How to Count Our Microbes? The Effect of Different Quantitative Microbiome Profiling Approaches.

Next-generation sequencing (NGS) has instigated the research on the role of the microbiome in health and disease. The compositional nature of such microbiome datasets makes it however challenging to identify those microbial taxa that are truly associated with an intervention or health outcome. Quantitative microbiome profiling overcomes the compositional structure of microbiome sequencing data by integrating absolute quantification of microbial abundances into the NGS data. Both cell-based methods (e.g., flow cytometry) and molecular methods (qPCR) have been used to determine the absolute microbial abundances, but to what extent different quantification methods generate similar quantitative microbiome profiles has so far not been explored. Here we compared relative microbiome profiling (without incorporation of microbial quantification) to three variations of quantitative microbiome profiling: (1) microbial cell counting using flow cytometry (QMP), (2) counting of microbial cells using flow cytometry combined with Propidium Monoazide pre-treatment of fecal samples before metagenomics DNA isolation in order to only profile the microbial composition of intact cells (QMP-PMA), and (3) molecular based quantification of the microbial load using qPCR targeting the 16S rRNA gene. Although qPCR and flow cytometry both resulted in accurate and strongly correlated results when quantifying the bacterial abundance of a mock community of bacterial cells, the two methods resulted in highly divergent quantitative microbial profiles when analyzing the microbial composition of fecal samples from 16 healthy volunteers. These differences could not be attributed to the presence of free extracellular prokaryotic DNA in the fecal samples as sample pre-treatment with Propidium Monoazide did not improve the concordance between qPCR-based and flow cytometry-based QMP. Also lack of precision of qPCR was ruled out as a major cause of the disconcordant findings, since quantification of the fecal microbial load by the highly sensitive digital droplet PCR correlated strongly with qPCR. In conclusion, quantitative microbiome profiling is an elegant approach to bypass the compositional nature of microbiome NGS data, however it is important to realize that technical sources of variability may introduce substantial additional bias depending on the quantification method being used.

Cell Type- and Exposure-Specific Modulation of CD63/CD81-Positive and Tissue Factor-Positive Extracellular Vesicle Release in response to Respiratory Toxicants.

Chronic exposure to respiratory stressors increases the risk for pulmonary and cardiovascular diseases. Previously, we have shown that cigarette smoke extract (CSE) triggers the release of CD63+CD81+ and tissue factor (TF)+ procoagulant extracellular vesicles (EVs) by bronchial epithelial cells via depletion of cell surface thiols. Here, we hypothesized that this represents a universal response for different pulmonary cell types and respiratory exposures. Using bead-based flow cytometry, we found that bronchial epithelial cells and pulmonary fibroblasts, but not pulmonary microvascular endothelial cells or macrophages, release CD63+CD81+ and TF+ EVs in response to CSE. Cell surface thiols decreased in all cell types upon CSE exposure, whereas depletion of cell surface thiols using bacitracin only triggered EV release by epithelial cells and fibroblasts. The thiol-antioxidant NAC prevented the EV induction by CSE in epithelial cells and fibroblasts. Exposure of epithelial cells to occupational silica nanoparticles and particulate matter (PM) from outdoor air pollution also enhanced EV release. Cell surface thiols were mildly decreased and NAC partly prevented the EV induction for PM10, but not for silica and PM2.5. Taken together, induction of procoagulant EVs is a cell type-specific response to CSE. Moreover, induction of CD63+CD81+ and TF+ EVs in bronchial epithelial cells appears to be a universal response to various respiratory stressors. TF+ EVs may serve as biomarkers of exposure and/or risk in response to respiratory exposures and may help to guide preventive treatment decisions.

Cigarette smoke extract induced exosome release is mediated by depletion of exofacial thiols and can be inhibited by thiol-antioxidants.

INTRODUCTION: Airway epithelial cells have been described to release extracellular vesicles (EVs) with pathological properties when exposed to cigarette smoke extract (CSE). As CSE causes oxidative stress, we investigated whether its oxidative components are responsible for inducing EV release and whether this could be prevented using the thiol antioxidants N-acetyl-l-cysteine (NAC) or glutathione (GSH). METHODS: BEAS-2B cells were exposed for 24h to CSE, H2O2, acrolein, 5,5'-dithiobis-(2-nitrobenzoic acid) (DTNB), bacitracin, rutin or the anti-protein disulfide isomerase (PDI) antibody clone RL90; with or without NAC or GSH. EVs in media were measured using CD63+CD81+ bead-coupled flow cytometry or tunable resistive pulse sensing (TRPS). For characterization by Western Blotting, cryo-transmission electron microscopy and TRPS, EVs were isolated using ultracentrifugation. Glutathione disulfide and GSH in cells were assessed by a GSH reductase cycling assay, and exofacial thiols using Flow cytometry. RESULTS: CSE augmented the release of the EV subtype exosomes, which could be prevented by scavenging thiol-reactive components using NAC or GSH. Among thiol-reactive CSE components, H2O2 had no effect on exosome release, whereas acrolein imitated the NAC-reversible exosome induction. The exosome induction by CSE and acrolein was paralleled by depletion of cell surface thiols. Membrane impermeable thiol blocking agents, but not specific inhibitors of the exofacially located thiol-dependent enzyme PDI, stimulated exosome release. SUMMARY/CONCLUSION: Thiol-reactive compounds like acrolein account for CSE-induced exosome release by reacting with cell surface thiols. As acrolein is produced endogenously during inflammation, it may influence exosome release not only in smokers, but also in ex-smokers with chronic obstructive pulmonary disease. NAC and GSH prevent acrolein- and CSE-induced exosome release, which may contribute to the clinical benefits of NAC treatment.