More effective drugs lead to harder selective sweeps in the evolution of drug resistance in HIV-1
- Stanford University, United States
- San Francisco State University, United States
Figures
Figure 1
Prediction of drug resistance acquisition with more and less effective treatments.
Among patients treated with more effective treatments (top), we predict HIV populations to have a lower probability of acquiring resistance per generation. As a result, the population must wait a …
Figure 2 with 2 supplements
Effect of DRMs on sequence diversity.
(A) For the most common reverse transcriptase and protease mutations, 95% confidence intervals are drawn for the difference in diversity associated with a single derived mutation. For each DRM, the …
Figure 2—figure supplement 1
Diversity and the number of drug resistance mutations by treatment categories.
The relationship between diversity and the number of drug resistance mutations is plotted separated out by the major treatment categories included in our analysis: (A) NRTI(s) without any other …
Figure 2—figure supplement 2
Effect of multiple DRMs on sequence diversity separated by subtype.
Average diversity level of sequences are plotted conditioned on number of fixed drug resistance mutations present separately by all the subtypes with more than 100 associated HIV populations. Means SE are plotted among all the D-PCR dataset.
Figure 3 with 2 supplements
Drug resistance mutations are correlated with diversity reduction differently in different types of treatments.
Treatment effectiveness from literature review (percentage of patients with virologic suppression after ~48 weeks) showed positive correspondence with clinical recommendation among RTI regimens (A) …
Figure 3—figure supplement 1
Drug resistance mutations are correlated with diversity reduction differently in different types of treatments when all years are included.
This figure is analogous with Figure 3 from the main text, but in this case, the data include 45 sequences sampled before 1995. The inclusion of these sequences substantially changes the effect of …
Figure 3—figure supplement 2
Drug resistance mutations are correlated with diversity reduction differently in different types of treatments with un-truncated data.
This figure is analogous with Figure 3 from the main text, but in this case, the data are not truncated to only include the patients with 4 or fewer DRMs. The figure caption is otherwise shared with …
Figure 4 with 2 supplements
Nonparametric test shows negative correlation between treatment effectiveness and .
Negative relationship between treatment effectiveness (percentage of patients with virologic suppression after ~48 weeks) and as fit within the GLMM among all treatments for which we have …
Figure 4—figure supplement 1
Nonparametric test shows negative correlation between treatment effectiveness and when 45 sequences from before 1995 are included.
This figure is analogous with Figure 4 from the main text, but in this case, 45 sequences before 1995 are included in the analysis. The coefficients are taken from the GLMM shown in Figure …
Figure 4—figure supplement 2
Nonparametric test shows negative correlation between treatment effectiveness and when sequences with any number of DRMs are included.
This figure is analogous with Figure 4 from the main text, but in this case, the data is not truncated to only include the patients with 4 or fewer DRMs. The coefficients are taken from the GLMM …
Figure 5 with 5 supplements
Summary of data subgroups and filtering.
Summary of all sequences from patients receiving first line therapy used throughout the analysis. The dataset is broken down into patients receiving protease inhibitor (PI) therapy with reverse …
Figure 5—figure supplement 1
Distribution of number of DRMs by treatment.
The distribution of the number of drug resistance mutations is plotted separated out by the major treatments included in our analysis. The y-axes are not standardized, and show the number of …
Figure 5—figure supplement 2
Data is heterogenous with respect to year in terms of the number of drugs per treatment, the diversity of treatments and the number of ambiguous reads per sequence.
(A) Percentage of patients receiving 1, 2 or 3 or more drugs (red, orange and blue bars, respectively) changes over the timecourse of our sampling period. (B) The cumulative number of unique …
Figure 5—figure supplement 3
-thinning controls for systematic changes in the called number of ambiguous reads over time A.
The mean number of ambiguous calls has increased over time among sequences with 0 DRMs. Linear model fits predicting the number of ambiguous reads from year among sequences with 0 DRMs are shown in …
Figure 5—figure supplement 4
The number of ambiguous reads is approximately distributed according to a negative binomial when p-thinned relative to 1989.
The subsampled number of ambiguous reads (relative to 1989) relative to the best fit Poisson distribution (A) and best fit negative binomial distribution (B). The fits to one particular subsample …
Figure 5—figure supplement 5
The number of ambiguous reads is approximately distributed according to a negative binomial when p-thinned relative to 1995.
The subsampled number of ambiguous reads (relative to 1995) relative to the best fit Poisson distribution (A) and best fit negative binomial distribution (B). The fits to one particular subsample …
Tables
Table 1
Model fits for the fixed effects from GLMMs fit to subsampled data. See Materials and methods: Quantifying the relationship between clinical effectiveness and diversity reduction for further …
| (Intercept) | (Number of DRMs) | (Length) | |
|---|---|---|---|
| 1995+, 4 DRMs | -0.82 | -0.12 | 0.0030 |
| (-0.94,-0.68) | (-0.13,-0.10) | (0.0028,0.0031) | |
| 1995+, All DRMs | -0.88 | -0.083 | 0.0030 |
| (-1.00,-0.76) | (-0.094,-0.073) | (0.0028,0.0031) | |
| 1989+, 4 DRMs | -1.33 | -0.12 | 0.0030 |
| (-1.50,-1.16) | (-0.13,-0.097) | (0.0028,0.0032) |
Table 2
Coefficients from GLMs fit to subsampled data. See Materials and methods: Quantifying the relationship between clinical effectiveness and diversity reduction for further explanations of coefficient …
| (Intercept) | (Length) | |||||
|---|---|---|---|---|---|---|
| 1995+, 4 DRMs | -0.51 | 0.0025 | -0.21 | -0.053 | -0.27 | 0.013 |
| (-0.62,-0.40) | (0.0024,0.0027) | (-0.23,-0.20) | (-0.070,-0.036) | (-0.36,-0.18) | (-0.009,0.035) | |
| 1995+, All DRMs | -0.66 | 0.0026 | -0.13 | -0.039 | -0.14 | 0.033 |
| (-0.77,-0.56) | (0.0025,0.0028) | (-0.14,-0.12) | (-0.053,-0.026) | (-0.21,-0.096) | (0.019,0.047) | |
| 1989+, 4 DRMs | -1.03 | 0.0026 | -0.22 | -0.047 | -0.27 | 0.016 |
| (-1.18,-0.88) | (0.0024,0.0028) | (-0.23,-0.20) | (-0.069,-0.025) | (-0.39,-0.17) | (-0.013,0.043) |
Additional files
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Supplementary file 1
Detailed description of treatment effectiveness computation including references.
(A) Detailed description (organized by study) of how we extracted treatment successes versus failures, as well as length of study and viral load limit for each study. Part (B) is a reorganization of (A), but it excludes specific details on how we calculated each number.
- https://doi.org/10.7554/eLife.10670.021
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Supplementary file 2
Detailed description of treatment effectiveness computation including references.
(B) Chart summary (organized alphabetically by treatment) detailing all studies considered for our treatment effectiveness analysis. For each study, we recorded the number of weeks, the virologic load limit under which was considered a ‘‘success,’’ the number of successes and the total number of patients on on-treatment analysis considered.
- https://doi.org/10.7554/eLife.10670.022
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Supplementary file 3
Part (C) is a further summary of the final treatment effectiveness used throughout our analysis.
This supplement has a full reference section describing the studies used.
- https://doi.org/10.7554/eLife.10670.023
