Stepwise acquisition of pyrimethamine resistance in the malaria parasite

Results

By means of site-directed mutagenesis of a bacterial plasmid containing the coding sequence of P. falciparum DHFR, we created all 16 possible combinations of the mutants Asn51Ile (N51I), Cys59Arg (C59R), Ser108Asn (S108N), and Ile164Leu (I164L). The sequences of the mutagenized DHFR genes were confirmed by direct sequencing.

Drug-Resistance Assays.

Assays for pyrimethamine resistance were carried out by means of a bacterial complementation system (17). In this system, the endogenous bacterial enzyme was chemically inhibited by 5 μM trimethoprim, a concentration sufficient to prevent bacterial growth. The P. falcarium DHFR enzyme is insensitive to trimethoprim and thus can complement the inhibited bacterial enzyme. Therefore, the bacterial system requires sufficient DHFR enzyme activity to allow the cells to grow, which in the presence of pyrimethamine represents an adaptive conflict between drug resistance and catalytic activity.

The level of pyrimethamine resistance was estimated as the concentration of drug that inhibited cell growth by 50%, a metric referred to as IC₅₀. Bacterial strains carrying each of the DHFR alleles were assayed across a range of pyrimethamine concentrations, and growth rate as a function of pyrimethamine concentration was estimated by polynomial regression. To determine the empirical distribution of IC₅₀ estimates, the IC₅₀ value for each of the 16 DHFR alleles was estimated in 4–8 independent biological replicates, each with 4 technical replicates.

The results are shown in a bar graph in Fig. 1 and in tabular form in Table S1. The DHFR alleles indicated along the horizontal axis are given in the form of a vector of 0's and 1's corresponding, from left-to-right, to residues 51, 59, 108, and 164. Each 0 indicates a nonmutant codon, and each 1 indicates a mutant codon. Thus, 0000 represents the nonmutant sequence NCSI, and 1111 represents the quadruple mutant IRNL. The error bars are equal to the SD of each of the distributions of estimated IC₅₀ values. Larger IC₅₀ values can be estimated with greater precision than smaller ones. The coefficient of variation is about 30% of the mean for IC₅₀ values <10 μg/mL and about 5% of the mean for values >10 μg/mL.

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Fig. 1.

Mean IC₅₀ values for pyrimethamine among the 16 possible combinations of mutant amino acid sites in DHFR. The error bars are the SDs of the estimated sampling distributions of IC₅₀.

Fig. 1 shows that 2 of the 4 possible single mutants (1000 and 0001) are about equally sensitive to pyrimethamine as the nonmutant allele 0000. Thus, the first step in the evolutionary pathway toward pyrimethamine resistance is likely either 0010 or 0100. Beyond that, the favored evolutionary pathways are not clear, although the quadruple mutant is obviously much more resistant than any of the triple mutants.

While the average IC₅₀ of the single, double, and triple mutants steadily increases, from 3.06 μg/mL to 49.9 μg/mL to 137.6 μg/mL, respectively, the complexity of the drug-resistance landscape is evident from Fig. 1. Compare, for example, the values among the double mutants, whose IC₅₀ values range from 0 to almost 150 μg/mL. A notable example of epistatic interaction among the mutant sites is allele 0011, which is extremely sensitive to pyrimethamine, whereas its single-mutant constituents 0010 and 0001 have IC₅₀ values of 9.56 μg/mL and 0.29 μg/mL, respectively. The growth of strains containing 0011 is impaired even in the absence of pyrimethamine. In particular, the growth rate of 0011 relative to that of 0000 in the absence of the drug is 0.169 ± 0.002, whereas the relative growth rates of 0010 and 0001 are 1.103 ± 0.009 and 0.968 ± 0.004, respectively. In other words, the combination of 2 mutants, each with a virtually normal growth rate in the absence of drug, results in a double mutant with severely impaired growth. The inability of 0011 to grow in pyrimethamine is a good illustration of the adaptive conflict between resistance to inhibition and residual enzyme activity.

The IC₅₀ values in Fig. 1 are in good agreement with another widely used metric of drug resistance, the minimal inhibitory concentration (MIC), which refers to the smallest concentration of the drug that can completely inhibit growth (Table S1). Although the solubility of pyrimethamine limits the discrimination among the MIC values of the most highly resistant alleles, the concordance between the estimated IC₅₀ and MIC values is very high. Because it is the relative ranking of the DHFR alleles that matters most in inferring the possible evolutionary pathways, this is the relevant comparison, and the linear association exhibits r² = 0.81. The linear association between the raw resistance scores of IC₅₀ and MIC has r² = 0.68, but, as noted earlier, this comparison is less relevant. The close correspondence between the rank ordering of alleles according to IC₅₀ and MIC seems to exclude the possibility that the analysis of the evolutionary pathways to pyrimethamine resistance is unduly influenced by the choice of resistance assay.

Evolutionary Model.

To analyze the data in Fig. 1, we used an evolutionary model in which selection acts to increase pyrimethamine resistance. In this model, selection pressure is assumed to be strong relative to mutation pressure, implying that the time to fixation or loss of a newly arising mutation is much shorter than the time between the occurrence of new mutations. In addition, the population size is assumed to be sufficiently large so that random genetic drift has a negligible effect on the probability of fixation. In the model, the selectively driven fixation or loss of each newly arising mutant occurs before the next mutation occurs, and so an evolutionary trajectory comprises a succession of new mutant alleles becoming fixed, each of which increases the level of drug resistance (30). In the mutational process, we allow all possible single-mutant neighbors to occur, including reversions of previously fixed mutations (33). Selection is based only on the relative values of IC₅₀, and differences in growth rate in the absence of drug are not explicitly taken into account. The rationale for this is that IC₅₀ values differ among alleles by a factor of more than 1,000, whereas for most alleles, the growth rates in the absence of drug differ by no more than a factor of 2 (Table S2). Thus, the presence of any significant drug pressure swamps selection attributable to intrinsic differences in growth rate; however, as discussed later, the case of the quadruple mutant is especially interesting in light of potential compensatory mutations.

The evolutionary model also incorporates the mutational bias of the malaria parasite. The genome of P. falciparum comprises approximately 82% A–T nucleotide pairs (34), reflecting a strong mutational bias toward A or T (25). We estimated the mutation matrix (Table S3) using a genome-wide collection of 1,105 SNPs) in intergenic regions, culled from publicly available sequencing reads from P. falciparum and its sister species P. reichenowi. Methods of analysis are described in Table S3.

Analysis of Evolutionary Pathways.

We used computer simulation to explore the evolutionary implications of the results in Fig. 1. A flow chart of the computer algorithm is provided in Fig. S1. In brief, replicate evolutionary landscapes were defined by random sampling of IC₅₀ values for each of the 16 possible alleles from normal distributions with the allele-specific genotypic means and SDs given in Fig. 1. The possible evolutionary paths along each landscape were then explored by randomly choosing single-step mutations, including possible reverse mutations, according to the mutation model given in Table S3. Each new mutant allele was discarded if its IC₅₀ value was smaller than that of the prevailing allele; otherwise, the mutant was fixed or lost according to the difference in the ranks of the IC₅₀ values defining the evolutionary landscape. This procedure reflects the principle that the probability of fixation of a new mutation is proportional to its selective advantage, while the use of ranks renders the multitude of simulated fitness landscapes commensurate. Evolution on each simulated landscape was continued until either fixation of the allele with the maximum IC₅₀ or fixation of a different allele at a submaximal fitness peak, whose single-mutant neighbors all had lower IC₅₀ values, occurred.

Simulation of 10,000 landscapes with 1,000 independent excursions in each landscape identified 10 evolutionary pathways that were traversed at significant (although very uneven) frequencies. These evolutionary pathways are shown in red in Fig. 2, where vectors of 0's and 1's again depict the amino acid residues 51, 59, 108, and 164. Numerous other pathways were realized, each at a negligible frequency, including all pathways with mutational reversions. These rare pathways mainly reflected the random sampling of extreme values from the distributions in Fig. 1, resulting from the large number of simulated landscapes.

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Fig. 2.

Major inferred pathways for the evolution of pyrimethamine resistance. The top 10 pathways are shown in red, along with their estimated probabilities.

To estimate the relative probabilities of the prominent pathways more precisely, we chose 100 of the landscapes at random and explored each with 1 million independent evolutionary excursions. The results confirm the prominence of the 10 previously mentioned pathways and yield the estimates of the relative probability of each pathway shown in Fig. 2 in the column headed “Prob.” Only those evolutionary pathways traced in red constitute the top 10 in the simulations, with the more likely pathways traced in thicker lines.

The relative frequencies of the 10 major pathways were virtually identical when 10,000 landscapes were traversed 1,000 times each and when 100 landscapes were traversed 1 million times each. This consistency reflects the relatively small SDs among the IC₅₀ distributions (Fig. 1). The probabilities given in Fig. 2 are determined primarily by the differences in IC₅₀ values, and are affected only slightly by the magnitude of the mutation bias.

Just 3 pathways in Fig. 2 account for 87.5% of the pathways traversed at significant frequencies in the simulations. All 3 of these pathways feature S108N as the first step, and the 2 most likely pathways include C59R as the second step. Similarly, all 3 of these most likely pathways have I164L incorporated either last or next to last.

Comparison With DHFR Polymorphisms.

The results in Fig. 2 are consistent with the combinations of amino acid replacements observed at significant frequencies in worldwide surveys of P. falciparum (Table 1; refs. in Table S4). The common polymorphisms, indicated by red ovals in Fig. 2, coincide with the intermediates predicted in the most likely pathways.

Note that according to Table 1, the quadruple mutant has not yet been found at significant frequencies in Africa. The absence of this allele in Africa is all the more surprising in light of the compelling evidence that pyrimethamine-resistant alleles in Africa derived from those present in Southeast Asia, where the quadruple mutant is quite common (23). Our results also may have relevance to this issue. In the absence of pyrimethamine, the growth rates of the strains bearing 0010 (S108N), 0110 (S108N + C59R), or either of the triple mutants are not manifestly impaired relative to the previous step or even the nonmutant allele (Fig. 3; Table S2). These results are consistent with previous inferences based on comparisons of the Michaelis constant (K_m) and catalytic turnover rate (k_cat) of the wild-type and mutant enzymes (35). However, when the fourth mutation (either N51I or I164L) is added, creating the quadruple mutant, a strongly deleterious interaction between N51I and I164L occurs, with the result that the fourth mutation decreases the growth rate in the absence of pyrimethamine by 30%–40% (Fig. S1), even though it increases the IC₅₀ value by 25%–50% (Fig. 1). Thus, in the absence of compensatory mutation (see Discussion), the potential fitness cost of incorporating the last mutation in the pathway is substantial.

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Fig. 3.

Relative growth rates of each intermediate in the 2 major evolutionary pathways. The growth rate of each strain after each step in the pathway was measured relative to the strain carrying the immediately preceding allele. The final steps in the pathway 0110–1110–1111 are shown in orange, and those in the pathway 0110–0111–1111 are shown in red. The error bars are 95% confidence intervals.