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Bulletin of Entomological Research (2000) 90, 3–7 3 CRITIQUE The character or the variation: the genetic analysis of the insecticide-resistance phenotype J.A. McKenzie* Centre for Environmental Stress and Adaptation Research, Department of Genetics, University of Melbourne, Parkville 3010, Australia Abstract In this critique it is argued that the genetic basis of the evolution of resistance is dependent on how the phenotypic, and underlying genotypic, variation is channelled during a selective response. A polygenic response is preferentially favoured if selection acts within the phenotypic distribution of susceptibles; a monogenic response is predicted if selection screens rare mutations with phenotypes outside that susceptible distribution. The relevance of this model to the method of genetic analysis, the prediction of resistance mechanisms to novel insecticides, the generation of resistant beneficial insects and the development of the most effective resistance and integrated pest management programmes is discussed. Introduction As biologists, we frequently concentrate our research on a particular organism and a particular character or developmental process of that organism. It is a method by which expertise, and reputation, is gained. When a particular phenotypic characteristic attracts the attention of experts of many organisms and disciplines there is an excellent opportunity, sometimes realized, for cross fertilization of ideas. The evolution of insecticide resistance has provided such an opportunity. Resistance has occurred over a broad range of species (Georghiou, 1986; Denholm et al., 1999) and has been investigated by researchers with expertise in numerous fields including applied entomology, behaviour, biochemistry, ecology, genetics, molecular biology, physiology, population biology and toxicology. The most effective studies have utilized, or combined, this expertise * Fax: (61 3) 9344 5139 E-mail: [email protected] within an evolutionary framework (Roush & McKenzie, 1987; Mallet, 1989; McKenzie, 1996, for reviews). It is equally true that resistance systems can contribute significantly to the investigation of more general evolutionary phenomena as the selective agent is known, the physiological/ biochemical/molecular basis of selection can be defined and relative fitness differences between phenotypes are sufficiently large to enable experimentation in the laboratory or field on a tractable time scale (McKenzie, 1996). Lenormand et al. (1999), in their analysis of the population dynamics of the evolution of organophosphate resistance in Culex pipiens Linnaeus (Diptera: Culicidae), provide a recent elegant example of the contribution that can be made. Resistance systems are therefore ideal to investigate the process of adaptation. In this context, one area that has generated ongoing debate in the evolutionary literature is the relative importance of monogenic or polygenic responses as the genetic basis of adaptive change (Lande, 1983; Orr & Coyne, 1992). The debate has been no less fulsome in the resistance literature (Roush & McKenzie, 1987; Macnair, 1991; McKenzie & Batterham, 1994, 1998; 4 J.A. McKenzie Gressel, 1995; Groeters, 1995; Tabashnik, 1995; McKenzie, 1996). It is my contention that much of the difference of opinion concerning the genetic basis of resistance has occurred because of a focus on the character, the resistance phenotype, rather than on the variation associated with that character. While conceding that an assessment of the quality of a character, with respect to individual components of resistance, may provide important clues to possible mechanisms, I believe a philosophy which focuses on the variation allows a flexibility in the prediction of a genetic response during selection for resistance, interpretation of data related to the resistance phenotype and in devising strategies for the management of susceptibility. Resistance as a phenotype In the resistance literature it is not uncommon for management decisions to be based on the assumption of direct correspondence between resistance phenotype and genotype. As any phenotype is influenced by the relative contribution of genetic and environmental factors, and the interaction between them, the assumption is not necessarily correct (McKenzie, 1996). Typically, a scientific assessment of resistance status has involved comparisons of concentrationmortality lines of the assessed strain, or population, against a susceptible reference strain or population (Brown & Pal, 1971; Cahill et al., 1996). If a linear relationship results when percentage mortality (in a probit scale) is plotted against log insecticide concentration, it indicates that the resistance phenotypes of the strain, or population, analysed fit a normal distribution. This, of itself, tells us nothing of the genetic basis of resistance or, indeed, of the level of genetic variation in the strain or population. The concentrationmortality relationship of a particular strain provides evidence of only a phenotypic distribution. In this context it is worth noting that results based on the gradient or the repeatability of concentration-mortality line analyses should not be over interpreted. For example, the gradient of the line reflects the level of phenotypic variation in the population, a more shallow gradient reflecting greater phenotypic variation. This is not necessarily indicative of greater genetic variation as environmental influences during the development of individuals, subsequently tested under standard conditions, may influence the result. The difficulty is compounded as concentration-mortality analysis is not always successful in distinguishing between resistant and susceptible phenotypes which limits the capacity to make effective resistance management decisions (Roush & Miller, 1986). These very limitations now commonly result in a simple compromise. Discriminating concentrations are used to define resistance status. This approach, while less laborious than concentration-mortality analysis, does not necessarily improve the quality of the resistance management response (McKenzie, 1996). The dominance of resistance, with respect to inheritance, is deduced by the distribution of the resistance phenotype of progeny (F1) of a cross between resistant and susceptible strains, relative to the parental distributions. Dominance relationships of themselves tell us nothing of the genetic basis of resistance, are influenced by the environment in which the genotype is expressed (Bourguet et al., 1996) and have the potential to evolve (Bourguet & Raymond, 1998). The distinction between dominance with respect to inheritance, and to fitness, is worthy of note. With respect to inheritance, resistance may vary from recessive to completely dominant but is frequently partially dominant (McKenzie, 1996). With respect to fitness, resistance may be rendered dominant, partly dominant or recessive depending on the concentration to which resistant and susceptible phenotypes are exposed (McKenzie, 1996; Bourguet & Raymond, 1998). Thus, the distribution of F1 phenotypes, relative to those of susceptible and resistant parents, defines the dominance relationship with respect to inheritance, while dominance with respect to fitness is concentrationdependent (McKenzie, 1996). The genetic basis of resistance The genetic basis of resistance depends on the genetic variation available and how it is channelled during selection of resistant phenotypes by an insecticide (McKenzie & Batterham, 1994). Tabashnik et al. (1998) have argued that the resistance profile of populations follows one of three possible scenarios. If mutations that generate a resistant phenotype to a particular chemical are very rare, mutations at a single genetic locus may result. Differences between populations will result from allele frequency differences at that locus (McKenzie, 1996) with the spread of the mutation significantly influenced by migration, as demonstrated in a study of the organophosphate resistance genes of C. pipiens (Raymond et al., 1991). If the mutation rate is less restricted, but the mechanism by which resistance can evolve is highly constrained, parallel mutations may occur in a number of species. This appears to be the case in the evolution of resistance to dieldrin (ffrench-Constant, 1994). Finally, if mutations causing resistance are neither uncommon nor constrained, Tabashnik et al. (1998) predict that different populations will have unique responses to selection. If such variation is appropriately screened by natural or artificial selection the response is likely to be polygenically based (McKenzie, 1996), an outcome observed in a laboratory selection programme for resistance to diazinon in the Australian sheep blowfly Lucilia cuprina (Weidemann) (Diptera: Calliphoridae) (McKenzie et al., 1992). Theoretical genetic responses The theoretical genetic responses expected given different selection regimes have been discussed in detail elsewhere (Whitten & McKenzie, 1982; McKenzie & Batterham, 1994, 1998). In summary, it is suggested that monogenic responses for rare mutations will be preferentially favoured if selection acts outside the phenotypic distribution of susceptible phenotypes. That is above the LC100 of susceptibles. However, a polygenic response is more likely if selection acts within the phenotypic distribution of susceptibles, at concentrations less than LC100, as genetic differences between phenotypes of a continuous distribution are expected to be polygenically based. The response observed in the field depends on how the variation is screened. It must be noted, however, that if a resistant phenotype is primarily under monogenic control this does not mean that polygenic variation for resistance does not exist in a population. For example, selection with diazinon within the resistance distributions of populations of L. cuprina fixed for either a resistant (McKenzie et al., 1980) The genetic analysis of insecticide resistance or a susceptible allele (McKenzie et al., 1992) at the diazinon resistance locus, Rop-1, resulted in a polygenically determined response. As expected (McKenzie, 1996; Tabashnik et al., 1998), the responses were population specific (McKenzie et al., 1980, 1992). A key question, on which few data are available, is to analyse the response to selection when resistant mutations are rare in a population subjected to selective concentrations less than the LC100 of susceptibles. In some circumstances, a monogenic response may result (Roush & Hoy, 1981; Heather, 1986). However, a particular outcome may depend on the balance between selection for and, because of pleiotropic effects of the resistant allele (Roush & McKenzie, 1987; McKenzie, 1996), selection against a resistant phenotype under monogenic control. At the point of balance, chance events may be important in defining the genetic basis of the response (Whitten & McKenzie, 1982; Tabashnik, 1990). This is an area that I believe warrants further work because it is of vital importance to the determination of resistance management strategies (Gressel, 1995; McKenzie, 1996; Denholm et al., 1999) and, more generally, to our understanding of the selective processes that determine the genetic structure of populations (Lande, 1983; Macnair, 1991; Orr & Coyne, 1992). Equally important is the capacity to genetically analyse the phenotypic variation associated with resistance. Methods of genetic analysis Genetic analysis of the resistance phenotype has been attempted in a number of ways. These include concentration-mortality line analysis, quantitative genetic analysis and classical genetic analysis using morphological and/or molecular markers. The method of genetic analysis is constrained by the ease with which the insect can be reared, the capacity to generate pure breeding strains, the ability to manipulate crosses for F2, backcross or testcross analyses or for more complex quantitative analytical genetic designs and by the availability of genetic markers for interand intra-chromosomal genetic mapping (McKenzie, 1996). Concentration-mortality line analysis depends on the presence or absence of inflection points in the lines of segregant populations, that are exposed to a range of concentrations, to determine the genetic basis of resistance. In theory, inflection points at critical discriminating concentrations allow single gene control to be distinguished from polygenic control where a linear response, without inflection, is expected over an appropriate concentration range (McKenzie, 1996). In practice, analysis is more difficult. For example, if parental and F1 concentrationmortality lines overlap, or if environmental effects are significant, clearly defined inflection points may be difficult to identify. The distinction between step function (monogenic control) and continuous (polygenic control) lines is obscure in these circumstances (Mallet, 1989; Roush & Daly, 1990). Classical quantitative genetic analysis has been restricted to estimates of heritability (Firko & Hayes, 1990). An estimate of heritability is strictly only applicable to the particular population in which the estimate is made at the time of that estimate. However, for a range of insects and insecticides the estimates of heritability in field and laboratory studies fall between 0.14 and 0.47 (McKenzie, 1996), comparable to the estimates of heritability for 5 physiological characters in general (Mousseau & Roff, 1987). Classical genetic analysis has traditionally been carried out in organisms such as Drosophila melanogaster Meigen (Diptera: Drosophilidae), Musca domestica Linnaeus (Diptera: Muscidae), L. cuprina and some mosquito species where morphological markers are available to distinguish chromosomes and the regions within them (McKenzie, 1996). Essentially, to map resistance loci to particular chromosomes, recombination within a chromosome must be restricted in the segregant generation so that the association between the marker and the chromosome it identifies is complete. Intra-chromosomal mapping requires free recombination to identify co-segregation of a marker with a chromosomal region. The availability of molecular markers has allowed this biphasic approach to be extended to organisms with a limited availability of morphological markers. In the case of resistance to Bacillus thuringiensis by Plutella xylostella (Linnaeus) (Lepidoptera: Plutellidae), the biphasic approach using molecular markers has been critical in assessing the relative contribution of monogenic or background polygenic influences in the evolution of resistance (Tabashnik, 1994; Heckel et al., 1999). I believe the approach used by Heckel and his colleagues has much to offer in the future genetic analysis of resistance phenotypes, providing a level of precision of mapping that has previously been restricted to few insect species. Future research I have already alluded to some areas I believe warrant more research effort. A consequence of considering the variation, associated with resistance and the genetic outcome of how that variation is selectively channelled offers further opportunities. These potentially allow us to be proactive rather than reactive in devising strategies to maximize the impact of the insecticide while minimizing the chance resistance will evolve. Predicting resistance If the selection regime screens for polygenic variation, the response is likely to be population specific. Resistance management programmes need to be structured accordingly. If monogenic responses are favoured, insecticidal delivery systems that generate rectangular decay curves, with the initial concentration above the LC100 of resistant heterozygotes, minimize the likelihood of resistance evolving (McKenzie, 1987, 1996). Typically, the resistance status and relative fitness of heterozygotes is unknown prior to resistance evolving. However, mutagenesis of susceptible laboratory populations followed by screening at concentrations above the LC100 of susceptibles has successfully generated monogenic resistant variants to a number of different insecticides (Kikkawa, 1964; Wilson & Fabian, 1986; McKenzie et al., 1992; Smyth et al., 1992; Adcock et al., 1993; Yen et al., 1996). In the case of dieldrin and diazinon resistance in L. cuprina, these variants are genetically (McKenzie et al., 1992; Smyth et al., 1992) and molecularly (McKenzie & Batterham, 1998) identical to those that evolved in natural populations. Further trials of the mutagenesis and selection approach need to be conducted for a range of organisms and chemicals to which they have evolved resistance to test if the 6 J.A. McKenzie apparent predictive power of the technique in L. cuprina can be generalized. If laboratory screening continues to generate similar genetic variants to those that have evolved in the field, there would be considerable confidence in the capacity of the approach to predict the genetic mechanism of resistance to new insecticides. There are obvious advantages in being able to predict likely resistance mechanisms to a new insecticide before it is released for pest control. Not least among these is being able to assess possible cross-resistance and to be proactive in the derivation of management practices to minimize the evolution of resistance (McKenzie & Batterham, 1998). The capacity to select for monogenic resistance may also have important consequences in the selection of resistant predators for use in integrated pest management (IPM). Selecting resistant predators Predator–prey interactions form an important foundation of many IPM programmes. The availability of resistant natural enemies potentially enables pesticide use against the pest without adversely influencing the resistant predator. Thus, the benefits of the predator–prey interactions are maintained during periods of pesticide application. Attempts to select resistant natural enemies in artificial selection programmes have, with the exception of responses in some phytoseiid mites, however, generally been disappointing (Hoy, 1990). Two explanations have been suggested for the relative lack of response. It has been suggested that natural enemies may have evolved so that they lack the capacity to detoxify pesticides (Croft & Mullin, 1984) or that there is a lack of genetic variability in laboratory populations on which selection can act (Hoy, 1990). As responses have been observed in several species of natural enemies (Hoy, 1990) it seems unlikely that the first suggestion provides a general explanation. The second explanation is possible for particular laboratory populations. During the establishment of these populations, bottlenecks and the effects of genetic drift may result in unrepresentative genotypic distributions and loss of variation. It remains true, however, that selection programmes have used concentrations of pesticide that cause mortality to only a proportion of the population. That is selection has occurred within the original phenotypic distribution. Selection for such incremental change preferentially selects for a polygenic response which may not provide a sufficiently resistant phenotype to allow a natural enemy to be effectively used in IPM programmes. If monogenic variants are in the initial population they may be screened by selection (Roush & Hoy, 1981). Such events are very rare but produce a resistant phenotype significantly different from the phenotype of the base population. This has not been the typical response when selection has occurred within the phenotypic distribution in the absence of such variants as resistance levels have typically been low (Hoy, 1990). By following a regime of mutagenesis, to increase genetic variation, and selection above the LC100 of susceptibles, to specifically screen for monogenic variants in an appropriate field background, it may be possible to select for natural enemies with high levels of resistance. The mutagenesis/selection approach does not guarantee that a resistant phenotype will be selected but if the programme is successful the resistant phenotypes, and genotypes, may be particularly suitable for use in IPM programmes (McKenzie, 1996). It is an area of research, based on a philosophy of channelling the variation associated with the resistance phenotype in a manner to gain a desired genetic response, that I am sure is worth investigating. Conclusion Viewing resistance as a phenotype and relating an adaptive response to the way selection acts on the phenotypic variation to channel the underlying genetic variation provides a template to predict whether the response is more likely to be polygenically or monogenically controlled. From this foundation, it is possible to consider establishing the genetic basis of resistance to a new insecticide before it evolves in natural populations and to devise delivery and management systems that minimize the probability that resistance will in fact evolve. Much work is needed. However, imagine an IPM programme in which a new insecticide is introduced via a delivery system that minimizes selection for resistance in a pest at concentrations that enable laboratory-generated resistant natural enemies to survive. Such a programme would provide a more stringent ecological and evolutionary challenge to pests than has typically occurred and would significantly enhance the management of agricultural and horticultural ecosystems. It is a goal worth striving for. Acknowledgements Support from the Australian Research Council to enable me to think about, and experiment in, the evolution of insecticide resistance is gratefully acknowledged as is the input of numerous colleagues with whom I have had many helpful exchanges about the topics discussed in this paper. Phil Batterham, David Heckel and Alan McCaffery are three of those colleagues. They are thanked for comments on the manuscript. References Adcock, G.J., Batterham, P., Kelly, L.E. & McKenzie, J.A. (1993) Cyromazine resistance in Drosophila melanogaster (Diptera: Drosophilidae) generated by ethyl methanesulfonate mutagenesis. Journal of Economic Entomology 86, 1001–1008. Bourguet, D. & Raymond, M. 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(1994) Evolution of resistance to Bacillus thuringiensis. Annual Review of Entomology 39, 47–79. Tabashnik, B.E. (1995) Insecticide resistance. Trends in Ecology and Evolution 10, 164–165. Tabashnik, B.E., Liu, Y-B., Malvar, T., Heckel, D.G., Masson, L. & Ferré, J. (1998) Insect resistance to Bacillus thuringiensis: uniform or diverse. Philosophical Transactions of the Royal Society of London B 353, 1751–1756. Whitten, M.J. & McKenzie, J.A. (1982) The genetic basis for pesticide resistance. pp. 1–16 in Lee, K.E. (Ed.) Proceedings of the 3rd Australasian Conference of Grassland Invertebrates. Adelaide, South Australian Government Printer. Wilson, T.G. & Fabian, J.A. (1986) Drosophila melanogaster mutant resistant to a chemical analog of juvenile hormone. Developmental Biology 118, 190–201. Yen, J.L., Batterham, P., Gelder, B. & McKenzie, J.A. (1996) Predicting resistance and managing susceptibility to cyromazine in the Australian sheep blowfly Lucilia cuprina. Australian Journal of Experimental Agriculture 36, 413–420. (Accepted 1 January 2000) © CAB International, 2000