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Distribution of scores for LacI protein mutants

Delta-bitscore

De

nsi

ty

FunctionalLoss of function

MethodA profile-based approach to measuring the significance of genetic variation

ResultsIdentification of several key genes that differentiate between pathogens and non-pathogens

Profile HMMs can be used to score sequence variation according to how likely it is to affect the functioning of a gene. The models capture information on the frequency of each amino acid at each position in the protein, as well as the frequency of indels. We can compare sequences to the models to get a score that indicates the quality of the match to the model.

The Pseudomonas genus of bacteria contains members capable of infecting a wide range of plants, including Psa, which had a dramatic impact on New Zealand's kiwifruit industry following an outbreak in 2010. However, the genus also contains members that live associated with, or in the presence of plants but don't cause disease.

We examined 27 Pseudomonas isolates from a variety of species in order to determine whether there were genes that showed significantly different functional potential in pathogenic isolates compared to non-pathogenic isolates.

IntroductionSearching for genetic variation indicative of pathogenicity in Pseudomonas genomes

This score can be used to differentiate functionally significant mutations from functionally neutral variation. The figure on the right shows the ability of the method to separate neutral variation and deleterious mutations in the E. coli LacI protein based on score.

AcknowledgementsPG funded by Rutherford Fellowship NW supported by UC PhD Scholarship

Profile-based comparison of Pseudomonas genomes reveals signatures of pathogenicityNicole E. Wheeler1, Honour McCann2, Paul P. Gardner1

1 School of Biological Sciences, University of Canterbury, Christchurch.2 New Zealand Imstitute for Advanced Study, Massey University, Auckland.

Using the delta-bitscore approach, we were able to identify three genes that showed significantly different score distributions in the non-pathogenic isolates compared to the pathogenic isolates. We were able to identify a number of genes that offer promising discriminatory power on their own, and when combined can perfectly discriminate between pathogenic and non-pathogenic isolates, given the sampling we looked at.

Using this scoring method, we identified genes shared by Pseudomonas isolates in our test group, that showed a significantly different score distribution in pathogens compared to non-pathogens.

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Score distributions for4−aminobutyrate aminotransferase

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Score distributions forouter membrane efflux protein

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Score distributions forbacterioferritin

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Potential application: Using delta-bitscore for the classification of organisms

Gene AScore > 350?

Gene BScore > 300?

Gene CScore > 115?

Gene BScore > 300?

Gene CScore > 115?

Gene CScore > 115?

Gene CScore > 115?

P

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P

NP

= pathogen

= non-pathogenP P PNPNPNPNP

For more information on the method:

Species 1

Species 2

S1 - S2 = delta-bitscore

Image: Eric Narwocki

Profile HMM

= score 1 (S1)

= score 2 (S2)

Image: thekiwifruitclaim.org