Within-host competition and evolution of drug resistance in Plasmodium falciparum Öffentlichkeit
Bushman, Mary (Fall 2017)
Published
Abstract
The focus of this dissertation is the role of within-host competition in the evolution of drug resistance in the malaria parasite Plasmodium falciparum. In high-transmission settings, such as those in sub-Saharan Africa, most P. falciparum infections contain multiple genetically distinct strains. If drug-sensitive and drug-resistant strains both exist in a population, then mixed-strain infections may contain both, with the potential for competition between sensitive and resistant parasites. Previous studies have found evidence for such competition in a rodent malaria parasite, and mathematical models have suggested that within-host competition could have a significant effect on the rate at which resistance evolves. However, evidence for within-host competition in P. falciparum is scarce, and theoretical models have generated conflicting predictions regarding the impact of within-host competition on the spread of resistance. I used samples from naturally occuring (human) infections to look for empirical evidence of within-host competition in P. falciparum. In samples from Angola, Ghana, and Tanzania, I used a molecular marker of chloroquine resistance to quantify sensitive and resistant parasites, and found strong empirical support for within-host competition in P. falciparum. In addition, I used deep sequencing of samples from a longitudinal study in Kenya to test for so-called “competitive release” – an expansion of drug-resistant parasites in the host following the removal of drug-sensitive parasites by antimalarial drug treatment, and a potentially important facilitator of the spread of resistance. The preliminary results from this ongoing study are consistent with competitive release of resistant parasites. Finally, I used a nested model – a model of within-host infection dynamics embedded into a second model of transmission between humans and mosquitoes – to explore the impact of within-host competition on the spread of resistance under a variety of conditions. The results suggest that within-host competition serves to inhibit the emergence of resistance in high-transmission settings; however, once resistance is established in the population, its spread in high-transmission settings may be paradoxically accelerated by competitive release. These results are consistent with, and advance our understanding of, global patterns of drug resistance evolution in P. falciparum.
Table of Contents
Chapter 1: Introduction
1.1 Overview
1.2 Within-host competition in Plasmodium falciparum
1.3 Competitive release of drug-resistant parasites following treatment
Figure 1.1 Effect of competitive release on frequency of resistance
1.4 Transmission intensity, within-host dynamics, and the evolution of drug resistance
Figure 1.2 Schematic representation of factors linking transmission intensity and drug resistance
Chapter 2: Within-host competition in Plasmodium falciparum
2.1 Introduction
2.2 Methods
Focus on chloroquine resistance
Sample collection and processing
Quantification of drug-sensitive and drug-resistant parasites
Neutral microsatellite genotyping and analysis
Statistical analysis
2.3 Results
Variation in PfCRT genotype frequencies
Overall parasite densities in single- and mixed-genotype infections
Figure 2.1 Total parasite densities of single- and mixed-genotype infections
Figure 2.2 Effects of MOI and host age on total parasite density
Within-host competition
Figure 2.3 Densities of CQ-sensitive and CQ-resistant parasites in single- and mixed-genotype infections
Fitness cost of resistance in mixed-genotype infections
Figure 2.4 Proportions of CQ-resistant parasites in mixed-genotype infections
Temporal dynamics of resistance in Ghana
Figure 2.5 Changes in prevalence of CQ-resistant allele at four sites in Ghana
2.4 Discussion
Chapter 3: Competitive release of drug-resistant parasites following treatment
3.1 Introduction
3.2 Methods
Parasite quantification using PET-PCR
Targeted amplicon deep sequencing (TADS) of dhps and dhfr
Statistical analysis
3.3 Results
Table 3.1 Observed variants linked to SP resistance
Figure 3.1 Within-host frequency distributions of SP resistance markers
Figure 3.2 Changes in sensitive and resistant alleles for all loci
Figure 3.3 Changes in sensitive and resistant alleles for individual loci
Figure 3.4 Changes in sensitive and resistant alleles for all loci, stratified by time between samples
Figure 3.5 Parasite densities stratified by time between samples and treatment status
Figure 3.6 Initial parasite densities for treated and control sets
3.4 Discussion
Chapter 4: Transmission intensity, within-host dynamics, and the evolution of drug resistance
4.1 Introduction
Figure 4.1 Within-host dynamics link transmission intensity and drug resistance
4.2 Methods
Figure 4.2 Overall structure of the full nested model
Within-host model
Table 4.1 Parameter definitions for the within-host model
Figure 4.3 Compartment-style schematic of the within-host model
Between-host model
Parasite population structure
Antimalarial drug treatment
Simulations
4.3 Results
Within-host dynamics
Figure 4.4 Dynamics of parasites and immunity in a naïve host
Figure 4.5 Within-host dynamics of mixed infections
Epidemiological patterns
Figure 4.6 Equilibrium infection prevalence
Figure 4.7 Frequency of mixed-strain infections
Figure 4.8 Parasite density vs. age
Figure 4.9 Fraction of infections above selected thresholds
Figure 4.10 Parasite densities in single- and mixed-strain infections
Simulation results
Figure 4.11 Simulations with no antimalarial drug use
Figure 4.12 Simulations with treatment of symptomatic infections
Figure 4.13 Simulations with unrestricted treatment
4.4 Discussion
Chapter 5: Discussion
5.1 Discussion of Chapter 2: Within-host competition in Plasmodium falciparum
5.2 Discussion of Chapter 3: Competitive release of drug-resistant parasites following treatment
5.3 Discussion of Chapter 4: Transmission intensity, within-host dynamics, and the evolution of drug resistance
Chapter 6: Appendix
6.1 Appendix to Chapter 2: Within-host competition in Plasmodium falciparum
Methods: Quantitative real-time PCR
Figures
Figure A1. PfCRT genotypes of samples from Angola, Ghana, and Tanzania, superimposed on map showing P. falciparum endemicity
Figure A2. MOI distribution stratified by host age
Figure A3. Fraction CQR (observed) vs. fraction CQR (actual)
Figure A4. Total parasite density: observed values vs. true values
Tables
Table A1. Study locations and sample sizes
Table A2. Primer and probe sequences used in PfCRT qPCR
Table A3. PfCRT qPCR master mix and thermal cycling protocol
Table A4. Primer sequences for microsatellite loci
Table A5. PCR master mixes and thermal cycling protocols for amplifying neutral microsatellites
Table A6. MOI distributions for Angola and Tanzania
Table A7. Infections positive for CQS and/or CQR alleles in Angola, Ghana, and Tanzania
Structure and implementation of null model of mixed-genotype infections
6.2 Appendix to Chapter 3: Competitive release of drug-resistant parasites following treatment
Table A8. PCR master mix for amplification of dhps and dhfr
Table A9. Primers used for whole-gene amplification of dhps and dhfr
Table A10. PCR thermal cycling protocol for amplification of dhps and dhfr
Figure A5. Changes in resistant and sensitive alleles for individual loci, stratified by time between samples
6.3 Appendix to Chapter 4: Transmission intensity, within-host dynamics, and the evolution of drug resistance
Methods
Within-host model
Figure A6. Growth rate of adaptive immunity as a function of merozoite density
Figure A7. “Decay” rate of adaptive immunity as a function of antigenic variants encountered
Figure A8. Number of “tries” to find a novel variant as repertoire is exhausted
Parasite population structure and acquired immunity
Figure A9. The four parasite population structure configurations used
Human-mosquito contact and parasite transmission
Populations and turnover
Treatment
Model parameters
Table A11. Default model parameters
Simulation parameters
Figure A10. Graphic of parameter combinations used for simulations
Table A12. All parameter combinations used for simulations
Model output (all simulations)
Model code
References
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