Journal of Heredity Advance Access published online on April 2, 2007
Journal of Heredity, doi:10.1093/jhered/esm008
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Variation and Heritability in Immune Gene Expression by Diseased Honeybees
United States Dept. Agriculture - Agricultural Research Service Bee Research Laboratory, BARC-East Building 476, Beltsville, MD 20705
Address correspondence to Jay D. Evans at the address above, or e-mail: jay.evans{at}ars.usda.gov.
Social insects are frequent targets for pathogens and have consequently evolved diverse ways to minimize disease impacts, one of which is the innate immune response. Here, a 4-generation mating scheme was carried out to assess heritability and variation in a honeybee (Apis mellifera) immune trait, the production of the key antimicrobial peptide abaecin. Larval offspring from controlled crosses (n = 576 offspring from 36 singly inseminated queens) as well as offspring of field colonies (896 individuals in 53 colonies) were challenged individually with a widespread bee pathogen, the gram-positive bacterium Paenibacillus larvae. After bacterial challenge, transcript levels for the gene encoding abaecin were quantified and then compared using known pedigrees and colony environments. Considerable variation among highly related siblings (r = 0.75) indicates that subtle allelic differences in immune pathway genes can have large effects on transcriptional profiles. Abaecin levels were moderately heritable (h2 = 
0.3–0.4), reflecting high amounts of standing genetic variation, and suggesting that this and other immune traits are amenable to selective programs aimed at improving honeybee health. The results help efforts to determine the relative effectiveness of social versus individual defenses by social insects toward their pathogens.
Understanding the mechanisms and efficacy of immunity remains a central goal in fields ranging from ecology to evolutionary biology, genetics, medicine, and agriculture (Hultmark 2003; Rolff and Siva-Jothy 2003). Immune responses can be either adaptive (and often antibody driven) or innate. Whereas true adaptive immunity is thought to be restricted to select vertebrate species (but see Kurtz 2004; Watson et al. 2005, for suggestions of analogous processes in invertebrates), innate immunity is nearly universal in the eukaryotes (Beutler 2004). Innate immune responses effectively target diverse natural pathogens, acting as a defense even in species possessing adaptive immunity.
Among insects and other arthropods, innate immunity is known to play an important role in reducing pathogen loads (Lycett and Kafatos 2002; Tzou et al. 2002; Shin et al. 2003). It is then curious that substantial genetic variation for immune responsiveness exists across invertebrate species (Cotter and Wilson 2002) because strongly adaptive traits are expected to have narrow levels of genetic variation. This puzzling variation has been explored using 2 major classes of hypotheses: those involving host–parasite dynamics such as ongoing directional or frequency-dependent selection toward pathogens and those involving host-level tradeoffs such as high costs involved with immune activity (Armitage et al. 2003; Cotter et al. 2004). A mechanistic, genetic, approach is one way to determine whether variation in immune responsiveness reflects primarily host tradeoffs or host–parasite covariation. Despite tremendous success in determining the genetic architecture of insect immunity (Hoffmann 2003; Wells et al. 2003), little is known about how variation in immune pathway members translates into phenotypic variation in immune responsiveness.
Honeybees, Apis mellifera, live at high density in a nest environment filled with resources that might be exploited by parasites and pathogens (Morse and Flottum 1997). Honeybees use both behavioral and physiological defenses against potential disease agents. For example, both hygienic behaviors by adults and larval resistance traits can mitigate infections of the gram-positive bacterium Paenibacillus larvae larvae. This bacterium is the causative agent responsible for American foulbrood (AFB), a widespread larval disease of honeybees (Shimanuki 1997). Whereas 1–2% of honeybee colonies inspected in the United States show signs of AFB, the frequency of P. l. larvae spores and latent infections is at least 10-fold higher (Smith IB and Evans JD, unpublished data). Presumably, disease rates of this bacterium are reduced in part through evolved resistance by their honeybee hosts (Evans 2004).
Previous work indicates genetic variation in the ability of larval honeybees to tolerate infections by P. l. larvae. First, Rothenbuhler and Thompson (1956) described significant variation across commercial strains of honeybees in terms of larval susceptibility to P. l. larvae and showed that this trait might be enhanced through artificial selection. Second, Palmer and Oldroyd (2003) recently found evidence that larvae from a specific patriline within the same colony were more likely than others to succumb after inoculation with P. l. larvae. Finally, Evans (2004) found substantial interindividual variation in expression of 2 immune response effectors, abaecin and defensin, on exposure of bees to P. l. larvae. Transcript levels for abaecin, and not defensin, appear to be correlated with colony-level disease resistance (Evans and Pettis 2005).
Here we use a controlled honeybee mating scheme to introduce, via haploid males, genetic variation in disease resistance into a homogenous maternal background. We challenged offspring from these crosses along with their relatives in order to quantify the genetic bases of variation in expression levels of the gene encoding the antimicrobial peptide abaecin. Using pairwise comparisons across a known pedigree, as well as restricted maximum likelihood (REML; Sheldon et al. 2003) approaches, we show moderate heritability of this trait along with evidence that abaecin transcript levels are affected by multiple genes with complex interactions. Heritable variation for this trait can be used to better understand innate immunity in bees and offers promise for breeding schemes aimed at selection for disease-resistant bees.
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Methods |
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Genetic Stock and Breeding Scheme
We began the experiment by creating a homogenous female source population from immunocompetent bee stock. To generate this maternal lineage, several daughter queens were reared from a single source colony derived from a restricted commercial population (R. Willbanks Apiaries, GA; Group "A" in Figure 1). One such daughter queen (Figure 1, "B") was then inseminated with a single haploid male bee (drone) from the same restricted stock, using standard instrumental insemination techniques (Caron 1999). Forty third-generation daughter queens were reared from this mating (Figure 1, "C"). Consequently, the queens used in the subsequent crosses were full sisters (r = 0.75) due to the haplodiploid genetic system found in bees (Crozier and Pamilo 1996). Female siblings of both the founding queen and her mate showed unusually high immune gene expression, as did their resulting progeny (see Results).
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Eighty-nine additional colonies were established in 4 apiaries (Getty, Meadow, Parasitology, and Poultry) near the Bee Research Laboratory (Beltsville, MD) from queens representing diverse commercial lineages from Georgia, Texas, Hawaii, and Maryland (Apis mellifera subspecies ligustica, carnica, and mellifera). Male pupae were collected from each of these colonies and then were incubated for several days at 34 °C. On emergence, adult males (n = 25–200, approximately 6000 in total) were marked with a colony-specific color code on the thorax using nontoxic artist paints and then returned to their original colonies. After finishing adult development for a minimum of 2 weeks, these marked males were recaptured from their source colonies and were held prior to collection of semen for instrumental insemination.
Full-sister queens (Figure 1C), offspring from a cross between individuals from 2 high-immunity colonies, were themselves artificially inseminated with a single drone each from one of the drone source colonies. These single-drone–inseminated (SDI) queens were marked with numerical tags and had their wings clipped prior to establishment in small mating nucleus hives (consisting of 4 brood frames, 20 x 14 cm) for the remainder of the summer season. Female progeny of SDI queens exhibit uniformly high levels of relatedness (r 
0.75), allowing a fairly homogenous set of replicate individuals without the need of inbreeding. A total of 36 SDI queens, crossed with drones from 26 source colonies, were successfully established in colonies.
To screen larvae for immune gene transcript levels, 16 first-instar larvae from each of 89 colonies (53 field colonies and the 36 SDI colonies) were reared individually in microtiter plates containing a liquid honeybee diet (Brodsgaard et al. 2000; Evans 2004). After being transferred by spatula from their native brood cells, larvae were fed 10 µl of this diet containing an even suspension of 100 spores/µl of the bacterium P. l. larvae. These spores were derived from a single pathogenic isolate of P. l. larvae collected in Berkeley, CA (accession no. BRL230010; Evans 2004). Larvae were reared in a 34 °C incubator at high humidity for 24 h and then stored at –80 °C prior to gene expression analyses.
Twelve larvae from each colony were assayed for immune gene expression. Total RNA was extracted from individual larvae using the RNAqueous protocol (Abmion, Austin, TX) in 96-well extraction plates. To minimize plate effects during the expression assays, colony samples were divided across 2 plates (e.g., each 96-well plate contained 6 bees each from 16 colonies). To ensure removal of DNA, samples were treated twice with DNase I, once during the extractions (immediately after RNA elution) and once immediately prior to cDNA synthesis (45-min incubation at 37 °C, 5 U DNase I in appropriate buffer; Roche, Indianapolis, IN, with the RNase inhibitor RNAsin; Ambion). First-strand cDNA's were generated from approximately 2 µg total RNA using a mix of 50 U Superscript II (Invitrogen, Carlsbad, CA), 2 nmol dNTP mix, and a composite of 2 nmol poly dT-18 and 0.1 nmol poly dT(12–18). Synthesis was carried out at 45 °C for 1 h.
Quantitative Polymerase Chain Reaction
Transcript copies were quantified using real-time polymerase chain reaction (PCR) in 96-well microtiter trays using specific oligonucleotide primers and an Icycler Real-Time thermal cycler (Bio-Rad, Hercules, CA). Twenty-five microliters of PCR mixes consisted of 1 U Taq DNA polymerase with recommended buffer (Roche), 1 mM dNTP mix, 2 mM MgCl2, 0.2 µM of each primer, and an internal fluorescent probe. We used either the fluorophore 6-FAM or VIC on the 5'-end of the probe and a Taqman-FRET quencher on the 3'-end (Applied Biosystems, Foster City, CA). Transcript levels for a moderately expressed housekeeper gene (ribosomal protein S5; Evans and Wheeler 2000; Evans 2004) were used to normalize against variable cDNA levels, using PCR primers AmRPS5.F: 5'-AATTATTTGGTCGCTGGAATTG-3' and AmRPS5.R: 5'-TAACGTCCAGCAGAATGTGGTA-3' and internal Taqman probe: 5'-GCCGTTAAAGAGAAAAATGCAA-3', with 6-FAM as a 5'-reporter. Transcript levels for the gene encoding the antimicrobial protein abaecin (Casteels et al. 1990) were assessed by reverse transcription (RT)–PCR using primers Abaec.F: 5'-CAGCATTCGCATACGTACCA-3' and Abaec.R: 5'-GACCAGGAAACGTTGGAAAC-3' and the internal fluorescent probe 5'-TGTACCACAACCAGGACGTC-3', with the fluorophore VIC as a reporter.
Control reactions using RPS5 were carried out using a thermal program of 95 °C for 3 min followed by 40 cycles of 95 °C for 30 s, 58 °C for 30 s, and 72 °C for 1 min 30 s. Quantitative PCR for abaecin involved a 2-step protocol, 40 cycles of 95 °C for 30 s and 55 °C for 1 min. Fluorescence was measured repeatedly during the 58 °C step (55 °C step in 2-step PCR) using appropriate laser excitation and filtration (494 and 521 nm, respectively, for 6-FAM, and 538 and 534 nm for VIC).
Data Analyses
For each sample by primer combination, fluorescence levels were normalized within wells using average fluorescence during cycles 2–10. Threshold cycles were defined as the point when well fluorescence became >10 times the mean standard deviation across all samples. Threshold cycle numbers for abaecin were then subtracted from the RPS5 threshold for each sample. This value was then transformed as a power of 1.8 (the de facto amplification x fluorescence efficiency per cycle, as estimated using cDNA of known quantities) to produce an estimate of relative cDNA abundance. Variation in abaecin transcript level across apiaries and colonies was quantified by nested analyses of variance (ANOVAs) (colonies within apiaries), using threshold cycle values. Samples included 12 female larvae each from 36 focal (Fig. 1A) colonies and 53 field colonies, 26 of which had provided mates used to inseminate SDI queens.
For estimates of narrow-sense heritability, 12 paternal aunts were sampled for each of the SDI colonies, along with 12 members of the SDI colonies themselves. Twelve maternal aunts (common to all SDI colonies) were also surveyed for abaecin transcript levels. Narrow-sense heritability was estimated via the correlation in average abaecin transcripts between SDI colonies and female larvae from their paternal source colonies. This correlation was scaled using explicit relatedness levels between individuals (e.g., aunt–niece relatedness = 0.25; Table 1; Oldroyd et al. 1991). Error estimates were generated using jackknife replication across paired (aunt–niece) colony comparisons, when sample sizes allowed.
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Narrow-sense heritability also could be estimated by covariation in abaecin transcript levels between colonies whose queens had mated with sibling drones. Specifically, one drone source colony provided sibling drones for 3 different matings, whereas 8 colonies provided drones for 2 matings each. Average relatedness between worker offspring across SDI colonies was 0.1875 when male parents were nonrelatives (n = 26 colonies) and 0.4375 between colonies for which male parents were derived from the same field colony (0.1875 on their maternal side and 0.25 via their related fathers).
REML (e.g., Sheldon et al. 2003) estimates of heritability were also generated for abaecin transcript levels, based on relatedness values between all colonies assayed. We established a 62 x 62 x 12 explicit relatedness matrix among bees, accounting for pairwise relatedness levels reflective of haplodiploidy and variable mate numbers. Included in this matrix were individuals from field colonies that provided males for SDI, from the colony that produced queens for artificial insemination, and from the SDI colonies themselves. Covariation between abaecin transcript levels and relatedness between individuals then formed the structure of the REML analyses. These estimates included an environmental (colony) component and we include them in order to help assess the relative importance of genetic and colony-level contributions to this trait.
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Results |
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Immune Gene Expression
To assess individual immune responses, 1082 larval bees from a total of 89 colonies (36 headed by SDI queens) were tested for abaecin transcript levels. At the apiary level, colony-weighted mean abaecin ranged from 5.25 x 10–3 to 8.05 x 10–4 relative to RPS5 transcript levels (Figure 2A). The SDI colonies had significantly higher transcript levels, on average, than colonies in 4 field apiaries (nested ANOVA; P < 0.0001; Figure 2A). Colony-level means for abaecin transcripts among the SDI colonies ranged from 8 x 10–4 to 4.6 x 10–2 (Figure 2B). The SDI colonies were nevertheless lower overall for this trait than was the maternal source colony (colony-weighted mean = 0.012, standard error [SE] = 0.0009 vs. $$\overline{x}$$ = 0.047, SE = 0.009, t-test, P < 0.001). The maternal source colony was itself an extreme outlier for immune responsiveness relative to colonies of the same (Meadow) population. This result is consistent with higher abaecin scores among grandparental (maternal) relatives (e.g., in generation "B" in Figure 1), which had higher mean abaecin levels (0.0032, SE = 0.004) than did the remaining "Meadow" colonies (0.0013, SE = 0.0001).
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Heritability Estimates
Narrow-sense heritability for abaecin levels could be calculated 1) among SDI colonies whose niece–aunt relatedness is represented by a single drone source per colony mating and 2) between paired colonies whose SDI drone came from the same source colony. Narrow-sense heritability for abaecin transcript levels was h2 = 0.40 among the experimental colonies whose niece–aunt relatedness is represented by a single drone source per colony mating (Table 1). Heritability estimates among niece–aunts for which more than one drone was used from a particular source colony were h2 = 0.35.
Broad-sense heritability could be estimated by contrasting sibs within field colonies to nonrelatives in other unrelated field colonies using the intraclass correlation (Oldroyd and Moran 1983). Estimated broad-sense heritability for abaecin levels, based on estimates from bees belonging to established field colonies, was disproportionately high (H2 = 0.72, SE= 0.05, Table 1). This estimate indicates a strong environmental (colony level but not apiary level) component to immune responsiveness in these colonies. Interestingly, this environmental effect was not found for the recently established SDI colonies (H2 = 0.25). REML analyses predicted a composite h2 = 0.36 (SE = 0.066) for abaecin transcript levels among the 89 field and experimental colonies used, in line with the direct pedigree estimates.
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Discussion |
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Honeybees provide a model system for understanding the etiology of natural disease agents and the abilities of individuals to tolerate or resist pathogens. Recent advances in honeybee genetics (e.g., RNAi; Amdam et al. 2003) and genomics (Evans et al. 2006; Honey Bee Genome Sequencing Consortium 2006), when coupled with longstanding interest in honeybee disease, establish this as a unique species for understanding immunity and disease. Defining heritable components of the immune abilities of bees will help define costs, tradeoffs, and mechanistic vulnerabilities of disease survival in this species and should have general impact on the evolution of immune resistance.
Here we show significant heritable variation for expression of the gene encoding abaecin, a key honeybee immune effector (Evans and Pettis 2005). Challenged bees in our study showed 10 000-fold differences in abaecin transcript levels, even within the same family group (r = 0.75). Adaptive arguments that might explain this variation include tradeoffs between costly defenses and other life-history traits (Cotter et al. 2004) and selective forces reflecting an arms race between hosts and diverse pathogen strains or species (reviewed by Schmid-Hempel 2004), which can maintain divergent phenotypes. Although the costs of immunity in social insects are unclear, considerable effort has been made recently to understand the social dynamics of immune defenses (Mallon et al. 2003; Hughes and Boomsma 2004). Much of this effort seeks to determine whether disease resistance can drive selection for reduced genetic relatedness among social insect nest mates, a drive that might counter kin-selective pressures favoring high relatedness among nest mates (Sherman et al. 1998; Schmid-Hempel and Crozier 1999; cf. Kraus and Page 1998). Social bees have played a central role in the development and testing of this "genetic diversity" hypothesis for colony disease survival (Tarpy 2003; Tarpy and Seeley 2006). Although this study was focused on the interaction between a single pathogen and one immune effector, the described methods will be useful for determining how bees respond differently, if at all, to the range of pathogen species and strains to which they are exposed.
Variation in the levels of immune system end products such as abaecin can reflect differences among bees in the ability to recognize pathogens (Werner et al. 2000) as well as allelic variation in signaling molecules and other proteins involved with the response pathway itself. We feel that the latter hypothesis is most strongly supported by our results. Honeybees appear to possess a reduced compliment of microbial recognition proteins when compared with other insects (Evans et al. 2006), comprising only 4 peptidoglycan recognition proteins, as one example. Consequently, many of the 432 full sisters (r = 0.75) screened within the 36 SDI colonies are likely to have had identical genotypes across all the handful of anticipated recognition proteins involved in responding to P. l. larvae. Despite this fact, even full sisters showed extreme and fairly continuous variation in transcript levels for their immune end products (as shown in Figure 2B), indicating complex, epistatic relationships among many immune-related genes. Similarly, the slopes of colony-level means, based on aunt–niece relationships, were highly variable (Figure 3). Lazzaro et al. (2004) provided evidence that the many steps involved with initiating, modulating, and transducing an immune response in Drosophila can amplify variation in the end products of this response. Further, a recent knockdown study in Drosophila points to epistatic and generally complex interactions among known and novel members of the Drosophila immune response pathways (Foley and O'Farrell 2004).
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Estimates of variance in immune responsiveness between established field colonies were extremely high (H2 = 0.72, Table 1), indicating a confounding environmental component to immune responsiveness in these colonies. This effect was at the colony level, not apiary level, and the effect was not found in the more recently established, smaller, SDI colonies. Several environmental factors appear to mediate the spread of disease in bee colonies exposed to P. l. larvae, including ratios between pollen and nectar in the diet, general colony strength, and time of outbreak (Hansen and Brodsgaard 1999). In addition, the presence of nonpathogenic bacteria in the digestive tracts of bees can modulate immune responsiveness (Evans and Lopez 2004), and it is possible that these older field colonies differed from each other in their resident bacterial communities. Further field analyses are needed to determine which colony-level factors can explain higher than expected correlations among nest mates in immune responsiveness.
The SDI colonies as a group shifted immune responses to a point intermediate between both parental populations (Figure 3), suggesting that this trait is amenable to continued selection. Because abaecin level provides a good correlate with colony-level foulbrood disease (Evans and Pettis 2005), the fact that this trait is under the control of heritable components offers promise for an immunity-focused selection regime aimed at making bees less susceptible to disease. Strategies for immune trait selection in bees and other insects will also benefit from a greater awareness of the pathway components involved with insect immunity (e.g., Evans et al. 2006).
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Acknowledgments |
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We thank J. Pettis, I.B. Smith, A. Ulsamer, and C. Van Tassell for assistance during this project and T. Armstrong, D. Lopez, and J. Leips for comments on the manuscript. Supported by USDA-NRI grant no. 2002-02546 and a USDA Administrator's Research Award to J.D.E. Mention of trade names or commercial products in this paper does not imply recommendation or endorsement by the USDA.
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Footnotes |
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Corresponding Editor: L. Lacey Knowles
Received January 12, 2006
Accepted January 31, 2007
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