Biology subjects

Torabian, P.

Publications and source records attributed to Torabian, P..

3 recordsLinked to original sources

bioRxiv2026

Bacterial extracellular vesicles indirectly destabilize a human stem cell-derived blood-brain barrier on-chip through pro-inflammatory stimulation of immune cells

Pathogenic bacterial extracellular vesicles (BEVs) can disrupt the blood-brain barrier (BBB), leading to neuroinflammation. Prior in vitro studies of this process were performed in simple models that may have lacked important physiological factors. We sought to determine if treatment with Escherichia coli-derived BEVs could directly compromise the integrity of a BBB lab-on-chip model or if an immune component was required. Our device featured isogenic human induced pluripotent stem cell-derived brain microvascular endothelial-like cells (BMECs) and pericytes separated by an ultrathin, porous silicon nitride membrane. BEVs and free lipopolysaccharide (LPS) were capable of causing upregulation of intercellular adhesion molecule-1 on the BMEC surfaces, which is important for immune cell recruitment. However, neither BEVs nor LPS at physiological doses caused pronounced loss of BMEC tight junction proteins, nor did they increase barrier permeability to small dye molecules. In contrast, stimulating THP-1 macrophages with BEVs led to increased production of pro-inflammatory cytokines, and conditioned media from the stimulated macrophages disrupted BMEC tight junctions and increased barrier permeability. Our work demonstrates the importance of incorporating an immune component in studies of BEV-mediated disruption of BBB models.

cell biology↗

bioRxiv2025

Antibiotic treatment modulates Escherichia coli-derived bacterial extracellular vesicle (BEV) production and their capacity to upregulate ICAM-1 in human endothelial cells

Antibiotic treatment is often necessary to eliminate life-threatening bacterial infections. However, these treatments can alter production of bacterial extracellular vesicles (BEVs), which often contain pro-inflammatory biomolecules. In this study, we examined how the clinically-relevant antibiotics meropenem, tobramycin, and ciprofloxacin impacted BEV production from a urinary tract infection-associated Escherichia coli strain (CFT073 [WAM2267]) and a meningitis-associated strain (K1 RS218). BEVs from both strains caused a dose-dependent increase in expression of intercellular adhesion molecule-1 (ICAM-1) in human umbilical vein endothelial cells, priming the endothelium for interactions with immune cells. Blockade of toll-like receptor 4 revealed that this receptor was responsible for BEV-endothelial interactions. Treatment with meropenem, a {beta}-lactam antibiotic, increased production of BEVs from strain K1 RS218. Furthermore, meropenem treatment caused strain CFT073 [WAM2267] to produce BEVs with heightened stimulatory capacity, possibly by amplifying the content of lipoprotein Lpp in these BEVs as measured by mass spectrometry. To our knowledge, this is the first study examining the interplay between antibiotic treatment and the effects of the resulting BEVs on endothelial ICAM-1 expression. Our results indicate treatment risks of certain antibiotics against specific strains of E. coli and could help identify therapeutic targets to reduce BEV-mediated endothelial stimulation during infection.

cell biology↗

bioRxiv2024

Complex Properties of Training Stimuli Affect Brain Alignment in a Deep Network Model of Mouse Visual Cortex

Deep convolutional neural networks are important models of the visual cortex that ac-count relatively well for brain activity and are able to perform ethologically relevant functions. However, it is unknown which combination of factors, such as network ar-chitecture, training objectives, and data best align this family of models with the brain. Here we investigate the statistics of training data. We hypothesized that stimuli that are naturalistic for mice would lead to higher similarity between deep network models and activity in mouse visual cortex. We used a video-game engine to create training datasets in which we varied the naturalism of the environment, the movement statis-tics, and the optics of the modelled eye. The naturalistic environment substantially and consistently led to greater brain similarity, while the other factors had more subtle and area-specific effects. We then hypothesized that differences in brain similarity between the two environments arose due to differences in spatial frequency spectra, distribu-tions of color and orientation, and/or temporal autocorrelations. To test this, we created abstract environments, composed of cubes and spheres, that resembled the naturalis-tic and non-naturalistic environments in these respects. Contrary to our expectations, these factors accounted poorly for differences in brain similarity due to the naturalis-tic and non-naturalistic environments. This suggests that the higher brain similarities we observed after training with the naturalistic environment were due to more complex factors.

neuroscience↗