Host ploidy, parasitism and immune defence in a coevolutionary snail–trematode system

Authors


Erik E. Osnas, Department of Wildlife Ecology, University of Wisconsin, 209 Russell Labs, 1630 Linden Drive, Madison, WI 53706, USA.
Tel.: 608 262 1984; fax: 608 262 6099;
e-mail: osnas@wisc.edu

Abstract

We studied the role of host ploidy and parasite exposure on immune defence allocation in a snail–trematode system (Potamopyrgus antipodarum-Microphallus sp.). In the field, haemocyte (the defence cell) concentration was lowest in deep-water habitats where infection is relatively low and highest in shallow-water habitats where infection is common. Because the frequency of asexual triploid snails is positively correlated with depth, we also experimentally studied the role of ploidy by exposing both diploid sexual and triploid asexual snails to Microphallus eggs. We found that triploid snails had lower haemocyte concentrations than did diploids in both parasite-addition and parasite-free treatments. We also found that both triploids and diploids increased their numbers of large granular haemocytes at similar rates after parasite exposure. Because triploid P. antipodarum have been shown to be more resistant to allopatric parasites than diploids, the current results suggest that the increased resistance of triploids is because of intrinsic genetic properties rather than to greater allocation to defence cells. This finding is consistent with recent theory on the advantages of increased ploidy for hosts combating coevolving parasites.

Introduction

Increases in the number of genome copies (polyploidy) have been common in animals and plants, and often lead to changes in the phenotype (Otto & Whitton, 2000). For example, polyploids are often larger, and they are more likely to be asexual than are diploids (Otto & Whitton, 2000). Less obvious changes include genetic effects, such as increased masking of deleterious mutations and increased heterozygosity in polyploids (Otto & Whitton, 2000), along with changes in the effects of selection on allele frequencies (Orr & Otto, 1994).

Host–parasite interactions are not immune to the effects of increased ploidy, because host–parasite interactions are often characterized by tight genetic specificity (Lively, 1989; Ebert, 1994) or phenotypic specificity (Clayton et al., 1999). Under tight genetic specificity, increased ploidy could either increase or decrease resistance of hosts to parasites, depending on how alleles interact between hosts and parasites (Nuismer & Otto, 2004). However, when increased heterozygosity in the host makes it more difficult for the parasite to evade the host's recognition mechanisms, then polyploidy would tend to increase host resistance (Nuismer & Otto, 2004). Changes in physiological processes with increased ploidy could also change patterns of resistance if allocation to defence mechanisms were affected. For example, increases in ploidy often increase cell size, which can affect physiological processes that depend on membranes (Otto & Whitton, 2000). Therefore, any cellular effect on defence cells caused by shifts in ploidy could lead to changes in resistance.

In animals, comparative studies between sexual and asexual animals are often complicated by the fact that the asexuals are often also polyploid, while sexuals are often diploid (Otto & Whitton, 2000). This coupling of reproductive mode and ploidy level is also a problem for comparative studies that examine the relationship between reproductive mode and parasite prevalence. For example, in studies of the New Zealand freshwater snail, Potamopyrgus antipodarum, diploid sexual snails are more common in lakes where the prevalence of trematode parasites is high; and triploid asexual snails are more common in lakes were the prevalence of infection by trematodes is low (Lively, 1987, 1992; Lively & Jokela, 2002). The same kind of association occurs within one well-studied lake: diploid sexual snails are more common in the shallow-water margins of the lake, where parasites are common; and triploid asexual snails are more common in deeper habitats where prevalence of infection is relatively low (Jokela & Lively, 1995a; Fox et al., 1996). The basic results are consistent with the Red Queen hypothesis for the maintenance of sexual reproduction (Jaenike, 1978; Hamilton, 1980; Bell, 1982; Hamilton et al., 1990); but because reproductive mode and parasite prevalence are confounded with ploidy in this system, the association between sexual reproduction and parasite prevalence could be due to factors associated with ploidy, rather than to any inherent advantages of sexual reproduction as a mechanism for overcoming coevolving parasites. Life-history traits have not been found to differ between diploids and triploids (Jokela et al., 1997a,b; Lively et al., 1998), but triploids are more resistant to allopatric parasites than are diploids (Lively et al., 2004). The greater resistance of triploid snails to allopatric parasites could be due to either (i) increased heterozygosity at highly specific self/non-self loci (Nuismer & Otto, 2004), or (ii) increased defence-cell allocation by triploids. To investigate these hypotheses, we first examined haemocyte concentrations along a depth gradient of Microphallus infection in the field. We then conducted a laboratory experiment in order to decouple the effects of parasite exposure and ploidy on defence-cell concentration. We chose haemocyte number as our measure of defence, as resistance is correlated with haemocyte concentration in a Drosophila-parasitoid interaction (Kraaijeveld et al., 2001).

Methods

Field observations

The snail P. antipodarum was collected from Lake Alexandrina, South Island, New Zealand, in January 2002. Snails were collected along a transect from three distinct habitats: (i) willow roots along the shore, (ii) Isoetes vegetation at 0.5–2.0 m depth and Elodea at 4.0–6.0 m depth (see Jokela & Lively, 1995a; Fox et al., 1996). All collected snails were stored in plastic containers at the Edward Percival Field Station. Water in the containers was replaced every other day, and the snails were feed dried Spirulina algae ad libitum.

Approximately 30 individuals from each collection site were assayed for one type of defence cell (haemocytes). We measured haemocyte concentration by first drying the snail with a paper towel. We then tapped on the operculum with the tip of a P10 Pipetteman pipette until haemolymph was expelled through the haemal pore (Amen et al., 1991; Rigby & Jokela, 2000; Russo & Lagadic, 2000). We then used the pipette to draw up the expelled haemolymph. This technique usually resulted in 1–2 μL of haemolymph. The haemolymph was then expelled onto the grid surface of a Bright–Line Hemocytometer; a cover slip was placed over the haemolymph, and the number of haemocytes in 0.1 μL was counted under a compound microscope at 400× magnification. After completion of the haemocyte count, the length of the snail was measured. The snail was then dissected under a dissecting microscope in order to determine its gender, count the number of brooded embryos, and to determine whether of not the snail was infected by trematodes.

Haemocyte induction and ploidy

Parasite culture and experimental design

To determine whether the immune defence could be induced, the snail host was exposed to eggs of the trematode parasite Microphallus sp. Microphallus sp. is a widespread and common trematode parasite of P. antipodarum in New Zealand. Genetic data show that Microphallus sp. is a single species with little genetic structure across New Zealand's South Island (Dybdahl & Lively, 1996). Microphallus-infected snails were collected from the willow root habitat of Lake Alexandrina (South Island, New Zealand) in February 2003, and transported under permit to the lab at Indiana University. Two mice were each fed the metacercariae from 30 infected snails, and two mice were kept for controls. Even though the natural host are waterfowl, the parasite can reproduce with common laboratory mice (Lively & McKenzie, 1991). Twenty-four hours after infected snails were fed to mice, we began collecting the mouse faecal pellets twice a day for 4 days. At collection, both control and parasite faeces were separately placed into 4 L of pond water to leach out soluble waste. The pond water was changed every day. Five days after the last faeces collection, excess pond water was decanted off, and diluted up to 1 L. This faeces–water mixture was then evenly distributed among replicate containers within each treatment using a serological pipette.

The host snails used for this experiment were collected from the Isoetes habitat zone of Lake Alexandrina in February 2003. Snails from this habitat are comprised of a mix of diploids and triploids (Fox et al., 1996). We placed 100 snails into each of 22 containers with 2 L of pond water. The egg-containing faeces were evenly distributed into 11 of these containers, and the control faeces were evenly distributed in the remaining 11 containers.

Data collection

Starting 14 days after exposure to parasites, haemocytes were counted from 500 snails randomly chosen across all containers. Haemocytes were sampled as described above. Two classes of haemocytes, based on size and morphology, were counted using a compound microscope at 400 total magnification. The first size class (Type 1) included all haemocytes ≤10 μm without visible pseudopodia. The second class (Type 2) included all haemocytes with pseudopodia that were also granular in appearance. Most cells were in one of these two categories. After the haemolymph sample had been counted, length of the snail was measured, sex was determined, and the snail was dissected under a microscope in order to count the number of embryos and to record presence of parasites. The head of the snail was then frozen in liquid nitrogen, and stored at −80 °C. The remaining snails were examined 5 months after the initial exposure to parasites. After measuring length, the snails were dissected in order to count number of brooding embryos, determine sex and record the presence of parasites.

Ploidy determination

Flow cytometry was used to determine DNA content of snail nuclei. DNA content is highly correlated with ploidy, as triploids are expected to contain 50% more DNA than do diploids. The procedure for ploidy determination was modified from Basic Protocol 2 in Darzynkiewicz & Juan (1997). Frozen snail head tissue (∼10 mg) was ground in 100 μL of ice-cold citrate/DMSO buffer (0.25 m sucrose, 40 mm trisodium citrate dihydrate, 0.5% dimethyl sulfoxide). A 750-μL volume of propidium iodide solution containing spermine (20 mg propidium iodoide and 116 mg spermine tetrahydrochloride in 100 mL detergent stock solution: 3.4 mm trisodium citrate dihydrate, 0.1% Nonidet P-40, 1.5 mm spermine tetrahydrochloride and 0.5 mm Tris) was then added to the ground tissue and incubated >5 min at room temperature. A DNA content standard (∼3.5 × 105 trout erythrocyte nuclei: 35 μL of a two-fold dilution of Biosure TEN, product number 1007) was then added to the tissue/dye solution, and the sample was filtered through two layers of Miracloth (EMD Biosciences, Inc., La Jolla, CA, USA). Samples were then stored on ice for ≤2 h until flow cytometry could be performed. DNA content was measured on a FACScan flow cytometer (Becton-Dickinson, Franklin Lakes, NJ, USA). At least 5000 cells were measured for each sample. Average DNA content of each sample was determined as: median fluorescent intensity of snail nuclei/median fluorescent intensity of trout nuclei × trout nuclei mass in picograms. The trout standard was assumed to be 2.80 pg nuclei−1.

Statistical analysis

Generalized linear models (McCullagh & Nelder, 1989) were used to model mean haemocyte count in relation to experimental and observational variables and to model mean brood size in relation to habitat. For all analyses, a Poisson error distribution was assumed because the data were counts, and the variance increased with the mean. An overdispersion parameter was also fit because the variance of the response increased faster than unity as assumed in the Poisson model. Overdispersion was estimated by the deviance divided by the d.f. method (SAS, 2002). A log link function was used to relate the mean of the response (count data) to the predictor (covariates). All confidence intervals were calculated from the profile of the likelihood function. For the field observational data, a model was fit that contained parameters for the main effects of habitat, brood size, and shell length. Brood size and shell length were fit because these vary significantly among habitats, and it was thought that they could affect haemocytes counts in addition to parasite exposure. Other models that contained interaction terms were explored, but the fit was not improved over the simpler model [based on Akaike information criterion, AIC (Burnham & Anderson, 1998; Johnson & Omland, 2004)]. For the induction experiment, parasite treatment, ploidy, and an interaction between treatment and ploidy were used to test for haemocyte induction and differences between diploids and triploids. The model was fit separately to Type 1, Type 2 and total haemocyte count. Individual snails were treated as the within-container effect in a repeated measures model. Significance for all parameters was determined using Type III analysis, and significance of whole models was determined by the likelihood ratio test. All model fitting was done in SAS version 9.1 (SAS, 2002).

Results

Field observations

The generalized linear model significantly explained variation in haemocyte count in Lake Alexandrina (P < 0.001, Table 1). Habitat and brood size were significantly related to haemocyte count (P < 0.001), but shell length was not significant (P = 0.44, Table 1). Haemocyte count was greatest in the shallow water habitat, and then decreased in the Isoetes and deep habitats (Fig. 1a), indicating that haemocyte count is greatest in the same habitats where parasite prevalence in greatest (Jokela & Lively, 1995b). Brood size was also related to habitat, such that snails from the deep and Isoetes habitats had higher brood sizes than did snails from the shallow-water habitat (P < 0.001, Fig. 1b). Over all habitats, haemocyte count was positively correlated with brood size (P < 0.001, Fig. 2).

Table 1.  Generalized linear model for haemocyte count in relation to lake habitat, brood size and shell length.
Parameterd.f.EstimateSE95% Confidence limitsChi-squaredP-value
  1. A Poisson error distribution with a log link function and a parameter for overdispersion (the Scale parameter) was used to model haemocyte count. Confidence intervals are based on the likelihood profile. For the habitat parameter, Shallow is the reference category (the intercept) and estimates for Isoetes and Deep are differences from Shallow.

Intercept14.2660.6003.049 to 5.40450.60<0.001
Habitat2   19.24<0.001
 Deep −0.8410.270−1.379 to −0.316  
 Isoetes −1.1620.275−1.714 to −0.633  
Length10.0110.014−0.017 to 0.0390.600.438
Brood Size10.0330.0090.015 to 0.05112.40<0.001
Scale08.527    
Figure 1.

The natural logarithm of (a) haemocyte number and (b) the number of brooded embryos in relation to habitat type in Lake Alexandrina. Error bars are ±2 SE, as estimated in the model from Table 1.

Figure 2.

A bivariate plot of ln-transformed haemocyte number against the number of brooded embryos for snails collected along a depth gradient in Lake Alexandrina.

Haemocyte induction and ploidy

Based on genome size as determined by flow cytometry, diploid and triploid snails were clearly distinguishable (Fig. 3). Fifty-seven (14%) of the snails were diploid and 407 (86%) were triploid. Diploids had an average nuclear genome size of 0.86 ± 0.006 (1 SE) pg nucleus−1, and triploids had an average genome size of 1.31 ± 0.005 pg nucleus−1.

Figure 3.

Histogram of nuclear size (pg nucleus−1) from Potamopyrgus antipodarum. The data fall into two groups that represent diploid and triploid individuals.

Containers to which trematode eggs were experimentally added had higher levels of infection (48.8% ± 0.13, 1 SE) than control containers (2.2% ± 0.02, t = 11.70, d.f. = 11, P < 0.001), indicating that the exposure protocol was successful. For Type 1 haemocytes, neither ploidy (P = 0.65), exposure (P = 0.35), nor the interaction between ploidy and exposure (P = 0.57) were statistically significant factors affecting haemocyte count (Table 2, Fig. 4). For Type 2 haemocytes, however, triploids had a lower count than did diploids (P = 0.01), and there was a significant increase in haemocyte number in the parasite exposure treatment (P = 0.05). The interaction between ploidy and exposure was not significant (P = 0.83), indicating that both diploids and triploids increased Type 2 haemocytes to similar levels after parasite exposure (Table 2, Fig. 4). For total haemocyte count, triploids had a lower haemocyte counts than did diploids (P < 0.05), but parasite exposure was not a significant factor (P = 0.08). The interaction between ploidy and exposure was also not significant (P = 0.89, Table 2).

Table 2.  For all analyses, the estimates of effects use triploids and exposed treatments as the reference population (the intercept in the model); Therefore, parameter estimates for Ploidy refer to diploids and parameter estimates for Exposure refer to the no parasite treatment.
Parameterd.f.EstimateSE95% Confidence limitsChi-squaredP-value
Response: Type 1 haemocytes
 Intercept13.6270.1163.399 to 3.85531.18<0.001
 Exposure1−0.1740.133−0.436 to 0.0870.870.35
 Ploidy1−0.0130.196−0.397 to 0.3720.210.65
 Exposure × ploidy10.1280.222−0.308 to 0.5640.330.57
 Scale06.091    
Response: Type 2 haemocytes
 Intercept13.4130.0923.232 to 3.59437.02<0.001
 Exposure1−0.2870.119−0.521 to −0.543.880.049
 Ploidy10.4830.1520.186 to 0.7816.650.010
 Exposure × ploidy1−0.0490.227−0.494 to 0.3960.050.829
 Scale05.202    
Response: total haemocytes
 Intercept14.2190.1004.023 to 4.41542.21<0.001
 Exposure1−0.2230.112−0.442 to −0.0043.020.082
 Ploidy10.2400.143−0.041 to 0.5205.490.019
 Exposure × ploidy10.0220.164−0.300 to 0.3440.020.894
 Scale07.112    
Figure 4.

Ln-transformed haemocyte count (±2 SE) for Type 1 (a), Type 2 (b) and Total haemocytes (c), in relation to parasite treatment and ploidy. CON, control snails; EXP, experimental snails. The black bars represent diploid individuals; and the grey bars represent triploid individuals.

Discussion

Polyploids often differ morphologically and physiologically from their diploid relatives (Otto & Whitton, 2000). Plants polyploids are often larger, have larger cells, inhabit extreme environments, and differ in physiology; animal polyploids often inhabit the edges of species’ range and are asexual (Otto & Whitton, 2000). For the relation between immune function and ploidy, only fish have been studied. Polyploid goldfish have lower resistance and lower phagocytic activity than diploids (Hakoyama et al., 2001), and polyploid salmon also have lower immune activity than diploids (Langston et al., 2001). In contrast, triploid hybrids in trout are more resistant to a virus, and they have lower antibody production against the virus when compared with nonhybrid diploids (LaPatra et al., 1996).

In the present study, we found that defence-cell (haemocyte) concentrations followed a depth-related cline in a natural population of the snail P. antipodarum (Fig. 1a). Shallow-water snails, which are mostly diploid sexual individuals, had more haemocytes than snails collected from deeper habitats where triploid asexuals are more common (Fig. 1). We also found that haemocyte production could be experimentally induced by exposing the snails to parasite eggs. Nonetheless, triploid snails had lower concentrations of defence cells than diploid snails, whether or not the snails were exposed to parasite eggs in the lab (Fig. 4). Thus our observation that shallow-water snails have more haemocytes than deep-water snails could be explained by the fact that diploids are more common in the shallow water and they produce more haemocytes. In addition, infection levels are greater in the shallow water; so there may be more exposure to parasites in that area, which could further increase the number of haemocytes per snail.

The induction of host defence (haemocyte count) in the lab depended on the type of haemocyte. The number of Type 1 haemocytes, which are smaller and do not possess pseudopodia, was not affected by parasite exposure or host ploidy. The number of Type 2 haemocytes, which are larger, granular, and usually have visible pseudopodia, increased with parasite exposure and deceased with host ploidy. Type 2 haemocytes are presumably capable of the defensive functions in the host such as phagocytosis and encapsulation of foreign material because of their increased cellular complexity (Vanderknaap & Loker, 1990; Loker, 1994; Bayne et al., 2001). Therefore, even though very little is known about the functions of haemocytes in this system, the induction of only these larger haemocytes suggests that in fact this is in response to increased defensive needs. It must be stated, however, that haemocyte count is a very crude measure of immunity and that there are many other measures that might better measure the immune response in these snails, which we had no success developing. In addition, multiple simultaneous immune measures may be required to assess total ‘immunity’ of an animal (Adamo, 2004).

In a previous study, we found that triploid snails were more resistant than diploid snails to allopatric sources of parasites (Lively et al., 2004). Thus, the results of the present study suggest that the greater resistance of triploid snails to allopatric parasites is not a result of a greater investment in haemocytes, or to a lower threshold for inducing the production of these cells in response to parasite exposure (we cannot, however, rule out the possibility that triploidy is associated with an increase in other types of defence allocation). Although not directly tested, the results are consistent with a recent model on the evolution of ploidy in host–parasite systems, which posits an advantage to increased heterozygosity at host loci involved in parasite recognition (Nuismer & Otto, 2004). According to this model, polyploids would be expected to be more resistant than diploids to coevolving parasites due to a greater ability to detect genetically encoded antigens associated with nonself tissue. Whatever the mechanism for the greater resistance by triploids to allopatric parasites, it would appear that the advantage to triploid genomes is short-lived, as triploid snails are not more resistant to coevolving sympatric populations of parasites than are diploid snails (Lively et al., 2004). In other words, coevolving parasites appear to eventually overcome any inherent genetic advantages associated with host polyploidy. This finding suggests that there may be an initial advantage to increases in host ploidy when rare, but the advantage might become diminished over coevolutionary time.

Acknowledgments

This manuscript was improved by comments from L. Rieseberg, E. Brodie and G. Demas. This work was funded by grants from the Center for the Integrative Study of Animal Behavior at Indiana University to EEO and by NSF grants DEB-9904840 and DEB-0128510 to CML.

Ancillary