Mosaic Evolution of Beta-Barrel-Porin-Encoding Genes in Escherichia coli.

Bacterial porin-encoding genes are often found under positive selection. Local recombination has also been identified in a few of them to facilitate bacterial rapid adaptation, although it remains unknown whether it is a common evolutionary mechanism for the porins or outer membrane proteins in Gram-negative bacteria. In this study, we investigated the beta-barrel (ß-barrel) porin-encoding genes in Escherichia coli that were reported under positive Darwinian selection. Besides fhuA that was found with ingenic local recombination previously, we identified four other genes, i.e., lamB, ompA, ompC, and ompF, all showing the similar mosaic evolution patterns. Comparative analysis of the protein sequences disclosed a list of highly variable regions in each family, which are mostly located in the convex of extracellular loops and coinciding with the binding sites of bacteriophages. For each of the porin families, mosaic recombination leads to unique combinations of the variable regions with different sequence patterns, generating diverse protein groups. Structural modeling indicated a conserved global topology among the different porins, with the extracellular surface varying a lot due to individual or combinatorial variable regions. The conservation of global tertiary structure would ensure the channel activity, while the wide diversity of variable regions may represent selection to avoid the invasion of phages, antibiotics or immune surveillance factors. Our study identified multiple bacterial porin genes with mosaic evolution. We hypothesize that this could be generalized strategy for outer membrane proteins to both maintain normal life processes and evade the attack of unfavored factors rapidly. IMPORTANCE Microevolution studies can disclose more elaborate evolutionary mechanisms of genes, appearing especially important for genes with multifaceted function such as those encoding outer membrane proteins. However, in most cases, the gene is considered as a whole unit, and the evolutionary patterns are disclosed. Here, we report that multiple bacterial porin proteins follow mosaic evolution, with local ingenic recombination combined with spontaneous mutations based on positive Darwinian selection, and conservation for most structural regions. This could represent a common mechanism for bacterial outer membrane proteins. The variable regions within each porin family showed large coincidence with the binding sites of bacteriophages, antibiotics, and immune factors and therefore would represent effective targets for the development of new antibacterial agents or vaccines.

Computational prediction of secreted proteins in gram-negative bacteria.

Gram-negative bacteria harness multiple protein secretion systems and secrete a large proportion of the proteome. Proteins can be exported to periplasmic space, integrated into membrane, transported into extracellular milieu, or translocated into cytoplasm of contacting cells. It is important for accurate, genome-wide annotation of the secreted proteins and their secretion pathways. In this review, we systematically classified the secreted proteins according to the types of secretion systems in Gram-negative bacteria, summarized the known features of these proteins, and reviewed the algorithms and tools for their prediction.

T1SEstacker: A Tri-Layer Stacking Model Effectively Predicts Bacterial Type 1 Secreted Proteins Based on C-Terminal Non-repeats-in-Toxin-Motif Sequence Features.

Type 1 secretion systems play important roles in pathogenicity of Gram-negative bacteria. However, the substrate secretion mechanism remains largely unknown. In this research, we observed the sequence features of repeats-in-toxin (RTX) proteins, a major class of type 1 secreted effectors (T1SEs). We found striking non-RTX-motif amino acid composition patterns at the C termini, most typically exemplified by the enriched "[FLI][VAI]" at the most C-terminal two positions. Machine-learning models, including deep-learning ones, were trained using these sequence-based non-RTX-motif features and further combined into a tri-layer stacking model, T1SEstacker, which predicted the RTX proteins accurately, with a fivefold cross-validated sensitivity of ∼0.89 at the specificity of ∼0.94. Besides substrates with RTX motifs, T1SEstacker can also well distinguish non-RTX-motif T1SEs, further suggesting their potential existence of common secretion signals. T1SEstacker was applied to predict T1SEs from the genomes of representative Salmonella strains, and we found that both the number and composition of T1SEs varied among strains. The number of T1SEs is estimated to reach 100 or more in each strain, much larger than what we expected. In summary, we made comprehensive sequence analysis on the type 1 secreted RTX proteins, identified common sequence-based features at the C termini, and developed a stacking model that can predict type 1 secreted proteins accurately.