Biology subjects

Gaudin, A.

Publications and source records attributed to Gaudin, A..

4 recordsLinked to original sources

bioRxiv2025

Lipid nanoemulsion incorporating DOTAP reverse micelles as clinically translatable carriers of ALDH inhibitors for lung cancer therapy

Lung cancer remains the most prevalent malignancy worldwide and the leading cause of cancer-related deaths. In this study, we designed and optimized a lipid nanosystem incorporating the cationic lipid DOTAP by utilizing reverse micelle structures to enhance pulmonary tropism. Fluorescence quenching assays confirmed the incorporation of reverse micelles into the oily core, thus validating the structural integrity of the system. This nanosystem was tailored for the encapsulation of ABD0171, a potent inhibitor of ALDH1A3, an enzyme strongly associated with chemoresistance in lung cancer. Physicochemical characterization revealed robust colloidal properties with a particle diameter of 60 nm, a surface charge of +45 mV, and a drug encapsulation yield of 99%. In vitro, formulations with/without DOTAP demonstrated high efficacy against epithelial-like H358 cells derived from human bronchioalveolar carcinoma, with IC50 values < 5 {micro}M. Chicken ChorioAllantoic Membrane (CAM) evaluations demonstrated good tolerability of the DOTAP-containing formulation and significant H358 tumor reduction by 28% compared with the untreated control, while maintaining a favorable safety profile. By combining industrially feasible methods with FDA-approved components, this study demonstrated the potential of DOTAP-containing lipid nanosystems as scalable platforms for the delivery of ALDH inhibitors for lung cancer treatment. These findings provide a pathway for future applications in cancer therapy, bridging the gap between nanosystem innovation and clinical translation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/659868v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@dbc8org.highwire.dtl.DTLVardef@5d5187org.highwire.dtl.DTLVardef@9391f3org.highwire.dtl.DTLVardef@52fd54_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

bioengineering↗

bioRxiv2024

Saturated fatty acid-Coenzyme A supplementation restores neuronal energy levels and protein homeostasis in hereditary spastic paraplegia

Mitochondrial ATP production is fuelled by a fatty acid flux generated by phospholipase and triglyceride lipases in metabolically demanding tissues such as heart and liver, while the brain has long been believed to use almost solely glucose for energy. Phospholipase A1 enzyme DDHD2 is a major triglyceride lipase in the brain, and the loss of DDHD2 function results in a saturated free fatty acid (sFFA) imbalance and lipid droplet (LD) accumulation in the brain. The LD accumulation in neurons has been enigmatic as LDs are mainly considered to serve as a fuel storage. Here, we demonstrate that the loss of DDHD2 results in a mitochondrial respiratory dysfunction that leads to a significant decrease in ATP production and acetyl coenzyme A levels in neurons, even when the glycolytic breakdown of glycose occurs normally. Loss of DDHD2 also leads to a presynaptic defect as well as an imbalance in the global protein homeostasis in the neurons. These defects were rescued by external supplementation of the sFFA myristic acid coupled with its cofactor coenzyme A (Myr-CoA), indicating sFFA fuelling for neuronal {beta}-oxidation. We have thus discovered that the sFFAs released by the activity of DDHD2 play a central role in providing energy to fuel synaptic function. One Sentence SummaryFree fatty acids released by DDHD2 activity play a central role in maintaining neuronal energy levels and synaptic function.

neuroscience↗

bioRxiv2023

Alternative cell entry mechanisms for SARS-CoV-2 and multiple animal viruses

The cell entry mechanism of SARS-CoV-2, the causative agent of the COVID-19 pandemic, is not fully understood. Most animal viruses hijack cellular endocytic pathways as an entry route into the cell. Here, we show that in cells that do not express serine proteases such as TMPRSS2, genetic depletion of all dynamin isoforms blocked the uptake and strongly reduced infection with SARS-CoV-2 and its variant Delta. However, increasing the viral loads partially and dose-dependently restored infection via a thus far uncharacterized entry mechanism. Ultrastructural analysis by electron microscopy showed that this dynamin-independent endocytic processes appeared as 150-200 nm non-coated invaginations and was efficiently used by numerous mammalian viruses, including alphaviruses, influenza, vesicular stomatitis, bunya, adeno, vaccinia, and rhinovirus. Both the dynamin-dependent and dynamin-independent infection of SARS-CoV-2 required a functional actin cytoskeleton. In contrast, the alphavirus Semliki Forest virus, which is smaller in diameter, required actin only for the dynamin-independent entry. The presence of TMPRSS2 protease rescued SARS-CoV-2 infection in the absence of dynamins. Collectively, these results indicate that some viruses such as canine parvovirus and SARS-CoV-2 mainly rely on dynamin for endocytosis-dependent infection, while other viruses can efficiently bypass this requirement harnessing an alternative infection entry route dependent on actin.

microbiology↗

bioRxiv2018

A New Method to Correct for Habitat Filtering in Microbial Correlation Networks

Amplicon sequencing of 16S, ITS, and 18S regions of microbial genomes is a commonly used first step toward understanding microbial communities of interest for human health, agriculture, and the environment. Correlation network analysis is an emerging tool for investigating the interactions within these microbial communities. However, when data from different habitats (e.g sampling sites, host genotype, etc.) are combined into one analysis, habitat filtering (co-occurrence of microbes due to habitat sampled rather than biological interactions) can induce apparent correlations, resulting in a network dominated by habitat effects and masking correlations of biological interest. We developed an algorithm to correct for habitat filtering effects in microbial correlation network analysis in order to reveal the true underlying microbial correlations. This algorithm was tested on simulated data that was constructed to exhibit habitat filtering. Our algorithm significantly improved correlation detection accuracy for these data compared to Spearman and Pearson correlations. We then used our algorithm to analyze a real data set of 16S-V4 amplicon sequences that was expected to exhibit habitat filtering. Our algorithm was found to effectively reduce habitat effects, enabling the construction of consensus correlation networks from data sets combining multiple related sample habitats.

bioinformatics↗