Bacteria release nano-sized extracellular vesicles (EVs) into the extracellular milieu. Bacterial EVs contain molecular cargo originating from the parent bacterium and have important roles in bacterial survival and pathogenesis. Using 8-plex iTRAQ approaches, we profiled the EV proteome of two Escherichia coli strains, uropathogenic (UPEC) 536 and probiotic Nissle 1917. For these strains, we compared the proteome of crude input EVs prepared by ultracentrifugation alone with EVs purified by either density gradient centrifugation (DGC) or size exclusion chromatography (SEC). We further compared the proteome of EVs from bacterial cultures that were grown in iron-restricted (R) and iron-supplemented (RF) conditions. Overall, outer membrane components were highly enriched, and bacterial inner membrane components were significantly depleted in both UPEC and Nissle EVs, in keeping with an outer membrane origin. In addition, we found enrichment of ribosome-related Gene Ontology terms in UPEC EVs and proteins involved in glycolytic processes and ligase activity in Nissle EVs. We have identified that three proteins (RbsB of UPEC in R; YoeA of UPEC in RF; BamA of Nissle in R) were consistently enriched in the DGC- and SEC-purified EV samples in comparison to their crude input EV, whereas conversely the 60 kDa chaperonin GroEL was enriched in the crude input EVs for both UPEC and Nissle in R condition. Such proteins may have utility as technical markers for assessing the purity of E. coli EV preparations. Several proteins were changed in their abundance depending on the iron availability in the media. Data are available via ProteomeXchange with identifier PXD011345. In summary, we have undertaken a comprehensive characterization of the protein content of E. coli EVs and found evidence of specific EV cargos for physiological activity and conserved protein cargo that may find utility as markers in the future. Abbreviation: DGC: density gradient centrifugation; DTT: 1,4-dithiothreitol; EV: extracellular vesicles; FDR: false discovery rate; GO: Gene Ontology; R: iron-restricted; RF: iron-supplemented; iTRAQ: isobaric tags for relative and absolute quantitation; OMV: outer membrane vesicle; SWATH-MS: sequential window acquisition of all theoretical mass spectra; SEC: size exclusion chromatography.

Prokaryotes release membrane vesicles (MVs) with direct roles in disease pathogenesis. MVs are heterogeneous when isolated from bacterial cultures so Density Gradient Centrifugation (DGC) is valuable for separation of MV subgroups from contaminating material. Here we report the technical variability and natural biological heterogeneity seen between DGC preparations of MVs for Mycobacterium smegmatis and Escherichia coli and compare these DGC data with size exclusion chromatography (SEC) columns. Crude preparations of MVs, isolated from cultures by ultrafiltration and ultracentrifugation were separated by DGC with fractions manually collected as guided by visible bands. Yields of protein, RNA and endotoxin, protein banding and particle counts were analysed in these. DGC and SEC methods enabled separation of molecularly distinct MV populations from crude MVs. DGC banding profiles were unique for each of the two species of bacteria tested and further altered by changing culture conditions, for example with iron supplementation. SEC is time efficient, reproducible and cost effective method that may also allow partial LPS removal from Gram-negative bacterial MVs. In summary, both DGC and SEC are suitable for the separation of mixed populations of MVs and we advise trials of both, coupled with complete molecular and single vesicle characterisation prior to downstream experimentation.

Bacteria secrete RNAs, some of which have effects on other cells and on other species as signalling RNAs. Prokaryotic membrane vesicles (MVs) contain a range of RNA types. The MV structure offers protection from degradation as well as receptors to facilitate delivery to target cells. Microscopic imaging and molecular biology analyses have provided evidence to demonstrate that bacterial MVs deliver their RNA into eukaryotic cells. Moreover, in some cases the RNA cargo is demonstrably functional and phenotypic changes can be identified in MV-RNA treated target cells. The challenge now is to dissect the effect of MV-RNA on target cells away from the effects of non-RNA components of the MV such as lipopolysaccharide that can co-purify with RNA.

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Cells from all domains of life release extracellular vesicles (EVs), packages that carry a cargo of molecules that participate in communication, co-ordination of population behaviours, virulence and immune response mechanisms. Mammalian EVs play an increasingly recognised role to fight infection, yet may also be commandeered to disseminate pathogens and enhance infection. EVs released by bacterial pathogens may deliver toxins to host cells, signalling molecules and new DNA to other bacteria, and act as decoys, protecting infecting bacteria from immune killing. In this review, we explore the role of EVs in infection from the perspective of both the pathogen and host, and highlight their importance in the host/pathogen relationship. We highlight proposed strategies for EVs in therapeutics, and call attention to areas where existing knowledge and evidence is lacking.

Biofilm infections can be chronic, life threatening and challenging to eradicate. Understanding in vivo stimuli affecting the biofilm cycle is one step toward targeted prevention strategies. Iron restriction by the host is a stimulus for biofilm formation for some Staphylococcus aureus isolates; however, in some infection scenarios bacteria are exposed to abundant amounts of hemoglobin (Hb), which S. aureus is able to use as iron source. Thus, we hypothesized a role for Hb in the biofilm infection. Microplate “biofilm” assays showed biofilm-matrix production was increased in the presence of hemoglobin when compared to the provision of iron as an inorganic salt. Microscopic analysis of biofilms showed that the provision of iron as hemoglobin consistently caused thicker and more structured biofilms when compared to the effect of the inorganic iron source. Iron responsive biofilm gene expression analysis showed that Agr Quorum Sensing, a known biofilm dispersal marker, was repressed with hemoglobin but induced with an equivalent amount of inorganic iron in the laboratory strain Newman. The gene expression of two biofilm structuring agents, PSMα and PSMβ, differed in the response to the iron source provided and was not correlated to hemoglobin-structured biofilms. A comparison of the model pathogen S. aureus Newman with local clinical isolates demonstrated that while there was a similar phenotypic biofilm response to hemoglobin, there was substantial variation in the expression of key biofilm dispersal markers, suggesting an underappreciated variation in biofilm regulome among S. aureus isolates and that no general inferences can be made by studying the behavior of single strains.

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Bacterial extracellular vesicles (EVs) are nanoscale lipidenclosed packages that are released by bacteria cells and shuttle various biomolecules between bacteria or host cells. They are implicated in playing several important roles, from infectious disease progression to maintaining proper gut health, however the tools available to characterise and classify them are limited and impractical for many applications. Surface-enhanced Raman Spectroscopy (SERS) provides a promising means of rapidly fingerprinting bacterial EVs in a label-free manner by taking advantage of plasmonic resonances that occur on nanopatterned surfaces, effectively amplifying the inelastic scattering of incident light. In this study, we demonstrate that by applying machine learning algorithms to bacterial EV SERS spectra, EVs from cultures of the same bacterial species (Escherichia coli) can be classified by strain, culture conditions, and purification method. While these EVs are highly purified and homogeneous compared to complex samples, the ability to classify them from a single species demonstrates the incredible power of SERS when combined with machine learning, and the importance of considering these parameters in future applications. We anticipate that these findings will play a crucial role in developing the laboratory and clinical utility of bacterial EVs, such as the label-free, noninvasive, and rapid diagnosis of infections without the need to culture samples from blood, urine, or other fluids.