Article
A directed screen for chlamydial proteins secreted by a type III mechanism identifies a translocated protein and numerous other new candidates.
Unité de Biologie des Interactions Cellulaires, Institut Pasteur, CNRS URA 2582, 25 rue du Docteur Roux, 75015 Paris, France.
Molecular Microbiology (impact factor:
5.01).
07/2005;
56(6):1636-47.
DOI:10.1111/j.1365-2958.2005.04647.x
pp.1636-47
Source: PubMed
-
Citations (0)
- Cited In (10)
-
Article: Targeting of a chlamydial protease impedes intracellular bacterial growth.
[show abstract] [hide abstract]
ABSTRACT: Chlamydiae are obligate intracellular bacteria that propagate in a cytosolic vacuole. Recent work has shown that growth of Chlamydia induces the fragmentation of the Golgi apparatus (GA) into ministacks, which facilitates the acquisition of host lipids into the growing inclusion. GA fragmentation results from infection-associated cleavage of the integral GA protein, golgin-84. Golgin-84-cleavage, GA fragmentation and growth of Chlamydia trachomatis can be blocked by the peptide inhibitor WEHD-fmk. Here we identify the bacterial protease chlamydial protease-like activity factor (CPAF) as the factor mediating cleavage of golgin-84 and as the target of WEHD-fmk-inhibition. WEHD-fmk blocked cleavage of golgin-84 as well as cleavage of known CPAF targets during infection with C. trachomatis and C. pneumoniae. The same effect was seen when active CPAF was expressed in non-infected cells and in a cell-free system. Ectopic expression of active CPAF in non-infected cells was sufficient for GA fragmentation. GA fragmentation required the small GTPases Rab6 and Rab11 downstream of CPAF-activity. These results define CPAF as the first protein that is essential for replication of Chlamydia. We suggest that this role makes CPAF a potential anti-infective therapeutic target.PLoS Pathogens 09/2011; 7(9):e1002283. · 9.13 Impact Factor -
Article: Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria.
[show abstract] [hide abstract]
ABSTRACT: Many pathogens use a type III secretion system to translocate virulence proteins (called effectors) in order to adapt to the host environment. To date, many prediction tools for effector identification have been developed. However, these tools are insufficiently accurate for producing a list of putative effectors that can be applied directly for labor-intensive experimental verification. This also suggests that important features of effectors have yet to be fully characterized. In this study, we have constructed an accurate approach to predicting secreted virulence effectors from Gram-negative bacteria. This consists of a support vector machine-based discriminant analysis followed by a simple criteria-based filtering. The accuracy was assessed by estimating the average number of true positives in the top-20 ranking in the genome-wide screening. In the validation, 10 sets of 20 training and 20 testing examples were randomly selected from 40 known effectors of Salmonella enterica serovar Typhimurium LT2. On average, the SVM portion of our system predicted 9.7 true positives from 20 testing examples in the top-20 of the prediction. Removal of the N-terminal instability, codon adaptation index and ProtParam indices decreased the score to 7.6, 8.9 and 7.9, respectively. These discrimination features suggested that the following characteristics of effectors had been uncovered: unstable N-terminus, non-optimal codon usage, hydrophilic, and less aliphathic. The secondary filtering process represented by coexpression analysis and domain distribution analysis further refined the average true positive counts to 12.3. We further confirmed that our system can correctly predict known effectors of P. syringae DC3000, strongly indicating its feasibility. We have successfully developed an accurate prediction system for screening effectors on a genome-wide scale. We confirmed the accuracy of our system by external validation using known effectors of Salmonella and obtained the accurate list of putative effectors of the organism. The level of accuracy was sufficient to yield candidates for gene-directed experimental verification. Furthermore, new features of effectors were revealed: non-optimal codon usage and instability of the N-terminal region. From these findings, a new working hypothesis is proposed regarding mechanisms controlling the translocation of virulence effectors and determining the substrate specificity encoded in the secretion system.BMC Bioinformatics 11/2011; 12:442. · 2.75 Impact Factor -
Article: Functional and computational analysis of amino acid patterns predictive of type III secretion system substrates in Pseudomonas syringae.
[show abstract] [hide abstract]
ABSTRACT: Bacterial type III secretion systems (T3SSs) deliver proteins called effectors into eukaryotic cells. Although N-terminal amino acid sequences are required for translocation, the mechanism of substrate recognition by the T3SS is unknown. Almost all actively deployed T3SS substrates in the plant pathogen Pseudomonas syringae pathovar tomato strain DC3000 possess characteristic patterns, including (i) greater than 10% serine within the first 50 amino acids, (ii) an aliphatic residue or proline at position 3 or 4, and (iii) a lack of acidic amino acids within the first 12 residues. Here, the functional significance of the P. syringae T3SS substrate compositional patterns was tested. A mutant AvrPto effector protein lacking all three patterns was secreted into culture and translocated into plant cells, suggesting that the compositional characteristics are not absolutely required for T3SS targeting and that other recognition mechanisms exist. To further analyze the unique properties of T3SS targeting signals, we developed a computational algorithm called TEREE (Type III Effector Relative Entropy Evaluation) that distinguishes DC3000 T3SS substrates from other proteins with a high sensitivity and specificity. Although TEREE did not efficiently identify T3SS substrates in Salmonella enterica, it was effective in another P. syringae strain and Ralstonia solanacearum. Thus, the TEREE algorithm may be a useful tool for identifying new effector genes in plant pathogens. The nature of T3SS targeting signals was additionally investigated by analyzing the N-terminus of FtsX, a putative membrane protein that was classified as a T3SS substrate by TEREE. Although the first 50 amino acids of FtsX were unable to target a reporter protein to the T3SS, an AvrPto protein substituted with the first 12 amino acids of FtsX was translocated into plant cells. These results show that the T3SS targeting signals are highly mutable and that secretion may be directed by multiple features of substrates.PLoS ONE 01/2012; 7(4):e36038. · 4.09 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed.
The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual
current impact factor.
Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence
agreement may be applicable.
Keywords
24 new candidate proteins
bacterial proliferation
C. caviae
C. trachomatis
Chlamydiae
chlamydial proteins
different chlamydial species
full-length proteins
genetic information
host cell cytosol
inclusion membrane
intracellular compartment
secretion assay
specific antibodies
specific proteins
TTS effectors
TTS mechanism
TTS signals
type III secretion
unknown function