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Reduced set of virulence genes allows high accuracy prediction of bacterial pathogenicity in humans.

Iraola G, Vazquez G, Spangenberg L, Naya H - PLoS ONE (2012)

Bottom Line: An accuracy of 95% using a cross-fold validation scheme with in-fold feature selection is obtained when classifying human pathogens and non-pathogens.A reduced subset of highly informative genes (120) is presented and applied to an external validation set.Also, we analyze which functional categories of virulence genes were more distinctive for pathogenicity in each taxonomic group, which seems to be a completely new kind of information and could lead to important evolutionary conclusions.

View Article: PubMed Central - PubMed

Affiliation: Unidad de Bioinformática, Institut Pasteur Montevideo, Montevideo, Uruguay.

ABSTRACT
Although there have been great advances in understanding bacterial pathogenesis, there is still a lack of integrative information about what makes a bacterium a human pathogen. The advent of high-throughput sequencing technologies has dramatically increased the amount of completed bacterial genomes, for both known human pathogenic and non-pathogenic strains; this information is now available to investigate genetic features that determine pathogenic phenotypes in bacteria. In this work we determined presence/absence patterns of 814 different virulence-related genes among more than 600 finished bacterial genomes from both human pathogenic and non-pathogenic strains, belonging to different taxonomic groups (i.e: Actinobacteria, Gammaproteobacteria, Firmicutes, etc.). An accuracy of 95% using a cross-fold validation scheme with in-fold feature selection is obtained when classifying human pathogens and non-pathogens. A reduced subset of highly informative genes (120) is presented and applied to an external validation set. The statistical model was implemented in the BacFier v1.0 software (freely available at http : ==bacfier:googlecode:com=files=Bacfier v1 0:zip), that displays not only the prediction (pathogen/non-pathogen) and an associated probability for pathogenicity, but also the presence/absence vector for the analyzed genes, so it is possible to decipher the subset of virulence genes responsible for the classification on the analyzed genome. Furthermore, we discuss the biological relevance for bacterial pathogenesis of the core set of genes, corresponding to eight functional categories, all with evident and documented association with the phenotypes of interest. Also, we analyze which functional categories of virulence genes were more distinctive for pathogenicity in each taxonomic group, which seems to be a completely new kind of information and could lead to important evolutionary conclusions.

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Frequencies of each of  genes per bacterial taxonomic group.Frquency calculation was performed for each gene as in Figure 2. Red triangles show significative genes that apart from the  distribution (same frequency in pathogens and non-pathogens) by exact Fisher test, black circles are non significative genes.
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pone-0042144-g004: Frequencies of each of genes per bacterial taxonomic group.Frquency calculation was performed for each gene as in Figure 2. Red triangles show significative genes that apart from the distribution (same frequency in pathogens and non-pathogens) by exact Fisher test, black circles are non significative genes.

Mentions: As shown in Figure 3 the number of present genes is highly variable among classes (pathogens and non-pathogens) and even between taxonomic groups. Moreover, a great number of these present genes, belonging to the functional categories, presented a frequency bias towards either pathogenic or non-pathogenic species (Figure 4), deviating from the proposed hypothesis. These findings supported the idea that presence/absence patterns of virulence-related genes are informative enough to discriminate between human pathogenic and non-pathogenic bacterial species (Table 1), indicating that this data can be used to construct a classification model based on highly significant biological information.


Reduced set of virulence genes allows high accuracy prediction of bacterial pathogenicity in humans.

Iraola G, Vazquez G, Spangenberg L, Naya H - PLoS ONE (2012)

Frequencies of each of  genes per bacterial taxonomic group.Frquency calculation was performed for each gene as in Figure 2. Red triangles show significative genes that apart from the  distribution (same frequency in pathogens and non-pathogens) by exact Fisher test, black circles are non significative genes.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3412846&req=5

pone-0042144-g004: Frequencies of each of genes per bacterial taxonomic group.Frquency calculation was performed for each gene as in Figure 2. Red triangles show significative genes that apart from the distribution (same frequency in pathogens and non-pathogens) by exact Fisher test, black circles are non significative genes.
Mentions: As shown in Figure 3 the number of present genes is highly variable among classes (pathogens and non-pathogens) and even between taxonomic groups. Moreover, a great number of these present genes, belonging to the functional categories, presented a frequency bias towards either pathogenic or non-pathogenic species (Figure 4), deviating from the proposed hypothesis. These findings supported the idea that presence/absence patterns of virulence-related genes are informative enough to discriminate between human pathogenic and non-pathogenic bacterial species (Table 1), indicating that this data can be used to construct a classification model based on highly significant biological information.

Bottom Line: An accuracy of 95% using a cross-fold validation scheme with in-fold feature selection is obtained when classifying human pathogens and non-pathogens.A reduced subset of highly informative genes (120) is presented and applied to an external validation set.Also, we analyze which functional categories of virulence genes were more distinctive for pathogenicity in each taxonomic group, which seems to be a completely new kind of information and could lead to important evolutionary conclusions.

View Article: PubMed Central - PubMed

Affiliation: Unidad de Bioinformática, Institut Pasteur Montevideo, Montevideo, Uruguay.

ABSTRACT
Although there have been great advances in understanding bacterial pathogenesis, there is still a lack of integrative information about what makes a bacterium a human pathogen. The advent of high-throughput sequencing technologies has dramatically increased the amount of completed bacterial genomes, for both known human pathogenic and non-pathogenic strains; this information is now available to investigate genetic features that determine pathogenic phenotypes in bacteria. In this work we determined presence/absence patterns of 814 different virulence-related genes among more than 600 finished bacterial genomes from both human pathogenic and non-pathogenic strains, belonging to different taxonomic groups (i.e: Actinobacteria, Gammaproteobacteria, Firmicutes, etc.). An accuracy of 95% using a cross-fold validation scheme with in-fold feature selection is obtained when classifying human pathogens and non-pathogens. A reduced subset of highly informative genes (120) is presented and applied to an external validation set. The statistical model was implemented in the BacFier v1.0 software (freely available at http : ==bacfier:googlecode:com=files=Bacfier v1 0:zip), that displays not only the prediction (pathogen/non-pathogen) and an associated probability for pathogenicity, but also the presence/absence vector for the analyzed genes, so it is possible to decipher the subset of virulence genes responsible for the classification on the analyzed genome. Furthermore, we discuss the biological relevance for bacterial pathogenesis of the core set of genes, corresponding to eight functional categories, all with evident and documented association with the phenotypes of interest. Also, we analyze which functional categories of virulence genes were more distinctive for pathogenicity in each taxonomic group, which seems to be a completely new kind of information and could lead to important evolutionary conclusions.

Show MeSH
Related in: MedlinePlus