembering the "when". We propose experiments, that could be done with humans or animals, to discriminate different biologically plausible mechanisms for remembering the "when".

neuroscience↗

bioRxiv2021

Surprise: a unified theory and experimental predictions

Surprising events trigger measurable brain activity and influence human behavior by affecting learning, memory, and decision-making. Currently there is, however, no consensus on the definition of surprise. Here we identify 16 mathematical definitions of surprise in a unifying framework, show how these definitions relate to each other, and prove under what conditions they are indistinguishable. We classify these surprise measures into four main categories: (i) change-point detection surprise, (ii) information gain surprise, (iii) prediction surprise, and (iv) confidence-correction surprise. We design experimental paradigms where different categories make different predictions: we show that surprise-modulation of the speed of learning leads to sensible adaptive behavior only for change-point detection surprise whereas surprise-seeking leads to sensible exploration strategies only for information gain surprise. However, since neither change-point detection surprise nor information gain surprise perfectly reflect the definition of surprise in natural language, a combination of prediction surprise and confidence-correction surprise is needed to capture intuitive aspects of surprise perception. We formalize this combination in a new definition of surprise with testable experimental predictions. We conclude that there cannot be a single surprise measure with all functions and properties previously attributed to surprise. Consequently, we postulate that multiple neural mechanisms exist to detect and signal different aspects of surprise. Author noteAM is grateful to Vasiliki Liakoni, Martin Barry, and Valentin Schmutz for many useful discussions in the course of the last few years, and to Andrew Barto for insightful discussions through and after EPFL Neuro Symposium 2021 on "Surprise, Curiosity and Reward: from Neuroscience to AI". We thank K. Robbins and collaborators for their publicly available experimental data (Robbins et al., 2018). All code needed to reproduce the results reported here will be made publicly available after publication acceptance. This research was supported by Swiss National Science Foundation (no. 200020_184615). Correspondence concerning this article should be addressed to Alireza Modirshanechi, School of Computer and Communication Sciences and School of Life Sciences, EPFL, Lausanne, Switzerland. E-mail: alireza.modirshanechi@epfl.ch.

neuroscience↗

bioRxiv2020

Testing two competing hypotheses for Eurasian jays' caching for the future: planning versus compensatory caching

Previous research reported that corvids preferentially cache food in a location where no food will be available or cache more of a specific food in a location where this food will not be available. Here, we consider possible explanations for these prospective caching behaviours and directly compare two competing hypotheses. The Compensatory Caching Hypothesis suggests that birds learn to cache more of a particular food in places where that food was less frequently available in the past. In contrast, the Future Planning Hypothesis suggests that birds recall what-when-where features of specific past events to predict the future availability of food. We designed a protocol in which the two hypotheses predict different caching patterns across different caching locations such that the two explanations can be disambiguated. We formalised the hypotheses in a Bayesian model comparison and tested this protocol in two experiments with one of the previously tested species, namely Eurasian jays. Consistently across the two experiments, the observed caching pattern did not support either hypothesis; rather it was best explained by a uniform distribution of caches over the different caching locations. Future research is needed to gain more insight into the cognitive mechanism underpinning corvids caching for the future.

animal behavior and cognition↗