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. 2013 Oct;87(19):10805-15.
doi: 10.1128/JVI.01402-13. Epub 2013 Jul 31.

Effects of the number of genome segments on primary and systemic infections with a multipartite plant RNA virus

Jesús A Sánchez-Navarro  1 , Mark P Zwart, Santiago F Elena
Affiliations

Effects of the number of genome segments on primary and systemic infections with a multipartite plant RNA virus

Jesús A Sánchez-Navarro et al. J Virol. 2013 Oct.

Abstract

Multipartite plant viruses were discovered because of discrepancies between the observed dose response and predictions of the independent-action hypothesis (IAH) model. Theory suggests that the number of genome segments predicts the shape of the dose-response curve, but a rigorous test of this hypothesis has not been reported. Here, Alfalfa mosaic virus (AMV), a tripartite Alfamovirus, and transgenic Nicotianatabacum plants expressing no (wild type), one (P2), or two (P12) viral genome segments were used to test whether the number of genome segments necessary for infection predicts the dose response. The dose-response curve of wild-type plants was steep and congruent with the predicted kinetics of a multipartite virus, confirming previous results. Moreover, for P12 plants, the data support the IAH model, showing that the expression of virus genome segments by the host plant can modulate the infection kinetics of a tripartite virus to those of a monopartite virus. However, the different types of virus particles occurred at different frequencies, with a ratio of 116:45:1 (RNA1 to RNA2 to RNA3), which will affect infection kinetics and required analysis with a more comprehensive infection model. This analysis showed that each type of virus particle has a different probability of invading the host plant, at both the primary- and systemic-infection levels. While the number of genome segments affects the dose response, taking into consideration differences in the infection kinetics of the three types of AMV particles results in a better understanding of the infection process.

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Figures

Fig 1
Fig 1
Infection model predictions. In all of the panels, the log10-transformed dose is on the abscissa (where the dose is the sum of the doses of each type of virus particle) and the infection frequency is on the ordinate. In panel a, predictions of the infection model for a virus with one genome segment (k = 1; IAH) are shown, with infection probabilities decreasing from 3.3 × 10−3 to 1 × 10−3 to 3.3 × 10−4 to 1 × 10−4 for the curves from left to right (the grain of the dotted line becomes finer as the infection probability decreases). Note that changing the infection probability shifts the curve but does not alter its shape. Panels b and c show model predictions for viruses with two (k = 2) and three (k = 3) genome segments at equal frequencies for different particle types, respectively, and the same infection probabilities. Note that while the genome segment number alters the shape of the curve, changing the infection probability only changes its shape. Panel d shows model predictions for a tripartite virus in which each segment has an infection probability of 3.3 × 10−3 but one segment has a 10-fold higher frequency than the other two. The shape of the dose-response curve is then similar to that of a bipartite virus. Panel e shows model predictions for a tripartite virus in which each segment has an infection probability of 3.3 × 10−3 but one segment has a 10-fold lower frequency than the other two. The dose response is then similar to that of a monopartite virus. In panel f, the frequencies of the different particle types are the same but the probability of infection for the rare segment is 3.3 × 10−2. The dose-response curve is then as steep as possible for a tripartite virus in the absence of nonadditive interactions (k is equal to the actual number of genome segments or ω = 1), even though the particle frequencies are different.
Fig 2
Fig 2
Data and model predictions for different plant types. In all of the panels, the log10-transformed total dose of particles is on the abscissa and the frequency of infection is on the ordinate. The dose is the sum of the doses of all three particle types. Solid lines represent the predicted dose-response curves for infection of the inoculated host obtained by the best-supported model, i.e., DA for wild-type and P2 plants and IAH for P12 plants. The dotted lines represent the model prediction of the dose-response curves for systemic infection. Circles represent the data for infection of the inoculated leaf, and triangles represent the systemic infection data. Error bars indicate the 95% CIs. Note the steeper dose-response curves for wild-type and P2 plants and the decrease between the dose-response curves for the inoculated leaf and systemic tissue, from wild-type to P2 to P12 plants, at which point the two curves practically coincide. For parameter estimates, see Table 1.
Fig 3
Fig 3
Effects of genome segment number on dose response. In all of the panels, the log10-transformed dose is on the abscissa and the frequency of infection is on the ordinate. The dose is the sum of the doses of all of the particle types. Solid lines represent the predicted dose-response curves for wild-type plants, coarse dotted lines represent the predicted dose-response curves for P2 plants, and fine dotted lines represent the predicted dose-response curves for P12 plants. Circles are the data for wild-type plants, triangles are the data for P2 plants, and crosses are the data for P12 plants, with errors bars indicating the 95% CIs. Panel a shows the results for model 1 fitted to the inoculated leaf data, panel b shows the results of model 1 for systemic infection, panel c shows the results of model 2 for the inoculated leaf, and panel d shows the results of model 2 for systemic infection. Both models include the empirically determined frequencies of different particle types. Model 1 fits the data poorly, because the invasion probabilities of all of the particle types are the same. The most abundant particle type (RNA1) then, in fact, determines the infection kinetics, resulting in a response that is independent of the plant type. Model 2 allows each particle type to have a different infection probability, even thought these infection probabilities do not depend on the plant type (i.e., model 5), and fits the data much better. Model 3 (not shown) fits the data poorly. Models 4 and 5 (not shown) fit the data slightly better than model 2, but model selection indicates that the improvements in fit do not justify the additional free parameters added.
Fig 4
Fig 4
Effects of plant type on virus expansion in primary infection foci. Panel a gives the areas (mm2) of primary infection foci at 2 (white bars) and 3 (gray bars) dpi for the three plant types used. The error bars represent the 95% CIs. Primary infection foci at 3 dpi are shown for the wild-type (b and c), P2 (d), and P12 (e) plants. Besides the differences in size, the foci of the P2 and P12 plants have a higher intensity of fluorescence, suggesting that there are higher levels of infection.
Fig 5
Fig 5
Effects of plant type on systemic infection. All leaves from inoculated tobacco plants were sampled, and tissue-printing analysis was performed. To compare the effects of the plant type on systemic infection, the lowest dose with which the majority of inoculated plants were infected was considered, even though this dose was smaller for P12 than that for P2 plants and that for P2 plants was smaller than that for wild-type plants. The blot for each plant type is shown, and the dilution is shown to the right of the plant type. N numbers below the columns indicate the plant replicates, and the designations to the left of the rows indicate the leaves. ILa is the inoculated leaf (ground whole leaf), ILb is the inoculated leaf stem, and SL numbers represent the stems of systemic leaves, with SL1 being the leaf above the inoculated leaf. For P12 plants, the data for even higher dilutions are also presented to show that all of the leaves remained infected at all of the doses used (d and e).
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