Fig 1.
E. coli Lipid A, the lipid moiety of LPS, constitutes the outer leaflet of the outer membrane and anchors LPS to the outer membrane[17].
Fig 2.
Outer leaflet of Gram-negative bacterial outer membrane.
A schematic representation of the Gram-negative bacteria outer membrane, showing Lipid A on the outer leaflet of the outer membrane.
Fig 3.
The Raetz pathway for LPS biosynthesis in Gram-negative bacteria.
Analytes are shown as monitored with our method for E. coli. Acyl chain lengths may vary between bacterial species. Molecular formulas and exact mass as depicted are shown. Enzymes are noted for each transformation and analytes are named as both the product of a specific enzyme and/or a common chemical name.
Table 1.
MRM settings for monitoring E. coli LPS intermediates and P. aeruginosa internal standard.
Fig 4.
Comparison of LPS intermediates between E. coli ΔtolC and E. coli Δcdh ΔtolC.
CDH-diacylglycerol pyrophosphatase in E. coli ΔtolC remains active in detergent lysis conditions, as indicated by increased signal of LpxA product (2), LpxC product (3), and LpxD product (4) as well as decreased signal of Lipid X (5) in E. coli Δcdh ΔtolC.
Fig 5.
Compound structures of tested LPS inhibitors.
CHIR-090 (9), ChemDiv 6359–0284 (10), and ChemDiv C324-2728 (11)9. CHIR-090 (9) is a LpxC inhibitor[15], ChemDiv 6359–0284 (10) is a LpxD inhibitor[17], and ChemDiv C324-2728 (11) is a LpxH inhibitor[16].
Fig 6.
OD600 values for untreated and compound treated cells.
OD600 values for cells treated at 8X MIC with LpxC inhibitor (CHIR-090 (9), 0.032 μg/mL) and putative LpxH inhibitor (ChemDiv C324-2728 (11), 16 μg/mL) show little difference compared to untreated cells at 1 hour; However treatments longer than 1 hour show very different OD600 values for the three conditions, indicating that cell density and viability is not consistent between conditions at these time-points.
Fig 7.
Relative levels of LPS intermediates in LpxA/D/K-depleted cells measured by NP-LCMS/MS.
Peak areas of each transition were summed for each parent mass and normalized against OD600 and the C10 Pseudomonas aeruginosa LpxA product (8) internal standard. Normalized responses of genetically perturbed cells were then compared against untreated E. coli ΔtolC cells. Metabolites with signal below the limit of detection are denoted by ND. (A) Fold change of LPS intermediates in LpxA depletion (n = 3) for 3 hours compared to untreated E. coli ΔtolC cells. (B) Fold change of LPS intermediates after LpxD depletion (n = 3) compared to untreated E. coli ΔtolC cells. (C) Fold change of LPS intermediates after LpxK depletion (n = 3) compared to untreated E. coli ΔtolC cells. (D) Fold change of LPS intermediates in ClearColi cells (n = 3) compared to untreated E. coli ΔtolC cells. Error bars represent the standard deviation of the fold change of treated cells over untreated cells.
Fig 8.
Relative levels of LPS intermediates in genetically depleted cells measured by NP-LCMS/MS.
(A) Fold change of LPS intermediates in E. coli Δcdh ΔtolC cells treated with CHIR-090 (9) (n = 8) at 8X MIC compared to untreated E. coli Δcdh ΔtolC cells. (B) Fold change of LPS intermediates in E. coli Δcdh ΔtolC cells treated with ChemDiv 6359–0284 (10) (n = 8), an LpxD inhibitor, at 8X MIC compared to untreated E. coli Δcdh ΔtolC cells. (C) Fold change of LPS intermediates in E. coli Δcdh ΔtolC cells treated with ChemDiv C324-2728 (11) (n = 8), a putative LpxH inhibitor, at 8X MIC compared to untreated E. coli Δcdh ΔtolC cells. Error bars represent the standard deviation of the fold change of treated cells over untreated cells.
Table 2.
Average accumulation of LPS intermediates in three independent experiments with compounds at 8X MIC (n = 8).
Table 3.
%CV of fold change across independent experiments for compound-treated E. coli Δcdh ΔtolC.
Fig 9.
Heatmap displaying LPS intermediate levels of E. coli Δcdh ΔtolC cells treated with various antibiotics in dose-response.
The red color indicates an accumulation, grey indicates no significant change, and blue indicates depletion. Hierarchical clustering was performed in Spotfire (version 6.5.3) and displayed as a row dendogram. Values in all columns were normalized to scale between 0 and 1 and used in the clustering calculation. Distances between all possible combinations of two rows were calculated using Euclidean distance measure. These calculated distances were then used in the UPGMA clustering method to derive the distance between all clusters that were formed from the rows during the clustering.