Advances in understanding the genetic basis of antimalarial drug resistance

Abstract

The acquisition of drug resistance by Plasmodium falciparum has severely curtailed global efforts to control malaria. Our ability to define resistance has been greatly enhanced by recent advances in Plasmodium genetics and genomics. Sequencing and microarray studies have identified thousands of polymorphisms in the P. falciparum genome, and linkage disequilibrium analyses have exploited these to rapidly identify known and novel loci that influence parasite susceptibility to antimalarials such as chloroquine, quinine, and sulfadoxine-pyrimethamine. Genetic approaches have also been designed to predict determinants of in vivo resistance to more recent first-line antimalarials such as the artemisinins. Transfection methodologies have defined the role of determinants including pfcrt, pfmdr1, and dhfr. This knowledge can be leveraged to develop more efficient methods of surveillance and treatment.

Figure 1. Identification of a selective sweep of mutant dhfr conferring pyrimethamine resistance from Asia to Africa

(a) Genotype data are shown for 12 Thai isolates with dhfr alleles that harbor 2–4 resistance mutations, 24 African isolates with triple-mutant alleles, and 18 African parasites with sensitive dhfr alleles. The four-letter codes designate amino acids* present at positions 51, 59, 108, and 164 in the predicted DHFR protein. Amino acids conferring resistance are underlined, and dhfr alleles are shaded yellow, orange, red, and black in order of increasing resistance. The sensitive allele is shaded blue. Fragment lengths are shown for eight microsatellites positioned at –0.1, –4.4, –5.3, –10, and –20 kb upstream and +0.5, +6, +10 kb and +20 kb downstream of dhfr. Dots and yellow shading indicate microsatellite sizes that are identical to the predominant resistant haplotype (shown on rightmost column). Figure reproduced from Roper et al. [9], reprinted with permission from AAAS. (b) Selective sweep of dhfr triple mutants. Resistance to pyrimethamine originated and spread in Southeast Asia in the early 1970s. Triple mutant resistant parasites arrived circa 1978 in Africa, by unknown routes, and spread in a selective sweep. Data from McCollum et al. [10] suggest an independent origin for South American dhfr resistance alleles. Orange shading denotes malaria endemic regions. Stars represent approximate origins of identified pyrimethamine resistance sweeps. Figure adapted from Tim Anderson with the author’s kind permission. *Single-letter abbreviations are: C, Cys; I, Ile; L, Leu; N, Asn; R, Arg; S, Ser.

Figure 2. Identification of a genetic locus of variable copy number, postulated to alter parasite susceptibility to SP

This heat map, generated from a high-density microarray analysis, shows the base 2 logarithm of the ratio of the normalized, background-subtracted probe signal for the listed lines (Sg18 and HB3), relative to the sequenced line 3D7 [15]. All probes on the array were selected to be unique within 3D7. Each horizontal bar represents a single gene on the right arm of chromosome 12. Dark probes, with low signal intensities, do not match any region in the genome of the listed line, while light probes contain matches to multiple regions of the genome. Based on comparisons of multiple clones, the authors detected heterogeneity in probe signal intensities at and around the GTP-cyclohydrolase gene (PFL1155w), indicated by the arrow. Further investigations demonstrated that the 3D7 reference line for the microarray had multiple copies of PFL1155w. Thus some lines, such as Sg18, demonstrate a reduced hybridization signal (dark probes) at this locus relative to 3D7. Others, such as HB3, demonstrate neutral or even increased hybridization signals, implying that they harbor amplifications of the PFL1155w gene. Amplification of DNA on either side of PFL1155w is also apparent in HB3 compared to 3D7. The authors postulate that the PFL1155w amplification may affect SP sensitivity. Figure reproduced from Kidgell et al. [15] with kind permission from Elizabeth Winzeler and PLoS Pathogens.

Figure 3. Identification of multiple loci associated with quinine resistance using QTL mapping

Ferdig et al. [16] mapped the QTL associated with quinine resistance using 35 independent progeny from the cross of quinine low level resistant and sensitive clones. Markers from linkage groups on each of the 14 P. falciparum chromosomes are distributed on the horizontal axis. Log of Difference scores are plotted on the vertical axis as a function of genome location. Horizontal dashed lines indicate threshold values from 1,000 permutations. Peaks at chromosomes 7 and 13 (colocalizing with pfcrt and pfnhe1 respectively) indicate QTL associated with elevated quinine 90% inhibitory concentrations. The peak at chromosome 5 (colocalizing with pfmdr1 and shown as a dashed line) was identified in a secondary scan after removing the effects from the major QTL defined by pfcrt and pfnhe1. Adapted from [16] with kind permission from Michael Ferdig.

Figure 4. Selection of artemisinin-resistant P. chabaudi and the LGS approach to identifying an in vivo determinant of resistance

a) Artemisinin-sensitive P. chabaudi parasites of the AS lineage were exposed to increasing concentrations of artemisinin (ART) or artesunate (ATN) over 14 passages in the mouse. This produced the AS-ART and AS-ATN resistant lines that were respectively 15-fold and 6-fold more resistant to their selecting agents compared to the parental AS line, displayed cross-resistance, and were genetically stable [23]. The AS-ART clone was then crossed with AJ and the progeny subjected to LGS. Ongoing studies associate resistance with a locus on chromosome 2 [27]. (b) Simulated results of a LGS analysis. Resistant and sensitive parasite clones are crossed and the progeny are subjected to a specific selection pressure. The relative intensities of quantitative markers of the sensitive parental line compared to the drug-pressured line are plotted against the genetic distance of each marker along a parasite chromosome (data points and line of best fit are represented as purple diamonds and red line). Data are plotted and analyzed for every chromosome. This graph illustrates a “selection valley” that has formed in a region spanning about 100 centiMorgans of genetic distance. Markers at the lowest point in the selection valley are predicted to be closest to the gene that determines resistance to the applied selection pressure. Backcrossing a selected progeny with the sensitive parent allows LGS to be repeated iteratively, thereby producing a steeper selection valley (green dashed line). Figure 4b was reproduced from [25], copyright Elsevier Press, with kind permission from Richard Carter.