The importance of understanding fitness costs associated with drug resistance throughout the life cycle of malaria parasites
Fitness costs and benefits in response to artificial artesunate selection in Plasmodium
Recommendation: posted 02 November 2022, validated 18 November 2022
Antimalarial resistance is a major hurdle to malaria eradication efforts. The spread of drug resistance follows basic evolutionary principles, with competitive interactions between resistant and susceptible malaria strains being central to the fitness of resistant parasites. These competitive interactions can be used to design resistance management strategies, whereby a fitness cost of resistant parasites can be exploited through maintaining competitive suppression of the more fit drug-susceptible parasites. This can potentially be achieved using lower drug dosages or lower frequency of drug treatments. This approach has been demonstrated to work empirically in a rodent malaria model [1,2] and has been demonstrated to have clinical success in cancer treatments . However, these resistance management approaches assume a fitness cost of the resistant pathogen, and, in the case of malaria parasites in general, and for artemisinin resistant parasites in particular, there is limited information on the presence of such fitness cost. The best suggestive evidence for the presence of fitness costs comes from the discontinuation of the use of the drug, which, in the case of chloroquine, was followed by a gradual drop in resistance frequency over the following decade [see e.g. 4,5]. However, with artemisinin derivative drugs still in use, alternative ways to study the presence of fitness costs need to be undertaken.
There are several good in vitro studies demonstrating artemisinin resistant parasites being competitively suppressed by wildtype parasites [see e.g. 6–9], however, these have the limitation that they will only be able to detect the fitness cost during the blood stage of the infection and in an artificial environment. So far, there have not been animal models that have thoroughly studied the presence of resistance fitness costs for artemisinin resistant parasites. Moreover, in these types of studies, the focus is mostly on the fitness cost as detected in the vertebrate host. However, malaria parasites spent a significant portion of their life cycle in the mosquito host, where fitness costs could also be expressed. Overall, it is the fitness over the entire life cycle of the parasite that would determine if and to what extent a reduction in resistance frequency would be observed when the use of a drug is stopped.
Here, Villa and colleagues present a study to quantify such fitness cost of artesunate-resistant parasites, not only in a vertebrate host, but also in the mosquito vector . They used the underutilized model system of avian malaria species Plasmodium relictum in canaries. Villa and colleagues selected for several different resistance strains, which had a similar delayed clearance phenotype as observed in the field. Interestingly, they did not find evidence of a fitness cost in the vertebrate host. In fact, the resistant strains reached greater parasitaemia than the susceptible strains. From this set of experiments it is unclear whether this is an anomaly or a relevant result. Future work should establish this, though fitness benefits associated with drug resistance have been seen before in Leishmania parasites . An important caveat to the present study is that the parasites were grown in the absence of competition and it is feasible that a cost is not detected when growing by themselves, but would become apparent when in competition. However, these types of experiments are technologically more challenging to perform as it would require an accurate quantification methodology able to distinguish based on one SNP. This problem has been circumvented by either using relative peak height in sanger sequencing , or via the likely more accurate route of pyrosequencing [7–9], though these methodologies only give relative frequencies rather than absolute densities.
The most interesting observation in the study by Villa et al is that the authors detected a fitness cost being played out in the mosquito vector, where the resistant strains had a decreased infectivity compared to the susceptible strain. This finding is important because 1) it demonstrates that the whole life cycle needs to be taken into account when understanding fitness costs, 2) resistance management strategies that are based on treatment within the vertebrate host may not have the intended effect if the cost does not play out in this host, and 3) it opens new research avenues to explore the possibility of exploiting fitness costs in mosquito vector. Future research should focus on incorporating these assays on fitness costs in mosquitoes, particularly for P. falciparum parasites. Additionally, it would be interesting to expand this work in a competitive environment, both in the vertebrate host as in the mosquito host. Finally, it would be important to establish the generalizability of such fitness cost in mosquitoes. If it is a significant factor, mathematical models could incorporate this effect in predictions on the spread of resistance.
 Huijben S, Bell AS, Sim DG, Tomasello D, Mideo N, Day T, et al. 2013. Aggressive chemotherapy and the selection of drug resistant pathogens. PLoS Pathog. 9(9): e1003578. https://doi.org/10.1371/journal.ppat.1003578
 Huijben S, Nelson WA, Wargo AR, Sim DG, Drew DR, Read AF. 2010. Chemotherapy, within-host ecology and the fitness of drug-resistant malaria parasites. Evolution (N Y). 64(10): 2952-68. https://doi.org/10.1111/j.1558-5646.2010.01068.x
 Zhang J, Cunningham JJ, Brown JS, Gatenby RA. 2017. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat Commun. 8(1). https://doi.org/10.1038/s41467-017-01968-5
 Laufer MK, Takala-Harrison S, Dzinjalamala FK, Stine OC, Taylor TE, Plowe C v. 2010. Return of chloroquine-susceptible falciparum malaria in Malawi was a reexpansion of diverse susceptible parasites. J Infect Dis. 202(5): 801-808. https://doi.org/10.1086/655659
 Mharakurwa S, Matsena-Zingoni Z, Mudare N, Matimba C, Gara TX, Makuwaza A, et al. 2021. Steep rebound of chloroquine-sensitive Plasmodium falciparum in Zimbabwe. J Infect Dis. 223(2): 306-9. https://doi.org/10.1093/infdis/jiaa368
 Tirrell AR, Vendrely KM, Checkley LA, Davis SZ, McDew-White M, Cheeseman IH, et al. 2019. Pairwise growth competitions identify relative fitness relationships among artemisinin resistant Plasmodium falciparum field isolates. Malar J. 18: 295. https://doi.org/10.1186/s12936-019-2934-4
 Hott A, Tucker MS, Casandra D, Sparks K, Kyle DE. 2015. Fitness of artemisinin-resistant Plasmodium falciparum in vitro. J Antimicrob Chemother. 70(10): 2787-2796. https://doi.org/10.1093/jac/dkv199
 Straimer J, Gnädig NF, Stokes BH, Ehrenberger M, Crane AA, Fidock DA. 2017. Plasmodium falciparum K13 mutations differentially impact ozonide susceptibility and parasite fitness in vitro. mBio. 8(2): e00172-17. https://doi.org/10.1128/mBio.00172-17
 Nair S, Li X, Arya GA, McDew-White M, Ferrari M, Nosten F, et al. 2018. Fitness costs and the rapid spread of kelch13-C580Y substitutions conferring artemisinin resistance. Antimicrob Agents Chemother. 62(9). https://doi.org/10.1128/AAC.00605-18
 Villa M, Berthomieu A, Rivero A. Fitness costs and benefits in response to artificial artesunate selection in Plasmodium. 2022. bioRxiv, 20220128478164, ver 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.01.28.478164
 Vanaerschot M, Decuypere S, Berg M, Roy S, Dujardin JC. 2013. Drug-resistant microorganisms with a higher fitness--can medicines boost pathogens? Crit Rev Microbiol. 39(4): 384-394. https://doi.org/10.3109/1040841X.2012.716818
 Hassett MR, Roepe PD. In vitro growth competition experiments that suggest consequences of the substandard artemisinin epidemic that may be accelerating drug resistance in P. falciparum malaria. 2021. PLoS One. 16(3): e0248057. https://doi.org/10.1371/journal.pone.0248057
Silvie Huijben (2022) The importance of understanding fitness costs associated with drug resistance throughout the life cycle of malaria parasites. Peer Community in Evolutionary Biology, 100156. 10.24072/pci.evolbiol.100156
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.
Evaluation round #1
DOI or URL of the preprint: https://doi.org/10.1101/2022.01.28.478164
Version of the preprint: 1
Author's Reply, 27 Sep 2022
Decision by Silvie Huijben, posted 19 Apr 2022
Dear authors. I have now received two reviews to your manuscript. Both the reviewers and myself agree this is an important piece of work that addresses a critical component of the evolution of drug-resistant malaria parasites. The reviewers’ comments are mostly centered around the clarity of the methods used, the data presentation and some of the data interpretation. In addition to the reviewers’ comments, please also consider the following:
- Experiment 2: What is the sample size for the data presented in figure 4? Since unfed mosquitoes were removed, how many were remaining that this data is based on? Also, only 29% of females were infected, but this data is, I presume, based on all mosquitoes regardless of infection status. It would be important to address this caveat, as it might be masking a true effect if it were there. Additionally, it seems that for some strains, it takes longer to establish an infection in the mosquito (AS2, figure 5) and this strain would not have any (detectable?) oocysts at the time of egg laying. It is unclear to me what the hypothesis was to expect to observe differences on day 3-6 when the parasites are unlikely to have an impact on the mosquito at that point in time.
- An argument is made that the phenotype of the selected lines is a delayed clearance time, similar as seen in the field. As the data is currently presented, I do not believe we could make this conclusion. In the untreated infections, there is also a ‘delayed clearance’ where AS1 and AS2 persist for longer. An alternative hypothesis could be that these are just be differences in growth dynamics (e.g. the reference strain peaking and crashing sooner than the resistant strains), and perhaps these differences are enlarged by the treatment? As also mentioned in the review by Sarah Reece, it would be important to show the full parasite dynamics (also before the onset of treatment) to be making any comparisons based on treatment.
- Slightly increased fitness was observed in the in selected lines: was the untreated reference strain selected at the same time? Could these differences feasibly have emerged as part of the selection process itself, rather than the pressure that the drug provides?
- No fitness cost were found in the vertebrate host in these experiments. It would be useful to compare these to results found in in vitro experiments. While these are referenced as having been conducted, the results from these experiments are not mentioned. In most of these in vitro competition experiments they do demonstrate a fitness cost for ART resistant parasite (in the vertebrate host). It would be useful to discuss this discrepancy as it may throw the extrapolation from this P. relictum model to P. falciparum into question and therefore the conclusion on costs observed in the mosquito host.
- It would be nice to see the raw data of the weights and RBC dynamics as well.
line 108: “will suffer higher fitness costs than their unselected counterparts”, change to “suffer fitness costs in comparison to their unselected counterparts”
Line 119: “occasional passage through the mosquito”, is it possible to give more details on frequency to get a sense on how frequently ‘occasional’ is?
Lines 132-137: Were lines AS2 and AS3 obtained as true replicate lines or isolated from the same birds (somewhere in the passage history)? Were other parasite lines obtained as well or were these the only ones that survived the passages? Some additional information on the process (perhaps in supplementary information) would likely be of interest to some.
Line 271: add ‘was’
Figure 1: Update the caption, it refers to different names of lines (C, R1, R2 and R3) and dpbi instead of pbi, etc)
Figure 2: brief explanation of px/pb would be helpful in the figure legend. It would also be helpful if the terminology around the reference line would be consistent, it’s mostly referred to as ‘reference line’, but in figures and sometimes in the text as ‘control’.
Bibliography: Fix author ‘Huijben’ initials from ‘U’ to ‘S’