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Durand, Jonas

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Durand, Jonas
Affiliation principale
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https://libra.unine.ch/handle/123456789/616

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  • Publication
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    Genetic variation in transmission success of the Lyme borreliosis pathogen Borrelia afzelii
    (2015)
    Tonetti, Nicolas 
    ;
    Voordouw, Maarten 
    ;
    Durand, Jonas 
    ;
    Monnier, Séverine
    ;
    Gern, Lise 
    The vector-to-host and host-to-vector transmission steps are the two critical events that define the life cycle of any vector-borne pathogen. We expect negative genetic correlations between these two transmission phenotypes, if parasite genotypes specialized at invading the vector are less effective at infecting the vertebrate host and vice versa. We used the tick-borne bacterium Borrelia afzelii, a causative agent of Lyme borreliosis in Europe, to test whether genetic trade-offs exist between tick-to-host, systemic (host-to-tick), and a third mode of co-feeding (tick-to-tick) transmission. We worked with six strains of B. afzelii that were differentiated according to their ospC gene. We compared the three components of transmission among the B. afzelii strains using laboratory rodents as the vertebrate host and a laboratory colony of Ixodes ricinus as the tick vector. We used next generation matrix models to combine these transmission components into a single estimate of the reproductive number (R0) for each B. afzelii strain. We also tested whether these strain-specific estimates of R0 were correlated with the strain-specific frequencies in the field. We found significant genetic variation in the three transmission components among the B. afzelii strains. This is the first study to document genetic variation in co-feeding transmission for any tick-borne pathogen. We found no evidence of trade-offs as the three pairwise correlations of the transmission rates were all positive. The R0 values from our laboratory study explained 45% of the variation in the frequencies of the B. afzelii ospC strains in the field. Our study suggests that laboratory estimates of pathogen fitness can predict the distribution of pathogen strains in nature.