Identifying the important HIV-1 recombination breakpoints

Abstract

Recombinant HIV-1 genomes contribute significantly to the diversity of variants within the HIV/AIDS pandemic. It is assumed that some of these mosaic genomes may have novel properties that have led to their prevalence, particularly in the case of the circulating recombinant forms (CRFs). In regions of the HIV-1 genome where recombination has a tendency to convey a selective advantage to the virus, we predict that the distribution of breakpoints--the identifiable boundaries that delimit the mosaic structure--will deviate from the underlying null distribution. To test this hypothesis, we generate a probabilistic model of HIV-1 copy-choice recombination and compare the predicted breakpoint distribution to the distribution from the HIV/AIDS pandemic. Across much of the HIV-1 genome, we find that the observed frequencies of inter-subtype recombination are predicted accurately by our model. This observation strongly indicates that in these regions a probabilistic model, dependent on local sequence identity, is sufficient to explain breakpoint locations. In regions where there is a significant over- (either side of the env gene) or under- (short regions within gag, pol, and most of env) representation of breakpoints, we infer natural selection to be influencing the recombination pattern. The paucity of recombination breakpoints within most of the envelope gene indicates that recombinants generated in this region are less likely to be successful. The breakpoints at a higher frequency than predicted by our model are approximately at either side of env, indicating increased selection for these recombinants as a consequence of this region, or at least part of it, having a tendency to be recombined as an entire unit. Our findings thus provide the first clear indication of the existence of a specific portion of the genome that deviates from a probabilistic null model for recombination. This suggests that, despite the wide diversity of recombinant forms seen in the viral population, only a minority of recombination events appear to be of significance to the evolution of HIV-1.

Figure 1. Model of HIV copy-choice recombination.

The reverse transcriptase (RT) complex is shown moving from the 3′ end of the donor RNA to the 5′ end. The RNAse activity of RT is indicated by the light grey nucleotides on the donor RNA. The nascent negative DNA strand can be observed to the right of the RT complex. A potential strand-transfer event by RT is indicated by the dashed arrow. The dashed boxes indicate windows of decreased probability of crossover that have been anchored to the 5′ side of each mismatch. The probability of a crossover occurring at each base on the acceptor strand is indicated by p ₁, p ₂, or p ₃ as described in Methods. The plot along the bottom is a representation of each of the probability values across the sequence. For this stretch of 16 nucleotides, the total probability of a crossover occurring is given by the equation shown.

Figure 2. The significance of local sequence identity to recombination.

The main plot displays the normalized distribution of in vitro breakpoints falling within zones ranging from size 1 to 25 (vertical grey bars); see Baird et al. for further details. The horizontal lines indicate the expected random distribution of breakpoints for the zones. The inset plot shows the normalised frequency of both the in vitro breakpoints and randomly generated breakpoints for zones up to size 25 (arranged in groups of five). On the main plot, error bars on the random distributions (vertical lines) represent one standard error to include 68.3% of the distribution. On the inset, the error bars on the random distributions represent 1.96×standard error to include 95% of the distribution.

Figure 3. Testing of the recombination models.

The probabilistic distribution of breakpoints as predicted by each model (horizontal lines) compared to the in vitro distribution of breakpoints (vertical grey bars). The three panels correspond to predicted breakpoint distributions that ignored sequence identity (A), prohibited a breakpoint on a mismatch (B) and the full model (C); see Methods for further details. The error bars (vertical lines) on the predicted values represent 1.96×standard error to include 95% of the distribution.

Figure 4. Implementation of the recombination model.

Comparison of model-predicted breakpoints (horizontal lines) with breakpoint locations from HIV-1 recombinants (vertical bars) from the HIV Sequence Database. White bars indicate where the number of breakpoints for the global data is significantly higher than the prediction for the region, light grey bars indicate where the global data falls within the prediction, while dark grey indicates where the global data is significantly lower than the model prediction. The error bars on the model-predicted values represent 1.645×standard error to include 90% of the distribution. The normalised frequency data (y-axis) have been divided into bins of size 400 nucleotides (x-axis). Below the x-axis, the various genomic regions of the HIV-1 genome are displayed. Note, positioning of genes is relative to a gap-stripped sequence alignment.