Prospective malaria control using entomopathogenic fungi: comparative evaluation of impact on transmission and selection for resistance
 Penelope A Lynch^{1}Email author,
 Uwe Grimm^{1},
 Matthew B Thomas^{2} and
 Andrew F Read^{3, 4}
DOI: 10.1186/1475287511383
© Lynch et al.; licensee BioMed Central Ltd. 2012
Received: 21 June 2012
Accepted: 3 October 2012
Published: 22 November 2012
Abstract
Background
Chemical insecticides against adult mosquitoes are a key element in most malaria management programmes, but their efficacy is threatened by the evolution of insecticideresistant mosquitoes. By killing only older mosquitoes, entomopathogenic fungi can in principle significantly impact parasite transmission while imposing much less selection for resistance. Here an assessment is made as to which of the wide range of possible virulence characteristics for fungal biopesticides best realise this potential.
Methods
With mathematical models that capture relevant timings and survival probabilities within successive feeding cycles, transmission and resistancemanagement metrics are used to compare susceptible and resistant mosquitoes exposed to no intervention, to conventional instantkill interventions, and to delayedaction biopesticides with a wide range of virulence characteristics.
Results
Fungal biopesticides that generate high rates of mortality at around the time mosquitoes first become able to transmit the malaria parasite offer potential for large reductions in transmission while imposing low fitness costs. The best combinations of control and resistance management are generally accessed at high levels of coverage. Strains which have high virulence in malariainfected mosquitoes but lower virulence in malariafree mosquitoes offer the ultimate benefit in terms of minimizing selection pressure whilst maximizing impact on transmission. Exploiting this phenotype should be a target for product development. For indoor residual spray programmes, biopesticides may offer substantial advantages over the widely used pyrethroidbased insecticides. Not only do fungal biopesticides provide substantial resistance management gains in the long term, they may also provide greater reductions in transmission before resistance has evolved. This is because fungal spores do not have contact irritancy, reducing the chances that a bloodfed mosquito can survive an encounter and thus live long enough to transmit malaria.
Conclusions
Delayedaction products, such as fungal biopesticides, have the potential to achieve reductions in transmission comparable with those achieved with existing instantkill insecticides, and to sustain this control for substantially longer once resistant alleles arise. Given the current insecticide resistance crisis, efforts should continue to fully explore the operational feasibility of this alternative approach.
Background
The impressive reductions in global malaria burden achieved this century by chemical insecticides against adult mosquitoes could be eroded by insecticideresistant mosquitoes [1–6], just as they were last century [7]. In principle, the evolution of insecticide resistance could be considerably slowed and perhaps prevented altogether by vector control aimed at killing only older mosquitoes, socalled latelife action (LLA) [8]. Malaria parasites in a mosquito host take at least nine days to develop to a stage which can be transmitted to a human via an infectious bite [9]. Since mortality in wild mosquito populations is high, the majority of eggs are produced by young mosquitoes. Thus, a vectorcontrol treatment which kills only older mosquitoes could remove infected mosquitoes before they can transmit malaria whilst only impacting the reproductive success of only the relatively few mosquitoes that survive to old age. This would dramatically reduce transmission while exerting only weak selection for resistance.
One option for an LLA vectorcontrol measure is entomopathogenic fungi [10]. Naturally occurring strains of two fungi, Beauveria bassiana and Metarhizium anisopliae, are already in commercial use for agricultural applications and have been shown to infect and kill mosquitoes in laboratory and field settings. Fungal spores can be picked up by mosquitoes following contact with treated surfaces, and so could be used against mosquitoes in indoor residual spray (IRS) programmes, or delivered via traps, curtains or netting [11–16].
A wide variety of mortality schedules can be induced in Anopheles by entomopathogenic fungi [17]. In some cases, all mosquitoes can be killed within a few days; in others, background mortality rates can be barely altered. This virulence variation depends on isolate [11], dose [18] and malariainfection status [15, 18], see also [19]. Lethality can also be increased by genetically modifying fungal isolates [20–22].
If fungal entomopathogens are to realize the potential of the LLA approach to sustainable malaria control, candidate biopesticides need to be chosen which balance reductions in parasite transmission (maximized by high fungal virulence) with resistance management (maximized by low fungal virulence). Here a mathematical model is used to ask which virulence phenotypes best achieve this balance. The intention is to guide the development of target product profiles. The possible efficacy of fungal biopesticides in IRS campaigns is compared with that of pyrethroidbased insecticides now in widespread use. Pyrethroids are highly lethal if contacted by a mosquito, but they also have a strong excitorepellency effect, which can drive away mosquitoes before they receive a lethal dose [23–25]. There is evidence that fungal spores do not repel mosquitoes [26], raising the prospect that, for IRS, fungal biopesticides might more effectively reduce transmission than pyrethroidbased technologies currently in use.
Methods
The model
Many malaria transmission models already exist [27], but most do not capture the detailed timings and probabilities of infection, infectiousness, reproduction and mortality over the mosquito lifespan which are key to assessing whether LLAs can provide a useful balance of transmission control and low selection for resistance. In order to encompass these elements, a model has been developed with two separate components, a markovian, deterministic, feeding cycle model (FCM) which calculates survival, egglaying and infectious bite values during the lifetime of an adult mosquito, and a population model (PM) which tracks the populationlevel spread of resistance alleles and corresponding loss of transmission control. The model is a development of a simpler version previously used to evaluate putative chemical LLAs [8]. Other modelling frameworks used to assess the LLA approach are heuristically useful but lack sufficient detail to define target virulence schedules [28–30].
The feeding cycle model
The FCM calculates survival, egglaying and infectious bite values across a series of discrete adult age classes for a specified type of mosquito (e.g., susceptible) subjected to a given intervention (e.g., a particular fungal biopesticide at a particular coverage). Each sequential age class is defined as lasting for the average length of one gonotrophic cycle. Use of the mosquito feeding cycle as the basis for agestructured analyses of mosquito populations is well established [31–34].
Assuming that the rate at which newly maturing adults join a population is constant through time, and that the size of the human host population is unaffected by the intervention being assessed, RAIB is equal to the proportionate reduction in the entomological inoculation rate (EIR), the number of infectious bites experienced per person per unit of time.
To evaluate mosquito fitness, the average number of eggs produced per mosquito per lifetime is used as a proxy for lifetime reproductive success (LRS). The selection coefficient, the proportionate fitness benefit of resistance to a given intervention, is calculated as $\text{Selection Coefficient}=1\frac{\text{LRS for specified mosquito type with intervention}}{\text{LRS for susceptible mosquitoes without intervention}}$. A selection coefficient of zero means no selection pressure in favour of resistance, with higher selection coefficients indicating increasingly strong selection for resistance.
Formulating these key variables in relative terms minimizes the sensitivity of the conclusions to parameter values that are independent of the vectorcontrol treatment or mosquito phenotype being evaluated.
Variables and parameters for the feeding cycle model
Variable or Parameter  Symbol  Comments and Constraints 

Time, measured in whole units equal to average length of sporogonic cycle, from infection of mosquito by malaria to cycle from which mosquito gives infectious bites  D  input 0< D 
Number of age classes included in analysis  λ  
Cycle number (identifies specific cycle in the λ cycles over which probabilities are tracked in the FCM)  i  0≤ i ≤λ 
Malaria status, the number of whole or partial cycles since infection with malaria  m  0≤ m ≤λ, m = 0 means not infected 
Biopesticide infection status, the number of whole or partial cycles since infection with biopesticide  l  0≤ l≤ λ, l = 0 means not infected 
Average number of eggs laid in cycle i by mosquitoes surviving to the start of cycle i  F _{ i }  
Average lifetime number of eggs laid per mosquito  φ  
Average number of eggs laid in cycle i, by mosquitoes starting cycle i with malaria status m and biopesticide status l  f _{ i,m,l }  m<i l<i 
Average probability of survival from start of cycle i to start of cycle i+1  S _{ i }  
Average probability that a mosquito starting cycle i with malaria status m and biopesticide status l, will survive to start of cycle i+1  s _{ i,m,l }  m<i l<i 
Average probability of a mosquito being alive at start of cycle i.  V _{ i }  
Average probability of a mosquito being alive, with malaria status m and biopesticide status l, at start of period i.  v _{ i,m,l }  m<i l<i 
Probability that a mosquito alive at start of cycle i with malaria status m and biopesticide status l, survives and bites host type h in cycle i  q _{ i,m,l,h }  m<i l<i 
Type of host attacked  h  h=1, nonhuman 
h=2, noninfectious human  
h=3, infectious human  
Average number of infectious bites in cycle i per mosquito alive at the start of cycle i  I _{ i }  
Average lifetime number of infectious bites per mosquito  u 
Values used in FCM for this analysis
Variable or Parameter  Symbol  Value  units 

Background instantaneous mortality rate for mosquito age i  r _{ B,i }  11.75% ^{ 1 }  per day 
Length of gonotrophic cycle  w  2.85 ^{ 1 }  days 
Time spent host searching and feeding during a cycle  b  1.26 ^{ 5 }  days 
Time spent finding oviposition site and laying during a cycle  ϕ  1.26 ^{ 5 }  days 
Length of resting period (days)  η  0.32 ^{ 5 }  days 
Proportion human population infectious for malaria^{4}  p  4.28% ^{ 1 }  
Probability attacks nonhuman host  H  0.17 ^{ 1 }  
Probability killed when attacking host before biting  a _{ 1 }  .05 ^{ 6 }  
Probability killed when attacking host after biting (excluding mortality from insecticide treatments)  a _{ 2 }  .05 ^{ 6 }  
Probability becomes infected with malaria when biting infectious human host^{4}  M  1.00  
Number of eggs laid per successfully laying mosquito per cycle  L  100 ^{ 2 }  eggs 
Time, measured in whole units equal to length of gonotrophic cycle, from infection of mosquito to cycle from which mosquito gives infectious bites  D  3^{ 3 } Based on 10.78 ^{1} days  cycles 
Baseline probability that mosquito contacts and is killed by conventional instantkill chemical insecticide (CC) whilst resting after biting human host  k  0 for cases not assessing use of CC  
0.8 for cases assessing use of CC  
Baseline probability that mosquito contacts and is affected by delayed action pesticide whilst resting after biting human host  X  0 for cases not assessing use of delayed action pesticide  
0.8 for cases assessing use of delayed action pesticide  
Number of age classes included in analysis  λ  10  cycles 
The probability that a mosquito contacts and is affected (killed or infected) by a conventional or biological insecticide after biting a human host is input as a single ‘coverage’ value, incorporating the probabilities of being in a treated property, of contacting the pesticide, and of being affected by the pesticide during contact. It is assumed that physical constraints on the proportion of surfaces and internal areas treated will apply equally to conventional and fungal insecticides, and that for mosquitoes contacting treated surfaces, biopesticides can potentially offer rates of infection equivalent to the rates of mortality generated by conventional insecticides. The latter assumption is supported by field trials showing >86% infection of mosquitoes entering outdoor bait boxes [36], 76% infection in experimental huts with fungusimpregnated eave curtains [13], and laboratory trials showing >95% infection from treated clay pots [14] or exposure to treated clay tiles [11].
This provides the basis for the evaluation of relative fitness using a comparison of values for φ, lifetime egg production, representing LRS, $\phi ={\displaystyle \sum _{i=1}^{\lambda}{F}_{i}}{V}_{i}$.
Comparative levels of transmission control are assessed using u, the average number of infectious bites per mosquito lifetime, $u={\displaystyle \sum _{i=1}^{\lambda}{I}_{i}{V}_{i}}$.
The average number of infectious bites during cycle i per mosquito surviving to the beginning of cycle i, I_{ i }, is calculated as
${I}_{i}=\frac{{\displaystyle \sum _{m=D}^{i1}{\displaystyle \sum _{l=0}^{i1}{q}_{i,m,l,2}{v}_{i,m,l}+{q}_{i,m,l,3}{v}_{i,m,l}}}}{{V}_{i}}$i > D
The population model
The PM tracks susceptible and resistant phenotypes over a sequence of time periods for a population subject to a given vectorcontrol treatment. The key outputs, calculated for each time period, are the proportion of the population with resistant and susceptible phenotypes and the overall reduction in infectious bites across the population compared to a susceptible population with no vectorcontrol treatment.
Variables and parameters for the population model
Variable or Parameter  Symbol  Comments & Constraints 

Period number (periods over which the population is tracked)^{*}  n  0<n 
Dominance of resistance allele  d  dominant d = 1 
recessive d = 0  
Genotype (normal allele s, resistant allele r)  g  (s,s) g = 1 
(s,r) g = 2  
(r,r) g = 3  
Proportion of total population having genotype g at start of period n  G _{ g,n }  
Proportion of the population resistant at start of period n  R _{ n }  
Average number of infectious bites per mosquito in population in period n  M _{ n }  
Size of initial population (susceptibles in the presence of treatment) as proportion of base population (susceptibles without treatment)  J  value from FCM 
Population size in period n as proportion of initial population size  W _{ n }  
Average infectious bites during one time period from an untreated population  q  value from FCM 
Number of infectious bites from treated population during time period n, expressed as a % of the number of infectious bites during one time period from a susceptible population without treatment,  Q _{ n }  Chosen measure of control 
Number of periods between egglaying and adult emergence  Φ  Input 
Values used in the population model for this analysis
Variable or Parameter  Symbol  Value 

Proportion of total population having genotype g at start of period 1  G _{ g,1 }  G_{ 1,1 }= 1G_{ 2,1 } 
G_{ 2,1 }= 10^{9}  
G_{ 3,1 } = 0  
Dominance of resistance allele (0=recessive, 1=dominant)  d  d = 1 
Number of periods between egglaying and adult emergence  Φ  3 
Fitness factor for males with genotype g  f _{ g }  f_{ 1 }=f_{ 2 }=f_{ 3 }= 1.00 
A detailed derivation of the model is given in Additional file 2: Appendix B. In brief, the PM works in discrete time periods, each equivalent to the length of one gonotrophic cycle, with recruitment of newly emerged adult mosquitoes treated as occurring at the start of each time period. For each sequential time period, the proportion of the population comprised by each genotype in each age class is calculated, reflecting the genotypes of new adult recruits and the survival of adults in each age class from the preceding period. This is then used to calculate the proportion of the total population in time period n with homozygous recessive (G_{ 3,n }) and heterozygous (G_{ 2,n }) genotypes, from which R_{ n } is calculated, the proportion of the population with a resistant phenotype in period n, with R_{ n } = G_{3,n} + G_{2,n}d. Dominance is actioned by the value of d, which is 0 when resistance is assumed recessive, and 1 when it is assumed to be dominant.
Results from the FCM are used by the PM to calculate the average number of infectious bites per mosquito in the population during each time period. From this Q_{ n }, the number of infectious bites given by the population as a whole relative to those given by an untreated population, can be calculated for each time period as ${Q}_{n}=\frac{{M}_{n}{W}_{n}J}{q}$.
Assumptions
The model does not attempt to capture the effects of mutational processes or stochastic demographic effects on the origin and initial spread of very low numbers of resistance alleles, and so it is assumed that resistant phenotypes are already established at a low frequency in the population at the start of the analysis. Resistance involves a single gene and a simple dominant/recessive process. Moreover, it is assumed that the size and age structure of the population at the start of the PM analysis is that achieved after sustained use in a susceptible population of the insecticide being evaluated, that there is no immigration or emigration, and the proportion of each genotype in the new adults joining the population matches that in the eggs from which they originate. Density dependence is assumed to occur at the mosquito larval stage and the number of adult mosquitoes recruited to the population per unit of time remains constant.
All model parameters are ageindependent, apart from background mortality rates and the action of agelinked pesticides, with incremental mortality from fungal biopesticide infection varying according to the number of days since infection. Conventional insecticides affecting a susceptible individual are assumed to be instantly fatal. Mosquitoes choose human hosts at random, and the model does not capture feedback between numbers of infectious bites and the proportion of human hosts with infectious malaria. Malariainfected mosquitoes never become uninfected. All feeding cycles are of equal duration and mosquitoes bite once in each cycle. All eggs laid are of equal quality and viability. The analysis assumes that malaria infection produces no effects on behaviour, background mortality or fecundity in infected mosquitoes, and fungusinfected mosquitoes that survive and lay eggs are assumed to lay as many eggs at each laying event as uninfected individuals.
Mosquitoes are assumed to contact the chemical or biopesticide when resting after biting a human host, reflecting an application method essentially consistent with IRS. Avoidance behaviour such as outdoor feeding and outdoor resting is not reflected in the coverage values for susceptible mosquitoes since it comprises a method of resistance.
Analysis
Fungal biopesticides can also impact mosquito feeding propensity and flight capacity in the days before mosquito death [11]. A mosquito which no longer attempts to feed or to lay eggs is effectively dead from the perspectives of fitness and disease transmission. For the purpose of the model therefore, ‘mortality’ encompasses cessation of feeding and reproduction, as well as actual death.
Results
Coverage and virulence
For equivalent reductions in EIR, selection for resistance is best minimized by high coverage with late initiation day, high mortality rate biopesticides. For example, the lowest selection coefficient associated with a 90% RAIB at 80% coverage is 21%, with day 9 initiation and a 91% mortality rate. At 50% coverage the lowest selection coefficient available in combination with 90% RAIB is 40%.
Repellency
Malaria interactions
Discussion
Although high coverage offers scope to use less virulent fungal strains to reduce EIR, for given virulence parameters, higher levels of coverage also generate stronger selection for resistance, for both conventional and biopesticide interventions. Remembering that the biopesticides must be considered in relation to the best currently used approaches, it is interesting to note that in relative terms, the benefits of biopesticides versus conventional instantkill insecticides are maximized at high coverage for both transmission control and resistance management (Figure 6).
The relative importance of initial control versus product lifespan depends on a large number of factors, including the availability of alternative replacement treatments, the meaning in terms of human morbidity and mortality of a smaller reduction in EIR at the outset, and the realities of public budgets and other resources. The relative costs and benefits also change if the biopesticide is being considered for use as part of a combination treatment with other interventions [34, 39, 40]. There is, therefore, no simple mathematical optimum for the many possible virulence schedules; the many possibilities need to be considered in context. In so far as it can be done without compromising transmission control, however, it is clearly beneficial to choose the biopesticide that generates the lowest selection for resistance in a particular context. For resistance management, the aim should be to achieve high levels of coverage, allowing less virulent fungal strains to achieve a given level of control, and maximizing their resistance management benefits over instantkill insecticides.
Even strains sufficiently virulent to match the transmissionreducing characteristics of conventional instantkill chemical insecticides at matching coverage levels still offer a small benefit in terms of the rate of spread of resistance (Figure 7). Such a resistance management gain would be enhanced by any fitness costs associated with resistance [8].
The conclusions presented here are independent of the method of resistance (e.g., metabolic or behavioural), provided resistance is genetically determined. It is assumed however that resistance is a binary quality, with mosquitoes either experiencing the full effects of a control measure, or remaining completely unaffected by it. The analysis of the speed of spread of resistance here thus assumes that susceptible mosquitoes experience infections with the specified virulence characteristics, and that resistant mosquitoes have no fungal mortality. In reality, it is more probable that a resistance/tolerance process would operate, with resistant mosquitoes still becoming infected, but experiencing a lower mortality rate than fully susceptible individuals. The spread of resistance would therefore effectively comprise a reduction in fungal virulence, rather than a complete loss of control. Considering the results presented in Figure 4, for example, this would mean that the spread of resistance to the highest virulence biopesticides, rather than comprising a steep function to complete resistance and total loss of transmission control, would move to the curves calculated for sequentially less virulent strains, as resistance converts high virulence strains to low virulence strains, offering even more beneficial resistance management possibilities. Future analyses could explore the impact of hypothetical resistance mechanisms that might operate with respect to conventional and fungal pesticides. The analyses presented here could also be extended to evaluate the impact of malaria infection on mosquito survival, fecundity and behaviour and variation in fecundity with mosquito age.
Certain widely used pyrethroid insecticides have high contact repellency, with studies suggesting that around 50% of mosquitoes landing on treated surfaces may leave before acquiring a fatal dose [23–25, 38]. Whilst this potentially enhances the impact of pyrethroidtreated bed nets on transmission by deflecting mosquitoes away from protected humans before they bite, for IRS it results in mosquitoes surviving to potentially transmit malaria in later feeding cycles [24]. Thus, for this group of conventional insecticides, the composite ‘coverage’ value at a given level of spray cover, would be half that for biopesticides, and could never be greater than 50%. Comparing biopesticide performance with that of a conventional insecticide, and assuming 50% contact repellency (Figure 8) across a full range of coverage values, fungi better reduce transmission than pyrethroid IRS, while still maintaining some resistance management benefits. This suggests that, for all spray coverage values, suitably virulent fungal strains might provide a better option for IRSbased vector interventions than contactrepellent pyrethroids. If only low levels of spray coverage are achievable, replacing repellent pyrethroids with highvirulence fungal treatments could significantly improve the achievable EIR reduction, without significantly increasing selection for resistance, which is in any case relatively weak at low coverage (Figure 8). Where high spray coverage is achievable, replacing pyrethroids with relatively lowvirulence fungal treatments could give improvements in both transmission control and resistance management, since the relative fitness of susceptible mosquitoes would be potentially doubled.
The analysis shows that in all cases, having higher fungalinduced mortality in malariainfected mosquitoes than in uninfected mosquitoes minimizes the fitness costs associated with a given reduction in transmission (Figure 9). The ideal biopesticide from the resistance management perspective would be one that had little or no impact on mosquitoes not infected with malaria, but was strongly virulent in malariainfected individuals. This might be possible since malaria infection can impose significant metabolic and immunological challenges to mosquitoes [41–44]. There is only a minimal tradeoff between transmission control and resistance management in malarialinked incremental biopesticide mortality. By changing the fitness cost to the mosquito of malaria infection, pesticides working in this way might also exert selection in favour of vector resistance to malaria, further enhancing the transmissioncontrol benefits from the intervention. Strain selection or genetic modification should ideally target this trait. A further development of this principle would be fungal strains which specifically block development of the malaria parasite in the mosquito, or simply act as a delivery mechanism for antimalaria interventions in the mosquito host (‘paratransgenesis’ [10, 37]), with minimum survival or fecundity costs to the mosquito. It must be noted, however, that this potentially moves selection for resistance from the mosquito to the malaria parasite, which has so far proved extraordinarily adept at evolving its way out of trouble.
Conclusions
This analysis shows that fungal biopesticides have the potential to significantly reduce EIR while imposing only weak selection for resistance. There is always a tradeoff between the magnitude of the initial reductions in transmission and maintaining those reductions in the longer term. Given the severe human and economic consequences of malaria transmission, choosing an intervention which does not maximally reduce transmission at the outset requires very careful justification. However, the analyses presented here show that fungal biopesticides can offer equivalent or better reductions in transmission than existing interventions in both the short and long term. This is especially true where existing conventional chemical pesticides have high contact irritancy or resistance to them has already begun to spread. The theoretical analyses presented here should help define the vector mortality profiles required to maximize the sustained malaria control potential of fungal biopesticides, or indeed other novel biological or chemical insecticides.
Abbreviations
 AIB:

Average number of infectious bites per mosquito per lifetime
 EIR:

Entomological inoculation rate, the number of infectious bites per person per unit of time
 FCM:

Feeding cycle model
 IRS:

Indoor residual spraying
 LLA:

Latelife acting
 LRS:

Lifetime reproductive success
 PM:

Population model
 RAIB:

Proportionate reduction in average number of infectious bites per mosquito per lifetime, compared to the number for untreated susceptible mosquitoes.
Declarations
Acknowledgements
We thank S Blanford for beautiful survival curves, members of the Research and Policy in Infectious Disease Dynamics Program of the Science and Technology Directorate, Department of Homeland Security and the Fogarty International Center, National Institutes of Health for discussion, and T Ayerst for patience, encouragement and support. This work was supported by a grant (R21 AI088094) from the National Institute for Allergy and Infectious Disease.
Authors’ Affiliations
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