Biology subjects

Soler, A.

Publications and source records attributed to Soler, A..

5 recordsLinked to original sources

bioRxiv2025

Morphological cell profiling for drug repurposing against SARS-CoV-2 infection

Antiviral drug discovery has traditionally focused on targeting viral proteins, while host-directed strategies remain largely underexplored. Here, present a systematic drug repurposing strategy leveraging morphological profiling to identify host-targeting antivirals. Our image-based approach combines viral protein immunostaining with high-content Cell Painting analysis to simultaneously assess viral replication and provide in-depth analysis of host cell responses. By screening 5,275 repurposable drugs against SARS-CoV-2 infected cells, we identified compounds that reversed the infected cell phenotype, including ones not detected by conventional cytopathicity and antibody-based assays. A counter-screen excluded compounds whose antiviral activity was likely driven by drug-induced phospholipidosis (DIPL). Pathway enrichment analysis of compounds validated by both Cell Painting dose-response and DIPL assays, revealed host processes frequently hijacked by viruses, including innate immune responses and kinases. Among the top hits, both novel candidates, such as serdemetan, and previously reported broad-spectrum antivirals, such as sunitinib, were identified. Our approach constitutes an adaptable and scalable platform suited for diverse viral pathogens and cell systems. We provide a resource of open access screening data, images, and automated analysis pipelines to advance both antiviral discovery and pandemic preparedness.

microbiology↗

bioRxiv2025

Targeting Pseudomonas aeruginosa Ventilator-associated pneumonia with a non-antibiotic and biolfilm-disrupting Live Biotherapeutic: Preclinical Safety and Efficacy study

Background.Ventilator-associated pneumonia (VAP) is the second most common hospital-acquired infection and is associated with high morbidity and mortality. The insertion of the endotracheal tube (ETT) compromises the hosts bronchial defenses and facilitates the formation of microbial biofilms and VAP. Although antibiotics remain the cornerstone treatment, their efficacy is often compromised by poor biofilm penetration and the prevalence of multidrug-resistant bacteria. This reveals the need to develop novel non-antibiotic therapeutic strategies. We engineered a non-pathogenic strain of the pulmonary bacteria Mycoplasma pneumoniae CV2 to express anti-biofilm and bactericidal enzymes (VAP platform). The therapeutic effect was validated in a murine model of Pseudomonas aeruginosa infection and ex vivo in ETTs of patients with P. aeruginosa-induced VAP. Methods.We evaluated the safety of an improved version of CV2 (CV8, Mycochassis) in a miniature pig model, and its efficacy with the VAP therapeutic platform (CV8_VAP) in a piglet model of P. aeruginosa-induced VAP. CV8_VAP was administered via intralobe instillation or nebulization. We assessed the platforms ability to degrade biofilms formed on ETTs in vivo and its impact on the airway microbiota. We evaluated bacterial load in the lungs, inflammatory responses, histopathological and the clinical outcomes to provide a comprehensive characterization of therapeutic efficacy and safety. Findings.VAP platform reduced biofilm thickness and P. aeruginosa load in ETTs and lungs, with the nebulized route showing enhanced efficacy. CV8_VAP contributed on microbiome diversity restoration. Inflammatory markers in the lung were markedly decreased, suggesting a local immunomodulatory effect. Interpretation.These findings support that CV8_VAP as a promising, non-antibiotic therapeutic strategy for treating VAP caused by P. aeruginosa. Funding.The project that gave rise to these results has received funding from "La Caixa" Foundation (HR18-00058), and the European Research Council under the European Unions Horizon 2020 research and innovation program (670216).

synthetic biology↗

bioRxiv2022

Automatic Sleep Stage Classification with Optimized Selection of EEG Channels

Visual inspection of Polysomnography (PSG) recordings by sleep experts based on established guidelines has been the gold standard in sleep stage classification. This approach is expensive, time consuming and mostly limited to experimental research and clinical cases of major sleep disorders. Various automatic approaches to sleep scoring have been emerging in the past years and are opening the way to a quick computational assessment of sleep architecture that may find its way to the clinics. With the hope to make sleep scoring a fully automated process in the clinics, we report here an ensemble algorithm that aims at not only predicting sleep stages but of doing so with an optimized minimal number of EEG channels. For that, we combine a genetic algorithm based optimization with a classification framework that minimizes the number of channels used by the machine learning algorithm to quantify sleep stages. This resulted in a scoring with an F1 score of 0.793 for the fully automatic model and 0.806 for the model trained on 10 percent of the unseen subject, both with only 3 EEG channels. The ensemble algorithm is based on a combination of extremely randomized trees and MiniRocket classifiers. The algorithm was trained, validated and tested on night sleep PSG data collected from 7 subjects. The novelty of our approach lies on the use of the minimum information needed for automated sleep scoring, based on a systematic search that concurrently selects the optimal-minimum number of EEG channels and the best performing features for the machine learning classifier. The optimization framework presented in this work may enable new designs for sleep scoring devices suited to studies in the comfort of the homes, easily and inexpensively and in this way facilitate experimental and clinical studies in large populations.

neuroscience↗

bioRxiv2021

Automated methodology for optimal selection of the minimum electrode subset for accurate EEG source localization based on Genetic Algorithm optimization

High-density Electroencephalography (HD-EEG) has been proven to be the most accurate option to estimate the neural activity inside the brain. Multiple studies report the effect of electrode number on source localization for specific sources and specific electrode configurations. The electrodes for these configurations have been manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, but electrodes were not selected according to their contribution to accuracy. In this work, an optimization-based study is proposed to determine the minimum number of electrodes and identify optimal combinations of electrodes that can retain the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single and multiple source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that concurrently minimizes 1) the localization error for each source and 2) the number of required EEG electrodes. The method can be used for evaluating the source localization quality of low-density EEG systems (e.g. consumer-grade wearable EEG). We performed an evaluation over synthetic and real EEG dataset with known ground-truth. The experimental results show that optimal subsets with 6 electrodes can obtain an equal or better accuracy than HD-EEG (with more than 200 channels) for a single source case. This happened when reconstructing a particular brain activity in more than 88% of the cases (in synthetic signals) and 63% (in real signals), and in more than 88% and 73% of the cases when considering optimal combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that optimized combinations of 8, 12 and 16 electrodes attained an equal or better accuracy than HD-EEG with 231 electrodes in at least 58%, 76%, and 82% of the cases respectively. Additionally, for such electrode numbers, a lower mean error and standard deviation than with 231 electrodes were obtained. HighlightsO_LIThe number of EEG electrodes and their locations can be optimized for reconstructing the brain source activity. C_LIO_LIOptimally selected EEG electrodes can retain the accuracy of high density montages (256, 128 chs) for brain source estimation, when electrodes are selected according to their contribution to accuracy. C_LIO_LIWith optimization, selected combinations of EEG electrodes will flexibilize the estimation of the source activity. C_LI

neuroscience↗

bioRxiv2020

Cerebrospinal fluid (CSF) boosts metabolism and virulence expression factors in Acinetobacter baumannii

In a recent report by the Centers for Disease Control and Prevention (CDC), multidrug resistant (MDR) Acinetobacter baumannii is a pathogen described as an "urgent threat". Infection with this bacterium manifests as different diseases such as community and nosocomial pneumonia, bloodstream infections, endocarditis, urinary tract, wound infections, burn infections, skin and soft tissue infections, and meningitis. In particular, nosocomial meningitis, a common complication of neurosurgery caused by extensively-drug resistant (XDR) A. baumannii, is extremely challenging to manage. Therefore, it is necessary to identify signals, such as exposure to cerebrospinal fluid (CSF), that trigger expression of virulence factors that are associated with the successful establishment and progress of this infection. While a hypervirulent A. baumannii strain did not show changes in its transcriptome when incubated in the presence of CSF, a low-virulence isolate showed significant differences in gene expression and phenotypic traits. Exposure to 4% CSF caused increased expression of virulence factors such as fimbriae, pilins, and iron chelators, and virulence as determined in various model systems. Furthermore, although CSFs presence did not enhance bacterial growth, it was associated with an increase of expression of genes encoding transcription, translation, and the ATP synthesis machinery. Experiments to identify the active CSF component pointed to human serum albumin (HSA). ImportanceAcinetobacter baumannii, notorious for its multidrug resistant phenotype, overcomes nutrient deprived and desiccated conditions through its metabolic flexibility, pathogenic and physiological adaptability. Although this pathogen is commonly associated with respiratory infections, there have been a considerable amount of cases of A. baumannii bacterial meningitis. These infections are usually post-neurological surgery complications associated with high mortality rates ranging from 40 to 70%. This work describes interactions that may occur during A. baumannii infection of human cerebrospinal fluid (CSF). A. baumanniis displays capabilities to persist and thrive in a nutrient-limited environment, which also triggers the expression of virulence factors. This work also further explores A. baumanniis utilization of an essential component within CSF to trigger enhanced expression of genes associated with its pathoadaptibility in this environment.

microbiology↗