Within-host competition and evolution of drug resistance in Plasmodium falciparum Open Access

Bushman, Mary (Fall 2017)

Permanent URL: https://etd.library.emory.edu/concern/etds/9p290934t?locale=en
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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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