Biology subjects

El Khoury, J.

Publications and source records attributed to El Khoury, J..

8 recordsLinked to original sources

bioRxiv2026

Reactions to Perceiving Tangible vs Virtual Impossible objects: Investigating Dimensions of Visual Reality

Considering the current plethora of 2D depictions via flat screens, we investigated whether our perception might have become more susceptible to this format or whether our perceptual system remained more captivated by tangible reality, i.e. 3D objects. To this end, we selected a set of impossible optical illusions that can be described as impossible objects and evaluated their impact on perceptual experience. Twenty-eight healthy volunteers were presented with 5 impossible objects in three ways: real wooden format, photographs or coloured drawings of these objects. They had to evaluate their reactions to each illusory stimulus separately in a random order. Then they were asked to rank the three presentations when presented simultaneously. We found that real objects presentation was more challenging than their 2D counterparts. Specifically real objects yield more surprise and impact on perception than their 2D image counterparts. Implications for naive realism and social cognition are proposed.

neuroscience↗

bioRxiv2026

Brain-resident myeloid cells promote rapid leukocyte adhesion in leptomeningeal vessels after anti-Aβ immunotherapy

Anti-amyloid {beta} (A{beta}) immunotherapy improves cognitive outcomes in Alzheimer's disease (AD) but is associated with amyloid-related imaging abnormalities (ARIA), through poorly understood mechanisms. To define how anti-A{beta} antibodies acutely engage brain immune and vascular compartments, we developed a longitudinal in vivo two-photon imaging platform to track microglial dynamics, peripheral immune cell recruitment, and vascular responses in APP/PS1 mice. Anti-A{beta} antibodies, including aducanumab and lecanemab, rapidly initiated microglial activation and spatial reorganization within 24 hours of dosing, with recruitment to plaque-associated regions, stabilizing plaque growth. Aducanumab and lecanemab also triggered a rapid and transient cerebrovascular immune response characterized by rolling and adhesion of peripheral immune cells along leptomeningeal vessels, accompanied by endothelial activation. Immune cell characterization revealed recruitment of innate immune cells (Iba1Ki67 monocytes and Ly6G neutrophils) and proliferative CD3 T cells into the vascular compartment following treatment. Prophylactic treatment with high-dose dexamethasone reduced the number of adherent cells without affecting microglial activation. Depletion of brain-resident immune cells similarly reduced peripheral immune cell recruitment, supporting their contribution to leukocyte recruitment. Postmortem brain tissue from an AD patient treated with lecanemab showed higher proliferation-associated monocyte signature scores, suggesting translational relevance to the proliferative myeloid response observed in our mouse models. These findings demonstrate that anti-A{beta} immunotherapy rapidly initiates a coordinated central and peripheral immune response at the leptomeningeal interface. These early immune-vascular interactions represent a plausible initiating mechanism for ARIA and provide a mechanistic framework to guide strategies for mitigating ARIA risk

neuroscience↗

bioRxiv2025

Deciphering Cellular Ecosystems Driving Tumor Progression and Immune Escape from Spatial Transcriptomics and Single-Cell with COMPOTES

Cell-cell communication is central to understanding the complex interactions within the tumor microenvironment. However, current methods fail to identify recurrent communication patterns across patient cohorts from spatial transcriptomics, as they are often limited to single samples or lack essential spatial context. Yet this is essential for understanding how local environments influence cell phenotype and states, and shape the entire cellular ecosystem. We introduce a machine-learning approach that models local, spatially aware ligand-receptor interactions and uses matrix factorization to extract global multicellular programs from large cohorts representing the complex biology of cancer. Applied to a multimodal muscle-invasive bladder cancer cohort of 146 patients, it uncovered 45 communication programs defined by distinct ligand-receptor pairs and cellular niches. In particular, we identified a conserved immune program linked to stalled anti-tumor immunity and a program linking KMT2D loss-of-function mutations with early-stage (T2) tumors, intense proliferation and a favorable response to neoadjuvant chemotherapy.

bioinformatics↗

bioRxiv2025

Multiple instance learning with spatial transcriptomics for interpretable patient-level predictions: application in glioblastoma

Accurate prediction of patient outcomes remains a major challenge in oncology. While recent machine learning (ML) approaches often rely on bulk omics lacking spatial resolution or histology-based multiple instance learning (MIL), spatial transcriptomics (SpT) provides a unique opportunity to capture both molecular content and tissue architecture. However, no generalizable ML framework has yet been established to exploit SpT for patient-level outcomes. We present SpaMIL, a flexible and interpretable MIL framework designed for SpT, with a distillation strategy that enables deployment for hematoxylin and eosin (H&E) slides alone. We evaluate the framework by predicting survival from glioblastoma (GBM) patients, a clinically compelling setting given its aggressiveness with a median survival of only 15 months and the lack of prognostic clinical variables. We analyzed 76 GBM cases from the MOSAIC dataset: 43 with matched SpT, H&E, single-nucleus RNA-seq (scRNA-seq), bulk RNA-seq, and clinical variables, and 33 with H&E for external validation. We developed two main architectures: abMIL, tailored to SpTs spatial molecular structure, and MabMIL, which distills SpT-derived representations into H&E. Model interpretability was achieved through a Shapley-based framework linking prognostic predictions to cell-type compositions via SpT deconvolution. In benchmarking across the five GBM MOSAIC modalities, SpT-based abMIL achieved unprecedented prognostic accuracy (median C-index: 0.72, standard deviation: 0.04), outperforming all other modalities, including established clinical predictors. PCA and deconvolution-based SpT representations surpassed recent foundation models, suggesting the need for further research on SpT foundation models. Our interpretability analysis highlighted malignant and non-malignant cell subpopulations associated with favorable or poor prognosis, consistent with recent reports. Finally, MabMIL maintained strong performance while enabling H&E-only deployment, with improved condorance index over H&E-only baselines in both internal (0.59 vs. 0.57) and external (0.62 vs. 0.55) cohorts.

cancer biology↗

bioRxiv2025

Filamentation-driven peripheral clustering of the inducible lysine decarboxylase is crucial for E. coli acid stress response

Bacteria have evolved numerous sophisticated acid stress response strategies to withstand fluctuating environmental pH, with enterobacterial inducible amino acid decarboxylases playing a major role. The lysine decarboxylase LdcI catalyses lysine-to-cadaverine conversion coupled to proton consumption and carbon dioxide release, thereby buffering cytoplasmic and extracellular pH. Our previous study showed that Escherichia coli LdcI forms intracellular patches under mild acid stress, and that at the corresponding acidic pH, purified LdcI polymerises into filaments, suggesting a potential role for polymerisation in its function. Here, we investigate the physiological relevance of LdcI filamentation in E. coli using 3D super-resolution fluorescence microscopy and a structure-based LdcI polymerisation-deficient E. coli mutant strain. We establish a semi-automated workflow for detection of clusters within a bacterial cell as well as quantitative analysis of their volume and localisation. We demonstrate that LdcI predominantly clusters at the cell periphery. We then show that disrupting LdcI polymerisation markedly reduces cluster size without significantly affecting localisation, suggesting that acid-stress induced LdcI clustering in vivo is driven by the enzymes filamentation. Growth and pH measurements reveal that the mutant strain exhibits reduced fitness and impaired extracellular buffering compared to the wild type, indicating that LdcI polymerisation enhances E. coli acid stress response. Our findings provide strong evidence that LdcI filamentation is a key regulatory feature of the E. coli capacity to counteract acid stress, optimising enzymatic activity by spatially organising LdcI within the cytoplasm. More broadly, this work adds to a growing body of evidence supporting the functional significance of metabolic enzyme self-assembly and its implications for bacterial stress adaptation. Author summaryAmino acid decarboxylases confer enterobacteria the ability to survive under acidic conditions. Kirchner et al. show that polymerisation of the acid-stress inducible lysine decarboxylase LdcI within E. coli cells leads to its peripheral intracellular clustering and fine-tunes its enzymatic activity in vivo.

microbiology↗

bioRxiv2024

Robust Evaluation of Deep Learning-based Representation Methods for Survival and Gene Essentiality Prediction on Bulk RNA-seq Data

Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact of design choices of DL approaches on the performance of the learned representation, including the model architecture, the training methodology and the various hyperparameters. To address this problem, we evaluate the performance of various design choices of DL representation learning methods using TCGA and DepMap pan-cancer datasets, and assess their predictive power for survival and gene essentiality predictions. We demonstrate that non DL-based baseline methods achieve comparable or superior performance compared to more complex models on survival predictions tasks. DL representation methods, however, are the most efficient to predict the gene essentiality of cell lines. We show that auto-encoders (AE) are consistently improved by techniques such as masking and multi-head training. Our results suggest that the impact of DL representations and of pre-training are highly task- and architecture-dependent, highlighting the need for adopting rigorous evaluation guidelines. These guidelines for robust evaluation are implemented in a pipeline made available to the research community.

bioinformatics↗

bioRxiv2022

The AAA+ ATPase RavA and its binding partner ViaA modulate E. coli aminoglycoside sensitivity through interaction with the inner membrane

Enteric bacteria have to adapt to environmental stresses in the human gastrointestinal tract such as acid and nutrient stress, oxygen limitation and exposure to antibiotics. Membrane lipid composition has recently emerged as a key factor for stress adaptation. The E. coli ravA-viaA operon is essential for aminoglycoside bactericidal activity under anaerobiosis but its mechanism of action is unclear. Here we characterise the VWA domain-protein ViaA and its interaction with the AAA+ ATPase RavA, and find that both proteins localise at the inner cell membrane. We demonstrate that RavA and ViaA target specific phospholipids and subsequently identify their lipid-binding sites. We further show that mutations abolishing interaction with lipids restore induced changes in cell membrane morphology and lipid composition. Finally we reveal that these mutations render E. coli gentamicin-resistant under fumarate respiration conditions. Our work thus uncovers a ravA-viaA-based pathway which is mobilised in response to antibiotics under anaerobiosis and has a major impact on cell membrane regulation.

microbiology↗

bioRxiv2022

The AAA+ ATPase RavA-ViaA complex sensitizes Escherichia coli to aminoglycosides under anaerobic low energy conservation conditions

Aminoglycosides have been used against Gram-negative bacteria for decades. Yet, uncertainties remain about various aspects of their uptake mechanism. Moreover their killing efficiency is well known to vary as a function of growth conditions and types of metabolism used by the targeted bacterium. Here we show that RavA, an AAA+ ATPase from the MoxR subfamily, associated with its VWA-containing partner, ViaA sensitize E. coli to lethal concentrations of AG, including gentamycin (Gm) and tobramycin, but not of antibiotics of other classes. We show this sensitizing effect to be due to enhanced Gm uptake in a proton motive force dependent manner. We evaluated the influence of RavA ViaA throughout a series of growth conditions, including aerobiosis and anaerobiosis. This led us to observe that the sensitizing effect of RavA ViaA varies with the respiratory chain used, i.e. RavA ViaA influence was prominent in the absence of exogenous electron acceptor or with fumarate, i.e. in poor energy conservation conditions, and dispensable in the presence of nitrate or oxygen, i.e. in high level of energy conservation. We propose RavA ViaA to be able to sense energetic state of the cell and to be used under low energy conditions for facilitating uptake of chemicals across the membrane, including Gm. Author SummaryAntibiotic resistance is a major public health, social and economic problem. Aminoglycosides are known for their high efficiency against Gram-negative bacteria but their use is restricted to life threatening infections because of their nephrotoxicity and ototoxicity at therapeutic dose. Elucidation of AG sensitization mechanisms in bacteria will allow the use of a decreased effective dose of AGs. Here we identified new molecular actors, RavA and ViaA, which sensitize E. coli to AG under anaerobiosis. RavA belongs to the AAA+ ATPase family while ViaA bears a VWA motif. Moreover we show here that the influence of RavA ViaA on AG sensitivity varies with growth conditions and respiratory metabolism used by E. coli. This is a significant step forward as anaerobiosis is well known to reduce antibacterial activity of AG. This study emphasizes the crucial importance of the relationships between culture conditions, metabolism and antibiotic resistance.

microbiology↗