Much research effort has been extended toward developing systems for managing soil inhabiting insect pests of crops with entomopathogenic nematodes as biocontrol agents. Although small plot or laboratory experiments may suggest a particular insect pest is vulnerable to management in this way, it is often difficult to scale-up nematode production for application at the field- and farm scale to make such a tactic viable. Part of the problem is that entomopathogenic nematode strains must be propagated by serial passage in vivo, because storage by freezing decreases fitness. At the same time, serial propagation results in loss of virulence (ability to infect) over generations in the laboratory, a phenomenon called attenuation.

To probe the underlying reasons for development of attenuation, as a prerequisite to designing strategies to mitigate it, Shapiro-Ilan and Raymond [1] turned to evolutionary theory of social conflict as a possible explanatory framework. Virulence of entomopathogenic nematodes depends on a combination of virulence factors, like various proteases, secreted by both the nematode and symbiotic bacteria to overcome host defenses. Attenuation is characterized in part by a reduced production of these factors. Invasion of a host involves simultaneous attack by a group of nematodes ("cooperators"), which together neutralize host defenses enough to allow individuals to successfully invade. "Cheaters" in the invading population can avoid the metabolic costs of producing virulence factors while reaping the benefits of infecting the host made vulnerable by the cooperators in the population. The authors hypothesize that an increase in frequency of cheaters may contribute to attenuation of virulence during serial propagation in the laboratory. The evolutionary dynamics of cheater frequency in a population have been explored in many contexts as part of kin selection theory. Cheaters can increase in a population by outcompeting cooperators in a host if overall relatedness within the invading population is low. Conversely, frequency of altruism, or costly cooperation, increases in a population if relatedness is high, which is enhanced by low effective dispersal. However, a population that is too isolated can suffer from inbreeding effects, and competition will occur mainly among relatives, which decreases the fitness benefits of altruism.

Shapiro-Ilan and Raymond [1] tested changes in virulence and reproductive output in a serially propagated entomopathogenic nematode, Heterorhabditis floridensis. They compared lines of high or low relatedness, manipulated via multiplicity of infection (MOI) rates (where a low dose of nematodes gives high relatedness and a high dose gives low relatedness); and under global or local competition, manipulated by pooling populations emerging from all or only two host cadavers per generation, respectively. As predicted, treatments of high relatedness (low MOI) and global competition had the greatest level of reproduction, while all lines of low relatedness (high MOI) evolved decreased reproduction and decreased virulence, which led to extinction. The key finding was that lines in the high relatedness (low MOI) and low (local) competition treatment exhibited the most stable virulence through the 12 generations tested. Thus, to minimize attenuation of virulence while maintaining fitness of recently isolated entomopathogenic nematodes, the authors recommend insect hosts be inoculated with low doses of nematodes from inocula pools from as few cadavers as possible.

The application of evolutionary theory, with a clever experimental design, to an important problem in pest management makes this paper particularly noteworthy.

Reference

[1] Shapiro-Ilan D, Raymond B. 2016. Limiting opportunities for cheating stabilizes virulence in insect parasitic nematodes. Evolutionary Applications 9:462-470. doi: 10.1111/eva.12348

The increasing number of reported cases of antibiotic resistance is one of today’s major public health concerns. Dealing with this threat involves understanding what drives the evolution of antibiotic resistance and investigating whether we can predict (and subsequently avoid or circumvent) it [1].
One of the most illustrative and common models of adaptation (and, hence, resistance evolution) is Fisher’s Geometric Model (FGM). The original model maps phenotypes to fitness, meaning that each point in the fitness landscape corresponds to a phenotype rather than a genotype. However, it has been shown that when mutations are numerous enough, FGM can also describe adaptive walks in genotype space [2]. Nevertheless, limitations have been highlighted, particularly when trying to study complex scenarios such as antibiotic resistance evolution [3].
Harmand et al. [4] incorporated three extensions to the FGM, which allowed them to match the mutational patterns of antibiotic resistance that they obtained from a screen across a gradient of drug concentrations. The implemented extensions took into account that: 1) only a subset of mutations may contribute to traits under selection, reflecting that not all regions in the genome affect the ability to resist antibiotics; 2) mutations that confer a fitness increase in one environment may not reflect a similar increase in others, if the selective constraints are different; and 3) different antibiotic concentrations may either constrain the maximum fitness that populations can reach (changing the height of the fitness peak) or change the rate of fitness increase with each mutation (changing the width/slope of the peak).
Traditionally, most empirical fitness landscape studies have focused on a subset of mutations obtained after laboratory evolution in specific conditions [5, 6]. The results obtained in Harmand et al. [4] indicate a potential shortcoming of studying these small fitness landscapes: rather than having a constrained evolutionary path to a resistant phenotype, as previously observed, their results suggest that antibiotic resistance can be the product of mutations in different regions of the genome. Returning to the fitness landscape perspective, this indicates that there are many alternative paths that can lead to the evolution of antibiotic resistance. This comparison points at a difficult challenge when aiming at developing a predictive framework for evolution: real-time experiments may indicate that evolution is likely to take similar and predictable paths because the strongest and most frequent mutations dictate the outcome, whereas systematic screens of mutants potentially indicate several paths, that may, however, not be relevant in nature. Only a combination of different experimental approaches with motivated theory as presented in Harmand et al. [4] will allow for a better understanding of where in this continuum evolution is taking place in nature, and to which degree we are able to interfere with it in order to slow down adaptation.

References

[1] Palmer AC, and Kishony R. 2013. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nature Review Genetics 14: 243—248. doi: 10.1038/nrg3351

[2] Tenaillon O. 2014. The utility of Fisher’s geometric model in evolutionary genetics. Annual Review of Ecology, Evolution and Systematics 45: 179—201. doi: 10.1146/annurev-ecolsys-120213-091846

[3] Blanquart F and Bataillon T. 2016. Epistasis and the structure of fitness landscapes: are experimental fitness landscapes compatible with Fisher’s geometric model? Genetics 203: 847—862. doi: 10.1534/genetics.115.182691

[4] Harmand N, Gallet R, Jabbour-Zahab R, Martin G and Lenormand T. 2017. Fisher’s geometrical model and the mutational patterns of antibiotic resistance across dose gradients. Evolution 71: 23—37. doi: 10.1111/evo.13111

[5] de Visser, JAGM, and Krug J. 2014. Empirical fitness landscapes and the predictability of evolution. Nature 15: 480—490. doi: 10.1038/nrg3744

[6] Palmer AC, Toprak E, Baym M, Kim S, Veres A, Bershtein S and Kishony R. 2015. Delayed commitment to evolutionary fate in antibiotic resistance fitness landscapes. Nature Communications 6: 1—8. doi: 10.1038/ncomms8385