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A complete map of potential pathogenicity markers of avian influenza virus subtype H5 predicted from 11 expressed proteins.

Khaliq Z, Leijon M, Belák S, Komorowski J - BMC Microbiol. (2015)

Bottom Line: We found potential markers of pathogenicity in all of the 11 proteins expressed by the H5 type of AIV.Our results suggest that the low pathogenicity is common to most of the subtypes of the H5 AIV while the high pathogenicity is specific to each subtype.The models were developed using public data and validated on new, unseen sequences.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell and Molecular Biology, Computational and Systems Biology, Science for Life Laboratory, Uppsala University, SE-751 24, Uppsala, Sweden. zeeshan.khaliq@icm.uu.se.

ABSTRACT

Background: Polybasic cleavage sites of the hemagglutinin (HA) proteins are considered to be the most important determinants indicating virulence of the avian influenza viruses (AIV). However, evidence is accumulating that these sites alone are not sufficient to establish high pathogenicity. There need to exist other sites located on the HA protein outside the cleavage site or on the other proteins expressed by AIV that contribute to the pathogenicity.

Results: We employed rule-based computational modeling to construct a map, with high statistical significance, of amino acid (AA) residues associated to pathogenicity in 11 proteins of the H5 type viruses. We found potential markers of pathogenicity in all of the 11 proteins expressed by the H5 type of AIV. AA mutations S-43(HA1)-D, D-83(HA1)-A in HA; S-269-D, E-41-H in NA; S-48-N, K-212-N in NS1; V-166-A in M1; G-14-E in M2; K-77-R, S-377-N in NP; and Q-48-P in PB1-F2 were identified as having a potential to shift the pathogenicity from low to high. Our results suggest that the low pathogenicity is common to most of the subtypes of the H5 AIV while the high pathogenicity is specific to each subtype. The models were developed using public data and validated on new, unseen sequences.

Conclusions: Our models explicitly define a viral genetic background required for the virus to be highly pathogenic and thus confirm the hypothesis of the presence of pathogenicity markers beyond the cleavage site.

No MeSH data available.


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Schematic representation of the applied computational modeling methodology
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Fig1: Schematic representation of the applied computational modeling methodology

Mentions: We used publicly available protein sequence data for all the proteins of H5 subtype of avian influenza viruses (H5-AIV) [15]. Pathogenicity is very strongly linked to the amino acid sequence of the cleavage site for naturally occurring viruses [16] and thus aligned sequences of the proteins were annotated with the pathogenicity value (high or low) using the presence or absence of insertions in the cleavage site of the corresponding HA protein, respectively. The cleavage site was subsequently removed from the HA protein sequences since we had already used this information to label the sequences. This enables learning other AA positions of the sequences that may be related to the pathogenicity label. Ranking of the pathogenicity significant AA positions for each of the proteins was done with Monte Carlo Feature Selection (MCFS) [17]. Rough set theory [18] as implemented by ROSETTA [19] was applied in constructing rule-based models of pathogenicity using the significant positions. Such models are expressed as IF-THEN rules. See Fig. 1 for a schematic description of the method. The rules explicitly specified AA’s and their combinations that were associated with the pathogenicity of the H5 subtype. In addition to already known markers of pathogenicity, we discovered other potential AA positions and mutations that may affect the pathogenicity of H5-AIV. The models were experimentally validated on new, unseen sequences released after we built our models. Similar approaches to modeling that we used here have been successfully applied to model many aspects of protein or gene features such as, for instance, cleavability of octamer peptides by HIV-1 protease [20], drug resistance [21], binding affinities [22] and participation in biological processes [23].Fig. 1


A complete map of potential pathogenicity markers of avian influenza virus subtype H5 predicted from 11 expressed proteins.

Khaliq Z, Leijon M, Belák S, Komorowski J - BMC Microbiol. (2015)

Schematic representation of the applied computational modeling methodology
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4482282&req=5

Fig1: Schematic representation of the applied computational modeling methodology
Mentions: We used publicly available protein sequence data for all the proteins of H5 subtype of avian influenza viruses (H5-AIV) [15]. Pathogenicity is very strongly linked to the amino acid sequence of the cleavage site for naturally occurring viruses [16] and thus aligned sequences of the proteins were annotated with the pathogenicity value (high or low) using the presence or absence of insertions in the cleavage site of the corresponding HA protein, respectively. The cleavage site was subsequently removed from the HA protein sequences since we had already used this information to label the sequences. This enables learning other AA positions of the sequences that may be related to the pathogenicity label. Ranking of the pathogenicity significant AA positions for each of the proteins was done with Monte Carlo Feature Selection (MCFS) [17]. Rough set theory [18] as implemented by ROSETTA [19] was applied in constructing rule-based models of pathogenicity using the significant positions. Such models are expressed as IF-THEN rules. See Fig. 1 for a schematic description of the method. The rules explicitly specified AA’s and their combinations that were associated with the pathogenicity of the H5 subtype. In addition to already known markers of pathogenicity, we discovered other potential AA positions and mutations that may affect the pathogenicity of H5-AIV. The models were experimentally validated on new, unseen sequences released after we built our models. Similar approaches to modeling that we used here have been successfully applied to model many aspects of protein or gene features such as, for instance, cleavability of octamer peptides by HIV-1 protease [20], drug resistance [21], binding affinities [22] and participation in biological processes [23].Fig. 1

Bottom Line: We found potential markers of pathogenicity in all of the 11 proteins expressed by the H5 type of AIV.Our results suggest that the low pathogenicity is common to most of the subtypes of the H5 AIV while the high pathogenicity is specific to each subtype.The models were developed using public data and validated on new, unseen sequences.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell and Molecular Biology, Computational and Systems Biology, Science for Life Laboratory, Uppsala University, SE-751 24, Uppsala, Sweden. zeeshan.khaliq@icm.uu.se.

ABSTRACT

Background: Polybasic cleavage sites of the hemagglutinin (HA) proteins are considered to be the most important determinants indicating virulence of the avian influenza viruses (AIV). However, evidence is accumulating that these sites alone are not sufficient to establish high pathogenicity. There need to exist other sites located on the HA protein outside the cleavage site or on the other proteins expressed by AIV that contribute to the pathogenicity.

Results: We employed rule-based computational modeling to construct a map, with high statistical significance, of amino acid (AA) residues associated to pathogenicity in 11 proteins of the H5 type viruses. We found potential markers of pathogenicity in all of the 11 proteins expressed by the H5 type of AIV. AA mutations S-43(HA1)-D, D-83(HA1)-A in HA; S-269-D, E-41-H in NA; S-48-N, K-212-N in NS1; V-166-A in M1; G-14-E in M2; K-77-R, S-377-N in NP; and Q-48-P in PB1-F2 were identified as having a potential to shift the pathogenicity from low to high. Our results suggest that the low pathogenicity is common to most of the subtypes of the H5 AIV while the high pathogenicity is specific to each subtype. The models were developed using public data and validated on new, unseen sequences.

Conclusions: Our models explicitly define a viral genetic background required for the virus to be highly pathogenic and thus confirm the hypothesis of the presence of pathogenicity markers beyond the cleavage site.

No MeSH data available.


Related in: MedlinePlus