Is the C-terminal insertional signal in Gram-negative bacterial outer membrane proteins species-specific or not?

Abstract

Background: In Gram-negative bacteria, the outer membrane is composed of an asymmetric lipid bilayer of phopspholipids and lipopolysaccharides, and the transmembrane proteins that reside in this membrane are almost exclusively β-barrel proteins. These proteins are inserted into the membrane by a highly conserved and essential machinery, the BAM complex. It recognizes its substrates, unfolded outer membrane proteins (OMPs), through a C-terminal motif that has been speculated to be species-specific, based on theoretical and experimental results from only two species, Escherichia coli and Neisseria meningitidis, where it was shown on the basis of individual sequences and motifs that OMPs from the one cannot easily be over expressed in the other, unless the C-terminal motif was adapted. In order to determine whether this species specificity is a general phenomenon, we undertook a large-scale bioinformatics study on all predicted OMPs from 437 fully sequenced proteobacterial strains.

Results: We were able to verify the incompatibility reported between Escherichia coli and Neisseria meningitidis, using clustering techniques based on the pairwise Hellinger distance between sequence spaces for the C-terminal motifs of individual organisms. We noticed that the amino acid position reported to be responsible for this incompatibility between Escherichia coli and Neisseria meningitidis does not play a major role for determining species specificity of OMP recognition by the BAM complex. Instead, we found that the signal is more diffuse, and that for most organism pairs, the difference between the signals is hard to detect. Notable exceptions are the Neisseriales, and Helicobacter spp. For both of these organism groups, we describe the specific sequence requirements that are at the basis of the observed difference.

Conclusions: Based on the finding that the differences between the recognition motifs of almost all organisms are small, we assume that heterologous overexpression of almost all OMPs should be feasible in E. coli and other Gram-negative bacterial model organisms. This is relevant especially for biotechnology applications, where recombinant OMPs are used e.g. for the development of vaccines. For the species in which the motif is significantly different, we identify the residues mainly responsible for this difference that can now be changed in heterologous expression experiments to yield functional proteins.

Figure 1

Cluster map based on 437 sequenced Gram-negative organisms. In the cluster map each node represents one organism. The Hellinger distance was used to calculate the pairwise overlap between the multi-dimensional peptide sequence spaces of organisms. The calculated similarity or overlap was used to cluster the organism in CLANS. Figure 1A is colored by taxonomic class and Figure 1B is colored by the number of peptides in each organism.

Figure 2

CLANS cluster map of randomly shuffled peptides from 437 organisms. Figure 2A is colored by taxonomic class and Figure 2B is colored by the number of peptides in an organism. Colors are similar to Figure 1.

Figure 3

Frequency plots derived from unique C-terminal insertion signal peptides for Escherichia (Figure 3A) and Neisseria (Figure 3B) strains. Frequency plots were made from 188 unique peptides of 31 Escherichia strains and 50 unique peptides of 7 Neisseria strains. The +2 position is indicated by the arrow in the figure. Escherichia strains (Figure 3A) have no strong preference for any amino acid at the +2 position, whereas Neisseria strains (Figure 3B) have a strong preference for positively charged amino acids (Arg and Lys) at the +2 position. Hydrophobic residues are colored in blue and polar residues are colored in red.

Figure 4

Percentage of Arg and Lys at +2 positions. We calculated the percentage of Arg and Lys residues at the +2 position from all unique peptides from the 437 organisms; color is based on taxonomic class. The Neisseria strains show a high preference for positively charged amino acids at the +2 position compared to other organisms.

Figure 5

Frequency plots of C-terminal β-strands from Proteobacteria. Frequency plots generated from unique peptides of α-proteobacteria are shown in Figure 5A, of β-Proteobacteria in Figure 5B, of γ-Proteobacteria in Figure 5C, of δ-Proteobacteria in Figure 5D and of ε-Proteobacteria in Figure 5E. The frequency plots are overall very similar; an exception is the high frequency of His at the +3 position in β-Proteobacteria and of Tyr at the +5 position in ε-Proteobacteria.

Figure 6

Frequency of His at the +3 position. The percentage of His at +3 was calculated from all unique peptides from 437 organisms. A high preference for His at +3 is observed for 16-stranded OMPs of β-Proteobacteria. Since there is a high number of 16-stranded OMPs in Burkholderia strains (see Additional file 1 and Additional file 2), they were also annotated in the plot.

Figure 7

Frequency plot of unique C-terminal β-strands from Helicobacter species. 163 unique C-terminal insertion signals from 14 Helicobacter strains were used to generate this plot. The +5 position which has the strong preference of Tyr is marked with the arrow.

Figure 8

The percentage of Tyr (Figure 8A) and aromatic hydrophobic amino acids (Figure 8B) at the +5 position. For Figure 8A, we calculated the percentage of Tyr at the +5 position from all unique peptides from 437 organisms and for Figure 8B, we calculated the frequency of Tyr, Phe and Trp at the +5 position from all unique peptides from 437 organisms. In both plots Helicobacter strains shows a high preference of Tyr and aromatic amino acids at the +5 position.

Figure 9

Control experiments to show the influence of overrepresented OMP classes. OMP classes OMP.8 (Figure 9A), OMP.12 (Figure 9B), OMP.16 (Figure 9C) and OMP.22 (Figure 9D) were removed and only organisms with more than 20 unique peptides were used in the clustering. Peptides belonging to OMP.nn and OMP.hypo (OMPs with unknown strand number and function) were not removed from the data set during the control experiments. Color legends are similar to the Figure 1A.

Figure 10

CLANS cluster map of OMP-Organism class based entities. In figure 10A and figure 10B, each node is a representative of OMP-Organism entities that have more than five unique peptides of a single OMP class from an individual organism. In Figure 10A, entities are only from the OMP.22 class, which includes entities from all proteobacterial taxonomic classes. In Figure 10B, entities are only from γ-Proteobacteria and include different OMP classes.

Figure 11

Illustration of the difference between the Euclidean distance and the Hellinger distance for one-dimensional Gaussian distributions. Two Gaussian distributions are shown as black lines for different choices of μ and σ. The grey area indicates the overlap between both distributions. |μ1−μ2| is the Euclidean distance between the centers of the Gaussians, D_H is the Hellinger distance (equation 1). Both values are indicated in the title of panels A-D. A: For μ1 = μ2 = 0, σ1 = σ2 = 1, the Euclidean distance and the Hellinger distance are both zero. B: For μ1 = μ2 = 0, σ1 =1, σ2 = 5 the Euclidean distance is zero, whereas the Hellinger distance is larger than zero because the distributions do not overlap perfectly (the second Gaussian is wider than the first). C: For μ1 =0, μ2 = 5, σ1 = σ2 = 1, the Euclidean distance is five, whereas the Hellinger distance almost attains its maximum because the distributions only overlap little. D: For μ1 =0, μ2 = 5, σ1 =1, σ2 =5, the Euclidean distance is still five as in C because the means did not change. However, the Hellinger distance is larger than in C because the second Gaussian is wider, which leads to a larger overlap between the distributions.