Distinguishable codon usage and amino acid composition patterns among substrates of leaderless secretory pathways from proteobacteria

Kampenusa, Inara; Zikmanis, Pēteris

doi:10.1007/s00253-009-2423-8

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…Interestingly, the distribution of non-optimal codon usage in the N-terminal region showed a similar tendency to that of the putative substrate of the Sec translocon. Kampenusa et al recently reported that the CAI was useful for discriminating among substrates from four different types (I, III, IV, and VI) of secretion systems [34]. The present study revealed that codon bias was especially prominent in the N-terminal region of the secretion substrates.…”

Section: Resultsmentioning

confidence: 48%

Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria

2011

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BackgroundMany 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.ResultsIn 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.ConclusionsWe 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.

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Section: Resultsmentioning

confidence: 48%

Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria

2011

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“…When the effectors encounter plant or human cells, they are capable of promoting virulence through breaking up and repressing the cells’ immune signals. These effectors are typically categorized as non-classically secreted proteins (NCSPs), which have no signal peptides, and contain uncommon or diverse patterns of the amino acids in their corresponding sequence regions (Arnold et al, 2009; Bendtsen et al, 2004; Kampenusa and Zikmanis, 2010). By contrast, the proteins secreted through the general secretion (Sec) or twin-arginine translocation (Tat) pathways of the type II, V, VII secretion systems (termed as T2SS, T5SS, and T7SS, respectively), and sometimes the T4SS, are categorized as classically secreted proteins (CSPs) (Nielsen and Krogh, 1998; Saier, 2006; Tseng et al, 2009).…”

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confidence: 99%

Computational Identification and Comparative Analysis of Secreted and Transmembrane Proteins in Six Burkholderia Species

Nguyen

Lee

Park

et al. 2017

The Plant Pathology Journal

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As a step towards discovering novel pathogenesis-related proteins, we performed a genome scale computational identification and characterization of secreted and transmembrane (TM) proteins, which are mainly responsible for bacteria-host interactions and interactions with other bacteria, in the genomes of six representative Burkholderia species. The species comprised plant pathogens (B. glumae BGR1, B. gladioli BSR3), human pathogens (B. pseudomallei K96243, B. cepacia LO6), and plant-growth promoting endophytes (Burkholderia sp. KJ006, B. phytofirmans PsJN). The proportions of putative classically secreted proteins (CSPs) and TM proteins among the species were relatively high, up to approximately 20%. Lower proportions of putative type 3 non-classically secreted proteins (T3NCSPs) (~10%) and unclassified non-classically secreted proteins (NCSPs) (~5%) were observed. The numbers of TM proteins among the three clusters (plant pathogens, human pathogens, and endophytes) were different, while the distribution of these proteins according to the number of TM domains was conserved in which TM proteins possessing 1, 2, 4, or 12 TM domains were the dominant groups in all species. In addition, we observed conservation in the protein size distribution of the secreted protein groups among the species. There were species-specific differences in the functional characteristics of these proteins in the various groups of CSPs, T3NCSPs, and unclassified NCSPs. Furthermore, we assigned the complete sets of the conserved and unique NCSP candidates of the collected Burkholderia species using sequence similarity searching. This study could provide new insights into the relationship among plant-pathogenic, human-pathogenic, and endophytic bacteria.

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Comparative and bioinformatics analyses of pathogenic bacterial secretomes identified by mass spectrometry in Burkholderia species

et al. 2017

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Secreted proteins (secretomes) play crucial roles during bacterial pathogenesis in both plant and human hosts. The identification and characterization of secretomes in the two plant pathogens Burkholderia glumae BGR1 and B. gladioli BSR3, which cause diseases in rice such as seedling blight, panicle blight, and grain rot, are important steps to not only understand the disease-causing mechanisms but also find remedies for the diseases. Here, we identified two datasets of secretomes in B. glumae BGR1 and B. gladioli BSR3, which consist of 118 and 111 proteins, respectively, using mass spectrometry approach and literature curation. Next, we characterized the functional properties, potential secretion pathways and sequence information properties of secretomes of two plant pathogens in a comparative analysis by various computational approaches. The ratio of potential non-classically secreted proteins (NCSPs) to classically secreted proteins (CSPs) in B. glumae BGR1 was greater than that in B. gladioli BSR3. For CSPs, the putative hydrophobic regions (PHRs) which are essential for secretion process of CSPs were screened in detail at their N-terminal sequences using hidden Markov model (HMM)-based method. Total 31 pairs of homologous proteins in two bacterial secretomes were indicated based on the global alignment (identity ≥ 70%). Our results may facilitate the understanding of the species-specific features of secretomes in two plant pathogenic Burkholderia species.

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Distinguishable codon usage and amino acid composition patterns among substrates of leaderless secretory pathways from proteobacteria

Cited by 3 publications

References 39 publications

Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria

Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria

Computational Identification and Comparative Analysis of Secreted and Transmembrane Proteins in Six Burkholderia Species

Comparative and bioinformatics analyses of pathogenic bacterial secretomes identified by mass spectrometry in Burkholderia species

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