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Horizontal Gene Transfers in prokaryotes show differential preferences for metabolic and translational genes.

Kanhere A, Vingron M - BMC Evol. Biol. (2009)

Bottom Line: One successful approach to the detection of HGT events is due to Novichkov et al. (J.Genes transferred between the archaea and bacteria are mostly metabolic genes.On the other hand, genes transferred within the bacterial phyla are mainly involved in translation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany. a.kanhere@ucl.ac.uk

ABSTRACT

Background: Horizontal gene transfer (HGT) is an important process, which contributes in bacterial pathogenesis and drug resistance. A number of methods have been proposed for detection of horizontal gene transfer. One successful approach to the detection of HGT events is due to Novichkov et al. (J. Bacteriology 186, 6575-85), who rely on comparing phylogenetic distances within a gene family with genomic distances of the source organisms. Building on their approach, we introduce outlier detection in the correlation between those two sets of distances. This approach is designed to detect horizontal transfers of core set of genes present in many bacteria. The principle behind method allows detection of xenologous gene displacements as well as acquisition of novel genes.

Results: Simulations indicated that our method performs better than Novichkov et al's original approach. The approach very efficiently identified HGT between distantly related bacteria and also a limited number of gene transfers between closely related bacteria. In combination with sequence similarity and likelihood tests, it yields a measure robust enough to derive a set of 171 genes deemed likely to have been horizontally transferred. Further analysis of these 171 established horizontal transfer events gave interesting insights in the direction of transfer.

Conclusion: The majority of transfers between archaea and bacteria have occurred in the direction from bacteria to archaea rather than the other way round. Genes transferred between the archaea and bacteria are mostly metabolic genes. On the other hand, genes transferred within the bacterial phyla are mainly involved in translation.

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ROC curve analysis carried out on simulated data. As described in the text, false positive accumulation rate and true positive accumulation rate were calculated by varying the <CDISS> cut-off at regular intervals. The false positive and true positive rates at different <CDISS> cut-offs are plotted.
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Figure 2: ROC curve analysis carried out on simulated data. As described in the text, false positive accumulation rate and true positive accumulation rate were calculated by varying the <CDISS> cut-off at regular intervals. The false positive and true positive rates at different <CDISS> cut-offs are plotted.

Mentions: In order to assess the performance of CDISS in detecting HGT, we first carried out an in silico experiment. This simulation was performed on 8 COG families (COG1660, COG1666, COG1949, COG2844, COG3091, COG3852, COG4536, COG5007), which showed very high correlation (r >0.95 and p-val < 10-5) between rRNA and protein distances. The correlation as well as the visual inspection of the protein trees indicated that the phylogenies of these families were very close to the species trees as derived from 16S rRNA sequences. To simulate a HGT scenario for a COG family, we randomly selected an acceptor and a donor species. We replaced the protein-protein distances involving acceptor species with corresponding distances involving donor species. The procedure was carried out 100 times on each of the above 8 COGs. In each case, the <CDISS> value corresponding to the horizontally transferred sequence was noted. Figure 2 shows an ROC curve for this simulation at various cut-offs on <CDISS>. For further calculations, the <CDISS> cut-off corresponding to 5% false positives was chosen. At this cut-off, 90% of true positives could be detected. It should be noted that a realistic scenario, where the transferred gene can diverge due to various cellular processes such as amelioration, can be much more complicated and difficult to detect. On the other hand, the protein sequences, which passed this stringent cut-off, are very likely to have originated from another organism.


Horizontal Gene Transfers in prokaryotes show differential preferences for metabolic and translational genes.

Kanhere A, Vingron M - BMC Evol. Biol. (2009)

ROC curve analysis carried out on simulated data. As described in the text, false positive accumulation rate and true positive accumulation rate were calculated by varying the <CDISS> cut-off at regular intervals. The false positive and true positive rates at different <CDISS> cut-offs are plotted.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2651853&req=5

Figure 2: ROC curve analysis carried out on simulated data. As described in the text, false positive accumulation rate and true positive accumulation rate were calculated by varying the <CDISS> cut-off at regular intervals. The false positive and true positive rates at different <CDISS> cut-offs are plotted.
Mentions: In order to assess the performance of CDISS in detecting HGT, we first carried out an in silico experiment. This simulation was performed on 8 COG families (COG1660, COG1666, COG1949, COG2844, COG3091, COG3852, COG4536, COG5007), which showed very high correlation (r >0.95 and p-val < 10-5) between rRNA and protein distances. The correlation as well as the visual inspection of the protein trees indicated that the phylogenies of these families were very close to the species trees as derived from 16S rRNA sequences. To simulate a HGT scenario for a COG family, we randomly selected an acceptor and a donor species. We replaced the protein-protein distances involving acceptor species with corresponding distances involving donor species. The procedure was carried out 100 times on each of the above 8 COGs. In each case, the <CDISS> value corresponding to the horizontally transferred sequence was noted. Figure 2 shows an ROC curve for this simulation at various cut-offs on <CDISS>. For further calculations, the <CDISS> cut-off corresponding to 5% false positives was chosen. At this cut-off, 90% of true positives could be detected. It should be noted that a realistic scenario, where the transferred gene can diverge due to various cellular processes such as amelioration, can be much more complicated and difficult to detect. On the other hand, the protein sequences, which passed this stringent cut-off, are very likely to have originated from another organism.

Bottom Line: One successful approach to the detection of HGT events is due to Novichkov et al. (J.Genes transferred between the archaea and bacteria are mostly metabolic genes.On the other hand, genes transferred within the bacterial phyla are mainly involved in translation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany. a.kanhere@ucl.ac.uk

ABSTRACT

Background: Horizontal gene transfer (HGT) is an important process, which contributes in bacterial pathogenesis and drug resistance. A number of methods have been proposed for detection of horizontal gene transfer. One successful approach to the detection of HGT events is due to Novichkov et al. (J. Bacteriology 186, 6575-85), who rely on comparing phylogenetic distances within a gene family with genomic distances of the source organisms. Building on their approach, we introduce outlier detection in the correlation between those two sets of distances. This approach is designed to detect horizontal transfers of core set of genes present in many bacteria. The principle behind method allows detection of xenologous gene displacements as well as acquisition of novel genes.

Results: Simulations indicated that our method performs better than Novichkov et al's original approach. The approach very efficiently identified HGT between distantly related bacteria and also a limited number of gene transfers between closely related bacteria. In combination with sequence similarity and likelihood tests, it yields a measure robust enough to derive a set of 171 genes deemed likely to have been horizontally transferred. Further analysis of these 171 established horizontal transfer events gave interesting insights in the direction of transfer.

Conclusion: The majority of transfers between archaea and bacteria have occurred in the direction from bacteria to archaea rather than the other way round. Genes transferred between the archaea and bacteria are mostly metabolic genes. On the other hand, genes transferred within the bacterial phyla are mainly involved in translation.

Show MeSH
Related in: MedlinePlus