Changes in lipid composition of host-derived extracellular vesicles following Salmonella infection.

IMPORTANCE: This study delves into the previously unexplored territory of extracellular vesicle (EV) cargo and composition, specifically focusing on lipid composition changes in EVs following Salmonella infection. EVs play crucial roles in intercellular communication, carrying a variety of biomolecules. Investigating how these EV cargo lipids change post-infection with Salmonella is significant for understanding the molecular mechanisms underlying lipid trafficking during infection. Given the impact of lipid composition on EV function, this research uncovers distinct differences in the lipid profiles of EVs at different time points post-infection and between infected and uninfected macrophages. This study identified lipids that are differentially abundant in EVs produced by the host during infection, offering novel insights into the dynamics of lipid profiles in EVs during cellular processes and infections. This work advances our understanding of host-pathogen interactions, specifically lipid-mediated EV functions during infection.

Extracellular vesicles elicit protective immune responses against Salmonella infection.

Small extracellular vesicles (sEVs) produced by antigen-presenting cells represent a novel mechanism of cell-to-cell communication. The sEVs have been shown to drive Th1-type adaptive immune responses against intracellular infections such as Salmonella. In this study, we have demonstrated that an administration of sEVs produced by Salmonella-infected macrophages to BALB/c mice that were then challenged with Salmonella infection decreased bacterial load in infected animals and led to protection against a lethal dose of Salmonella. Second, the same sEVs induced a robust production of IgA anti-Salmonella antibodies (Abs) in BALB/c mice, including IgA anti-OmpD Abs. These results show that the nanoscale sEVs stimulate adaptive immune responses against intracellular pathogens and that these sEVs can be used to provide animals with complete protection against lethal infection, such as the systemic bacterial infection in immunodeficient BALB/c mice.

Automating Aggregate Quantification in Caenorhabditis elegans.

A rise in the prevalence of neurodegenerative protein conformational diseases (PCDs) has fostered a great interest in this subject over the years. This increased attention has called for the diversification and improvement of animal models capable of reproducing disease phenotypes observed in humans with PCDs. Though murine models have proven invaluable, they are expensive and are associated with laborious, low-throughput methods. Use of the Caenorhabditis elegans nematode model to study PCDs has been justified by its relative ease of maintenance, low cost, and rapid generation time, which allow for high-throughput applications. Additionally, high conservation between the C. elegans and human genomes makes this model an invaluable discovery tool. Nematodes that express fluorescently tagged tissue-specific polyglutamine (polyQ) tracts exhibit age- and polyQ length-dependent aggregation characterized by fluorescent foci. Such reporters are often employed as proxies to monitor changes in proteostasis across tissues. Manual aggregate quantification is time-consuming, limiting experimental throughput. Furthermore, manual foci quantification can introduce bias, as aggregate identification can be highly subjective. Herein, a protocol consisting of worm culturing, image acquisition, and data processing was standardized to support high-throughput aggregate quantification using C. elegans that express intestine-specific polyQ. By implementing a C. elegans-based image processing pipeline using CellProfiler, an image analysis software, this method has been optimized to separate and identify individual worms and enumerate their respective aggregates. Though the concept of automation is not entirely unique, the need to standardize such procedures for reproducibility, elimination of bias from manual counting, and increase throughput is high. It is anticipated that these methods can drastically simplify the screening process of large bacterial, genomic, or drug libraries using the C. elegans model.