Lipoproteins in Gram-negative bacteria: new insights into their biogenesis, subcellular targeting and functional roles

The bacteria cell envelope is a complex multilayered structure that serves to protect these organisms from their unpredictable and often hostile environment. The cell envelopes of most bacteria fall into one of two major groups. Gram-negative bacteria are surrounded by a thin peptidoglycan cell wall, which itself is surrounded by an outer membrane containing lipopolysaccharide. Gram-positive bacteria lack an outer membrane but are surrounded by layers of peptidoglycan many times thicker than is found in the gram-negatives. Threading through these layers of peptidoglycan are long anionic polymers, called teichoic acids. The composition and organization of these envelope layers and recent insights into the mechanisms of cell envelope assembly are discussed.

A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI-cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram-positive lipoprotein signal peptides differ from Gram-negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram-positive test set. A genome search was carried out for 12 Gram-negative genomes and one Gram-positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network-based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/.

Quantitative views of cellular functions require precise measures of rates of biomolecule production, especially proteins-the direct effectors of biological processes. Here, we present a genome-wide approach, based on ribosome profiling, for measuring absolute protein synthesis rates. The resultant E. coli data set transforms our understanding of the extent to which protein synthesis is precisely controlled to optimize function and efficiency. Members of multiprotein complexes are made in precise proportion to their stoichiometry, whereas components of functional modules are produced differentially according to their hierarchical role. Estimates of absolute protein abundance also reveal principles for optimizing design. These include how the level of different types of transcription factors is optimized for rapid response and how a metabolic pathway (methionine biosynthesis) balances production cost with activity requirements. Our studies reveal how general principles, important both for understanding natural systems and for synthesizing new ones, emerge from quantitative analyses of protein synthesis. Copyright © 2014 Elsevier Inc. All rights reserved.