Mechanistic Insights into the Role of Extracellular Vesicles at the Bacterial–Host Interface

Bacterial and human cells produce extracellular vesicles (EVs) in response to diverse stimuli, e.g., toxins, oxidative stress, nutrient depletion, or high cell density. Here, we describe a cocultivation platform that allows recovery of mixed extracellular vesicles (mix-EVs) produced simultaneously by both cell types. We investigated interactions between Gram-positive and Gram-negative bacteria (<i>Streptococcus pyogenes</i>, <i>Staphylococcus aureus</i>, <i>Escherichia coli</i>, and <i>Neisseria meningitidis</i>) and human peripheral blood mononuclear cells (PBMCs). The production of the mix-EVs population decreased with higher bacterial concentrations. Exposing PBMCs to mix-EVs repressed the general transcriptomic signature, in contrast with a significant upregulation generated by single bacterial-EVs. However, mix-EVs-derived IL-1β upregulation was confirmed at the protein level. Inhibition experiments showed that IL-1β production involved TLR2 and TLR4 signaling, acting through IRAK-1 and TRAF6 related pathways. This approach provides a new platform for the study of EVs at the pathogen-host interface and presents mechanistic insights into the effect of EVs on an infected host.

Authors

Institutions

Publication Details

Journal
ACS Infectious Diseases
Published
2025-11-16
DOI
https://doi.org/10.1021/acsinfecdis.5c00664
Primary Topic
Bacterial Infections and Vaccines
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mechanistic Insights into the Role of Extracellular Vesicles at the Bacterial–Host Interface

Christoph Saffer, Marc Thilo Figge, Cláudia Vilhena, Miriana Battista
ACS Infectious Diseases
Bacterial Infections and Vaccines
article

Mechanistic Insights into the Role of Extracellular Vesicles at the Bacterial–Host Interface

Christoph Saffer, Marc Thilo Figge, Cláudia Vilhena, Miriana Battista
article en

Abstract

Bacterial and human cells produce extracellular vesicles (EVs) in response to diverse stimuli, e.g., toxins, oxidative stress, nutrient depletion, or high cell density. Here, we describe a cocultivation platform that allows recovery of mixed extracellular vesicles (mix-EVs) produced simultaneously by both cell types. We investigated interactions between Gram-positive and Gram-negative bacteria (<i>Streptococcus pyogenes</i>, <i>Staphylococcus aureus</i>, <i>Escherichia coli</i>, and <i>Neisseria meningitidis</i>) and human peripheral blood mononuclear cells (PBMCs). The production of the mix-EVs population decreased with higher bacterial concentrations. Exposing PBMCs to mix-EVs repressed the general transcriptomic signature, in contrast with a significant upregulation generated by single bacterial-EVs. However, mix-EVs-derived IL-1β upregulation was confirmed at the protein level. Inhibition experiments showed that IL-1β production involved TLR2 and TLR4 signaling, acting through IRAK-1 and TRAF6 related pathways. This approach provides a new platform for the study of EVs at the pathogen-host interface and presents mechanistic insights into the effect of EVs on an infected host.

ACS Infectious DiseasesVol. 11(12)
Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Schiller International University (FR), Interface (United States) (US), Biology of Infection (FR), Engineering Systems (United States) (US), Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie e. V. - Hans-Knöll-Institut (HKI) (DE), Friedrich Schiller University Jena (DE)
Openalex Percentile: Top 68%
Bacterial Infections and Vaccines
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.