Impact of HIV co-infection on the evolution and transmission of multidrug-resistant tuberculosis
- Norwegian Institute of Public Health, Norway
- University College London, United Kingdom
- ANLIS Carlos Malbrán, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
- Hospital Muñiz, Argentina
- Imperial College London, United Kingdom
Figures
Figure 1
Whole-genome Bayesian evolutionary phylogeny of the M outbreak.
The peripheral color strips indicate the HIV status of patients from which the clinical isolates were collected and the resistance burden of the isolate. The scale bar is given in years since the most recent common ancestor of the outbreak.
Figure 2 with 1 supplement
Impact of HIV co-infection on Mtb evolution.
Left: Rate of evolution (substitutions/site/year) on terminal branches (p = 0.1920). Right: resistance load (number of antimicrobials to which resistance-conferring mutations were found in clinical Mtb isolates, stratified by HIV status of the host.
Figure 2—figure supplement 1
Evolution of Mtb within patients as a function of HIV status.
From top to bottom: Rate of evolution (substitutions/site/year) (p=0.1920). Terminal branch lengths (p=0.0006). Number of SNPs on terminal branches (p=0.0009). *** denotes p<0.001.
Figure 3 with 3 supplements
Reconstruction of transmission events.
(A) Graphs representing two selected high-likelihood transmission chains. The colors of the edges indicate the probabilities of each transmission event from high (red) to lower (orange). Patient HIV-status is indicated by grey (negative) and blue (positive). (B) The corresponding transmission chains annotated in the timed phylogenetic tree. Red color highlights isolates linked by transmission events from a single source. Branches in magenta indicate subsequent transmission from a secondary case to additional cases (blue).
-
Figure 3—source data 1
Likelihood matrix of all possible pairwise transmission events.
- https://doi.org/10.7554/eLife.16644.009
-
Figure 3—source data 2
Conversion table linking transmission graph nodes and sample IDs.
- https://doi.org/10.7554/eLife.16644.010
Figure 3—figure supplement 1
Inferred transmission graph including all 251 transmission events (grey boxes HIV negative; blue HIV positive).Graph edges colored by likelihood from high (red) to low (yellow).
https://doi.org/10.7554/eLife.16644.011
Figure 3—figure supplement 2
Inferred transmission graph including only the most likely transmissions after applying various cut-offs (grey boxes HIV negative; blue HIV positive).Graph edges colored by likelihood from high (red) to low (yellow).
(A) Top 45% likely transmissions. (B) Top 35% likely transmissions. (C) Top 25% likely transmissions.
Figure 3—figure supplement 3
Top 25% likely transmission events mapped on the timed phylogeny.
Red coloring is used to highlight isolates linked by transmission events from a single source. Branches in magenta indicate isolates transmitted further from one of the secondary cases to other cases (blue).
Figure 4
Estimating latency time as a function of HIV status.
(A) For pairs of samples connected by a transmission event from i to j, transmission of Mtb is expected to have occurred on the terminal branch above j. Even though we do not know exactly when j went from latent TB to active TB, the latent period is included in the length of the terminal branch leading to j (see main text). We therefore use this branch length as an upwardly biased estimate for latency time. (B) For transmission pairs in the calculated transmission networks, the length (in years) of terminal branches leading to the recipient of the pairs (overestimated latency period) was extracted and stratified by HIV status of the recipient. To account for incomplete sampling, the analyses were performed on all 251 calculated transmission events as well as subsets including only the most likely transmission pairs (top 45, 35 and 25%). ***denotes p<0.001, *denotes p<0.05 as determined by unpaired t-test.
Figure 5 with 1 supplement
Correlations between global patterns of HIV, TB and MDR-TB prevalence.
Clockwise: Per country prevalence of MDR-TB as a function of TB prevalence (p=2.2 × 10−16); TB prevalence as a function of HIV prevalence (p=5.9 × 10−6); MDR-TB prevalence as a function of HIV prevalence (p=1.6 × 10−4); Proportion of MDR-TB cases among TB patients as a function of HIV prevalence (p=0.8). All values are log-transformed. The depth of shading of individual dots reflect the TB prevalence in individual countries.
-
Figure 5—source data 1
Global per-country health, economy and disease metrics.
- https://doi.org/10.7554/eLife.16644.018
Figure 5—figure supplement 1
Correlations between global patterns of HIV, TB and MDR-TB prevalence restricted to the top 50% countries in terms of GDP per capita.
Clockwise: Per country prevalence of MDR-TB as a function of TB prevalence (p=1.5 × 10−15); TB prevalence as a function of HIV prevalence (p=5.3 × 10−5); MDR-TB prevalence as a function of HIV prevalence (p=5.1 × 10−5); Proportion of MDR-TB cases among TB patients as a function of HIV prevalence (p=0.11). All values are log-transformed. The depth of shading of individual dots reflect the TB prevalence in individual countries.
Figure 6
Timed phylogeny used in simulation of SEIR model.
https://doi.org/10.7554/eLife.16644.020Tables
Table 1
Number of SNPs accumulated in clinical isolates.
Host HIV status | n | Mutations total | Mean number per isolate | χ2 p-value |
|---|---|---|---|---|
Negative | 99 | 262 | 2.646 | < 0.001 |
Positive | 153 | 277 | 1.810 |
Table 2
Identified events of within-patient acquired resistance.
Isolate ID | HIV | Treatment history | Mutation | Acquired resistance |
|---|---|---|---|---|
107 | - | follow-up (ETH* treated) | ethA L225fs | ETH |
108 | - | follow-up (ETH and FLQ treated) | ethA S208P | ETH |
516 | - | follow-up (unknown treatment) | pncA D129G | PZA |
1757 | - | follow-up (ETH and FLQ treated) | ethA H22P | ETH |
2098 | - | follow-up (ETH and FLQ treated) | ethA F302S + gyrB D461V | ETH + FLQ |
2485 | - | follow-up (unknown treatment) | ethA G437fs | ETH |
POGU | - | follow-up (ETH and FLQ treated) | ethA R259fs + gyrB R292G | ETH + FLQ |
110 | + | follow-up (ETH and FLQ treated) | gyrB R446S | FLQ |
257 | + | follow-up (ETH and FLQ treated) | inhA -15 C>T | ETH |
1298 | + | follow-up (ETH and FLQ treated) | gyrA D94N | FLQ |
2569 | + | follow-up (ETH treated) | ethA S251fs | ETH |
-
*Patient received the ETH analogue prothionamide
Table 3
Number of reconstructed transmission events.
Transmission event cut-off | Donor HIV status | Observed | Expected | Obs/Exp | χ2 p value |
|---|---|---|---|---|---|
All transmissions | Negative | 80 | 98.61 | 0.81 | 0.3185 |
Positive | 171 | 152.39 | 1.12 | ||
Top 25% events | Negative | 20 | 24.75 | 0.81 | 0.2205 |
Positive | 43 | 38.25 | 1.12 | ||
Top 35% events | Negative | 30 | 34.57 | 0.87 | 0.3185 |
Positive | 58 | 53.43 | 1.09 | ||
Top 45% events | Negative | 36 | 44.39 | 0.81 | 0.1060 |
Positive | 77 | 68.61 | 1.12 |
Table 4
Distribution of transmissions as a function of HIV status of transmitter.
All transmission events | |||||||||||||
Transmissions per transmitter: | Kruskal-Wallis p value | ||||||||||||
HIV status | none | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|
neg | 50 | 37 | 4 | 2 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0.075 |
pos | 63 | 58 | 11 | 10 | 5 | 2 | 2 | 1 | 0 | 0 | 0 | 1 | |
Top 25% likely transmission events | |||||||||||||
Transmissions per transmitter: |
| ||||||||||||
HIV status | none | 1 | 2 | 3 | 4 | 5 |
| ||||||
neg | 83 | 15 | 0 | 0 | 0 | 1 | 0.304 | ||||||
pos | 121 | 25 | 3 | 4 | 0 | 0 | |||||||
Top 35% likely transmission events | |||||||||||||
Transmissions per transmitter: |
| ||||||||||||
HIV status | none | 1 | 2 | 3 | 4 | 5 |
| ||||||
neg | 75 | 21 | 2 | 0 | 0 | 1 | 0.505 | ||||||
pos | 111 | 33 | 4 | 4 | 0 | 1 | |||||||
Top 45% likely transmission events | |||||||||||||
Transmissions per transmitter: |
| ||||||||||||
HIV status | none | 1 | 2 | 3 | 4 | 5 |
| ||||||
neg | 69 | 27 | 2 | 0 | 0 | 1 | 0.324 | ||||||
pos | 100 | 39 | 7 | 5 | 1 | 1 | |||||||
Additional files
-
Supplementary file 1
M. tuberculosis sample information.
- https://doi.org/10.7554/eLife.16644.021
-
Supplementary file 2
Patient treatment histories.
- https://doi.org/10.7554/eLife.16644.022
-
Supplementary file 3
SNP distances and transmission reconstruction results for samples pairs with known epidemiological link.
- https://doi.org/10.7554/eLife.16644.023
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Impact of HIV co-infection on the evolution and transmission of multidrug-resistant tuberculosis
eLife 5:e16644.
https://doi.org/10.7554/eLife.16644
