Protein-translocating trimeric autotransporters of gram-negative bacteria.
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ABSTRACT:
SUBMITTER: Kim DS
PROVIDER: S-EPMC1540064 | biostudies-literature | 2006 Aug
REPOSITORIES: biostudies-literature
Publications
Protein-translocating trimeric autotransporters of gram-negative bacteria.
Kim David S H DS Chao Yi Y Saier Milton H MH
Journal of bacteriology 20060801 16
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Project description:Burkholderia pseudomallei is a tier 1 select agent and the causative agent of melioidosis, a severe and often fatal disease with symptoms ranging from acute pneumonia and septic shock to a chronic infection characterized by abscess formation in the lungs, liver, and spleen. Autotransporters (ATs) are exoproteins belonging to the type V secretion system family, with many playing roles in pathogenesis. The genome of B. pseudomallei strain 1026b encodes nine putative trimeric AT proteins, of which only four have been described. Using a bioinformatic approach, we annotated putative domains within each trimeric AT protein, excluding the well-studied BimA protein, and found short repeated sequences unique to Burkholderia species, as well as an unexpectedly large proportion of ATs with extended signal peptide regions (ESPRs). To characterize the role of trimeric ATs in pathogenesis, we constructed disruption or deletion mutations in each of eight AT-encoding genes and evaluated the resulting strains for adherence to, invasion of, and plaque formation in A549 cells. The majority of the ATs (and/or the proteins encoded downstream) contributed to adherence to and efficient invasion of A549 cells. Using a BALB/c mouse model of infection, we determined the contributions of each AT to bacterial burdens in the lungs, liver, and spleen. At 48 h postinoculation, only one strain, Bp340::pDbpaC, demonstrated a defect in dissemination and/or survival in the liver, indicating that BpaC is required for wild-type virulence in this model.
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Project description:Environmental Gram Negative Bacteria Genome Sequencing and Assembly
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Project description:This deposit is composed of paired-end reads from 90 isolates of four different gram-negative bacterial species. The raw reads were used to generate predictions of antimicrobial resistance. Raw sequence reads
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Project description:The exploration of therapies combining antimicrobial lung proteins and conventional antibiotics is important due to the growing problem of multidrug-resistant bacteria. The aim of this study was to investigate whether human SP-A and a recombinant trimeric fragment (rfhSP-A) have cooperative antimicrobial activity with antibiotics against pathogenic Gram-negative bacteria. We found that SP-A bound the cationic peptide polymyxin B (PMB) with an apparent dissociation constant (K D) of 0.32 ± 0.04 µM. SP-A showed synergistic microbicidal activity with polymyxin B and E, but not with other antibiotics, against three SP-A-resistant pathogenic bacteria: Klebsiella pneumoniae, non-typable Haemophilus influenzae (NTHi), and Pseudomonas aeruginosa. SP-A was not able to bind to K. pneumoniae, NTHi, or to mutant strains thereof expressing long-chain lipopolysaccharides (or lipooligosaccharides) and/or polysaccharide capsules. In the presence of PMB, SP-A induced the formation of SP-A/PMB aggregates that enhance PMB-induced bacterial membrane permeabilization. Furthermore, SP-A bound to a molecular derivative of PMB lacking the acyl chain (PMBN) with a K D of 0.26 ± 0.02 μM, forming SP-A/PMBN aggregates. PMBN has no bactericidal activity but can bind to the outer membrane of Gram-negative bacteria. Surprisingly, SP-A and PMBN showed synergistic bactericidal activity against Gram-negative bacteria. Unlike native supratrimeric SP-A, the trimeric rfhSP-A fragment had small but significant direct bactericidal activity against K. pneumoniae, NTHi, and P. aeruginosa. rfhSP-A did not bind to PMB under physiological conditions but acted additively with PMB and other antibiotics against these pathogenic bacteria. In summary, our results significantly improve our understanding of the antimicrobial actions of SP-A and its synergistic action with PMB. A peptide based on SP-A may aid the therapeutic use of PMB, a relatively cytotoxic antibiotic that is currently being reintroduced into clinics due to the global problem of antibiotic resistance.
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Project description:BackgroundGenome-wide prediction of protein subcellular localization is an important type of evidence used for inferring protein function. While a variety of computational tools have been developed for this purpose, errors in the gene models and use of protein sorting signals that are not recognized by the more commonly accepted tools can diminish the accuracy of their output.ResultsAs part of an effort to manually curate the annotations of 19 strains of Shewanella, numerous insights were gained regarding the use of computational tools and proteomics data to predict protein localization. Identification of the suite of secretion systems present in each strain at the start of the process made it possible to tailor-fit the subsequent localization prediction strategies to each strain for improved accuracy. Comparisons of the computational predictions among orthologous proteins revealed inconsistencies in the computational outputs, which could often be resolved by adjusting the gene models or ortholog group memberships. While proteomic data was useful for verifying start site predictions and post-translational proteolytic cleavage, care was needed to distinguish cellular versus sample processing-mediated cleavage events. Searches for lipoprotein signal peptides revealed that neither TatP nor LipoP are designed for identification of lipoprotein substrates of the twin arginine translocation system and that the +2 rule for lipoprotein sorting does not apply to this Genus. Analysis of the relationships between domain occurrence and protein localization prediction enabled identification of numerous location-informative domains which could then be used to refine or increase confidence in location predictions. This collective knowledge was used to develop a general strategy for predicting protein localization that could be adapted to other organisms.ConclusionImproved localization prediction accuracy is not simply a matter of developing better computational algorithms. It also entails gathering key knowledge regarding the host architecture and translocation machinery and associated substrate recognition via experimentation and integration of diverse computational analyses from many proteins and, where possible, that are derived from different species within the same genus.
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