Genome-Wide Assessment of Outer Membrane Vesicle Production in Escherichia coli

Abstract

The production of outer membrane vesicles by Gram-negative bacteria has been well documented; however, the mechanism behind the biogenesis of these vesicles remains unclear. Here a high-throughput experimental method and systems-scale analysis was conducted to determine vesiculation values for the whole genome knockout library of Escherichia coli mutant strains (Keio collection). The resultant dataset quantitatively recapitulates previously observed phenotypes and implicates nearly 150 new genes in the process of vesiculation. Gene functional and biochemical pathway analyses suggest that mutations that truncate outer membrane structures such as lipopolysaccharide and enterobacterial common antigen lead to hypervesiculation, whereas mutants in oxidative stress response pathways result in lower levels. This study expands and refines the current knowledge regarding the cellular pathways required for outer membrane vesiculation in E. coli.

Fig 1. Outline of experimental and computational approaches used in this study.

(A) Flowchart of experimental and computational analyses used to measure and select mutants with vesiculation phenotypes significantly different from the mean of the Keio collection (see Methods). (B) Flowchart of the high-throughput dot-blot method to determine vesiculation values. Cultures grown in 96-well plates were centrifuged to pellet cells. Using a vacuum manifold, supernatant from each culture was passed through a filter to remove remaining cells (blue) and through a nitrocellulose membrane to capture OMVs (red). The membrane was then probed using anti-LPS antibodies to detect captured OMVs, and densitometry was employed to quantify vesiculation levels. (C) Growth rates were calculated for each of the mutants tested for vesiculation to rule out lysis or poor growth. Each box represents a growth curve for one strain. The dotted line (right) represents the remaining growth curves not shown. Growth rates were calculated from raw data (blue dots) using Gompertz regression fit to the data (green line). For clarity, a growth curve for one mutant is shown in zoomed view below.

Fig 2. Analysis of high throughput vesiculation experiment.

The range of the vesiculation scores from the high throughput method is displayed on the histogram. Mutants with log10 vesiculation scores outside two standard deviations from the mean of the distribution (vertical dotted lines) were considered in subsequent analyses.

Fig 3. Biosynthesis of immature LPS alters vesiculation.

Vesiculation phenotypes of the LPS core sugar biosynthesis pathway are depicted in the pathway diagram. Colored boxes represent vesiculation values for mutants of enzyme-coding genes at each step of the pathway. The vesiculation score for the mutants of each respective enzyme are indicated by the color of the box (see legend). Pathway adapted from KEGG [40].

Fig 4. ECA biosynthesis regulates vesiculation.

The network for ECA biosynthesis is shown. Colors, arrows, boxes, and dots are as in Fig 3. Pathway adapted from EcoCyc [41]. Abbreviations for sugar intermediates are as follows: UDP-GlcNAc, UDP-N-acetyl-glucosamine; UDP-ManNAc, UDP-N-acetyl-mannosamine; UDP-ManNAcA, UDP-N-acetyl-mannosaminouronate; UndP-UDP-GlcNAc, undecaprenyl phosphate-UDP-N-acetyl-glucosamine; glucpyr, glucopyranose; dTDP-thomoNAc, dTDP-N-acetylthomosamine.

Fig 5. Validation and hypothesis testing.

Vesiculation levels were tested using the established FCOP method for selected mutants with vesiculation values significantly different from the mean according to the high throughput dot-blot method. Bar graph compares vesiculation values as measured by the whole genome high throughput screen (HTP) vs. large-scale vesicle preparation (FCOP). Each bar represents the average of at least 3 biological replicate trials. These mean values and standard error are given in S4 Table.