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Nieuwland, R.

Publications and source records attributed to Nieuwland, R..

2 recordsLinked to original sources
bioRxiv2023

M1-derived extracellular vesicles polarize recipient macrophages into M2 and alter skeletal muscle homeostasis in a hyper-glucose environment

BackgroundMacrophages release not only cytokines but also extracellular vesicles (EVs). EVs are small lipid-derived vesicles with virus-like properties transferring lipids, RNA and proteins between cells. Until now, the consequences of macrophage plasticity on the release and the composition of EVs have been poorly explored. In this study, we determined the impact of high-glucose (HG) concentrations on macrophage metabolism, and characterized their derived EV subpopulations. Finally, we determined whether HG-treated macrophage-derived EVs participate in immune responses and in metabolic alterations of skeletal muscle cells. MethodsTHP1-macrophages (M0) were treated with 15mM (MG15) or 30mM (MG30) glucose. M1 or M2 canonical markers, pro- and anti-inflammatory cytokines and lactate production were evaluated. Macrophage-derived EVs were characterized by TEM, flow cytometry, and 1H-Nuclear magnetic resonance spectroscopy for lipid composition. M0 macrophages or C2C12 muscle cells were used as recipients of MG15 and MG30-derived EVs. The lipid profiles of recipient cells were determined, as well as protein and mRNA levels of relevant genes for macrophage polarization or muscle metabolism. ResultsM0 released 2 populations of small and large EVs (sEVs, lEVs) with specific lipid profiles. Proportionally to the glucose concentration, glucose-treatment induced glycolysis in M0 macrophages which consequently shifted into a pro-inflammatory M1 phenotype, containing increased triacylglycerol and cholesterol content. Glucose also affected macrophage sphingolipid and phospholipid compositions. The lipid profile differences between sEVs and lEVs were abolished and represented the lipid profile alterations of MG15 and MG30 macrophages. Both sEVs and lEVs from M15 and M30 macrophages polarized M0 into anti-inflammatory M2, with increased contents of triacylglycerol and cholesterol. MG15 lEVs and sEVs induced insulin-induced AKT hyper-phosphorylation and accumulation of triacylglycerol in muscle cells, a state observed in pre-diabetes. Conversely, MG30 lEVs and sEVs induced insulin resistance in myotubes. ConclusionsAs inflammation involves first M1 macrophages, then the activation of M2 macrophages to attenuate inflammation, this study demonstrates that the dialog between macrophages through the EV route is an intrinsic part of the inflammatory response. In a hyperglycemic context, EV macrophages could participate in the development of muscle insulin-resistance and chronic inflammation.

cell biology↗
bioRxiv2023

Proteome encoded determinants of protein sorting into extracellular vesicles

Extracellular vesicles (EVs) are membranous structures released by cells into the extracellular space and are thought to be involved in cell-to-cell communication. While EVs and their cargo are promising biomarker candidates, protein sorting mechanisms of proteins to EVs remain unclear. In this study, we ask if it is possible to determine EV association based on the protein sequence. Additionally, we ask what the most important determinants are for EV association. We answer these questions with explainable AI models, using human proteome data from EV databases to train and validate the model. It is essential to correct the datasets for contaminants introduced by coarse EV isolation workflows and for experimental bias caused by mass spectrometry. In this study, we show that it is indeed possible to predict EV association from the protein sequence: a simple sequence-based model for predicting EV proteins achieved an area under the curve of 0.77{+/-}0.01, which increased further to 0.84{+/-}0.00 when incorporating curated post-translational modification (PTM) annotations. Feature analysis shows that EV associated proteins are stable, polar, and structured with low isoelectric point compared to non-EV proteins. PTM annotations emerged as the most important features for correct classification; specifically palmitoylation is one of the most prevalent EV sorting mechanisms for unique proteins. Palmitoylation and nitrosylation sites are especially prevalent in EV proteins that are determined by very strict isolation protocols, indicating they could potentially serve as quality control criteria for future studies. This computational study offers an effective sequence-based predictor of EV associated proteins with extensive characterisation of the human EV proteome that can explain for individual proteins which factors contribute to their EV association.

bioinformatics↗