Locomotion and the pollen hoarding behavioral syndrome of the honeybee (<Emphasis Type="Italic">Apis mellifera</Emphasis> L.)

Honeybees selected for the colony level phenotype of storing large quantities of pollen (pollen hoarding) in the nest exhibit greater walking activity than those selected against pollen hoarding. In this study, we use a simple walking assay to demonstrate that walking activity increases with the proportion of high pollen-hoarding alleles in pure and backcrossed strains of bees (high-strain bees > offspring generated from a high backcross > offspring generated from a low backcross > low-strain bees). The trait is heritable but is not associated with markers linked to three quantitative trait loci (QTL) mapped for their effects on pollen hoarding with demonstrated pleiotropic effects on pollen and nectar foraging and learning behavior. However, locomotion in non-selected bees is correlated with responsiveness to sucrose, a trait that correlates with foraging and learning behavior. We propose that pollen-hoarding behavior involves a syndrome of behavioral traits with complex genetic and regulatory architectures that span sensory sensitivity, foraging behavior, and learning. We propose that locomotor activity is the component of this syndrome and reflects the early maturation of the bees that become pollen foragers.     

Genetic architecture of ovary size and asymmetry in European honeybee workers

The molecular basis of complex traits is increasingly understood but a remaining challenge is to identify their co-regulation and inter-dependence. Pollen hoarding (pln) in honeybees is a complex trait associated with a well-characterized suite of linked behavioral and physiological traits. In European honeybee stocks bidirectionally selected for pln, worker (sterile helper) ovary size is pleiotropically affected by quantitative trait loci that were initially identified for their effect on foraging behavior. To gain a better understanding of the genetic architecture of worker ovary size in this model system, we analyzed a series of crosses between the selected strains. The crossing results were heterogeneous and suggested non-additive effects. Three significant and three suggestive quantitative trait loci of relatively large effect sizes were found in two reciprocal backcrosses. These loci are not located in genome regions of known effects on foraging behavior but contain several interesting candidate genes that may specifically affect worker-ovary size. Thus, the genetic architecture of this life history syndrome may be comprised of pleiotropic, central regulators that influence several linked traits and other genetic factors that may be downstream and trait specific.     

The genetic architecture of the behavioral ontogeny of foraging in honeybee workers

The initiation of foraging during the life course of honeybee workers is of central interest to understanding the division of labor in social insects, a central theme in sociobiology and behavioral research. It also provides one of the most complex phenotypic traits in biological systems because of the interaction of various external, social, and individual factors. This study reports on a comprehensive investigation of the genetic architecture of the age of foraging initiation in honeybees. It comprises an estimation of genetic variation, the study of candidate loci, and two complementary quantitative trait loci (QTL) maps using two selected, continually bred lines of honeybees. We conclude that considerable genetic variation exists between the selected lines for this central life history component. The study reveals direct pleiotropic and epistatic effects of candidate loci (including previously identified QTL for foraging behavior). Furthermore, two maps of the honeybee genome were constructed from over 400 AFLP markers. Both maps confirm the extraordinary recombinational size of the honeybee genome. On the basis of these maps, we report four new significant QTL and two more suggestive QTL that influence the initiation of foraging.     

Sensory response system of social behavior tied to female reproductive traits

Background

Honey bees display a complex set of anatomical, physiological, and behavioral traits that correlate with the colony storage of surplus pollen (pollen hoarding). We hypothesize that the association of these traits is a result of pleiotropy in a gene signaling network that was co-opted by natural selection to function in worker division of labor and foraging specialization. By acting on the gene network, selection can change a suite of traits, including stimulus/response relationships that affect individual foraging behavior and alter the colony level trait of pollen hoarding. The ‘pollen-hoarding syndrome’ of honey bees is the best documented syndrome of insect social organization. It can be exemplified as a link between reproductive anatomy (ovary size), physiology (yolk protein level), and foraging behavior in honey bee strains selected for pollen hoarding, a colony level trait. The syndrome gave rise to the forager-Reproductive Ground Plan Hypothesis (RGPH), which proposes that the regulatory control of foraging onset and foraging preference toward nectar or pollen was derived from a reproductive signaling network. This view was recently challenged. To resolve the controversy, we tested the associations between reproductive anatomy, physiology, and stimulus/response relationships of behavior in wild-type honey bees.

Methodology/Principal Findings

Central to the stimulus/response relationships of honey bee foraging behavior and pollen hoarding is the behavioral trait of sensory sensitivity to sucrose (an important sugar in nectar). To test the linkage of reproductive traits and sensory response systems of social behavior, we measured sucrose responsiveness with the proboscis extension response (PER) assay and quantified ovary size and vitellogenin (yolk precursor) gene expression in 6–7-day-old bees by counting ovarioles (ovary filaments) and by using semiquantitative real time RT-PCR. We show that bees with larger ovaries (more ovarioles) are characterized by higher levels of vitellogenin mRNA expression and are more responsive to sucrose solutions, a trait that is central to division of labor and foraging specialization.

Conclusions/Significance

Our results establish that in wild-type honey bees, ovary size and vitellogenin mRNA level covary with the sucrose sensory response system, an important component of foraging behavior. This finding validates links between reproductive physiology and behavioral-trait associations of the pollen-hoarding syndrome of honey bees, and supports the forager-RGPH. Our data address a current evolutionary debate, and represent the first direct demonstration of the links between reproductive anatomy, physiology, and behavioral response systems that are central to the control of complex social behavior in insects.     

Genetic dissection of honeybee (Apis mellifera L.) foraging behavior

We demonstrate the effects of a new quantitative trait locus (QTL), designated pln3, that was mapped in a backcross population derived from strains of bees selected for the amount of pollen they store in combs. We independently confirmed pln3 by demonstrating its effects on individual foraging behavior, as we did previously for QTLs pln1 and pln2 (Hunt et al. 1995). QTL pln2 is very robust in its effects on foraging behavior. In this study, pln2 was again shown to affect individual foraging behavior of workers derived from a hybrid backcross of the selected strains. In addition, pln2 was shown to affect the amount of pollen stored in combs of colonies derived from a wide cross of European and Africanized honeybees. This is noteworthy because it demonstrates that we can map QTLs for behavior in interstrain crosses derived from selective breeding and study their effects in unselected, natural populations. The results we present also demonstrate the repeatability of finding QTLs with measurable effects, even after outcrossing selected strains, suggesting that there is a relatively small subset of QTLs with major effects segregating in the population from which we selected our founding breeding populations. The different QTLs, pln1, pln2, and pln3, appear to have different effects, revealing the complex genetic architecture of honeybee foraging behavior.     

Pleiotropy, epistasis and new QTL: the genetic architecture of honey bee foraging behavior

The regulation of division of labor in social insects, particularly in the honey bee (Apis mellifera L.), has received considerable attention from a number of biological subdisciplines, including quantitative and behavioral genetics, because of the high complexity of the behavioral traits involved. The foraging choices of honey bee workers can be accurately quantified, and previous studies have made the foraging behavior of honey bees one of the best studied naturally occurring behavioral phenotypes. Three quantitative trait loci (QTL) have been identified that influence a set of foraging variables, including the concentration of nectar collected and the amount of pollen and nectar brought back to the hive. This study extends previous genetic investigations and represents the most comprehensive investigation of the genetic architecture of these foraging variables. We examined the effects of markers for the three established QTL and for one further candidate gene (Amfor), in two reciprocal backcross populations. These populations were also used to carry out two new QTL mapping studies, with over 400 Amplified Fragment Length Polymorphism (AFLP) markers in each. We detected a variety of effects of the genetic markers for the established QTL and the candidate gene, which were mostly epistatic in nature. A few new QTL could be detected with a variety of mapping techniques. Our results add complexity to the genetic architecture of the foraging behavior of the honey bee. Specifically, we support the hypotheses that pln1, pln2, pln3, and Amfor are involved in the regulation of foraging behavior in the honey bee and add some new factors that deserve further study in the future.     

Analysis of quantitative trait loci that influence animal behavior

Behavioral differences between inbred strains of mice and rats have a genetic basis that can now be dissected using quantitative trait locus (QTL) analysis. Over the last 10 years, a large number of genetic loci that influence behavior have been mapped. In this article I review what that information has revealed about the genetic architecture of behavior. I show that most behaviors are influenced by QTL of small effect, each contributing to less than 10% of the variance of a behavioral trait. The small effect of each QTL on behavioral variation suggests that the mutational spectrum is different from that which results in Mendelian disorders. Regions of DNA should be appropriately prioritized to find the molecular variants, for instance by looking at sequences that control the level of gene expression rather than variants in coding regions. While the number of allelic loci that can contribute to a trait is large, this is not necessarily the case: the analysis of selected strains shows that a remarkably small number of QTL can explain the bulk of the genetic variation in behavior. I conclude by arguing that genetic mapping has more to offer than a starting point for positional cloning projects. With advances in multivariate analyses, mapping can also test hypotheses about the psychological processes that give rise to behavioral variation.     

Genotype and rearing environment affect honeybee perception and foraging behaviour

We tested the effects of larval and preforaging rearing environment on the foraging behaviour and sucrose response thresholds of honeybees, Apis mellifera L., derived from high and low pollen-hoarding strains. Bees were reared as larvae and as preforaging adults in colonies containing high and low pollen-hoarding strains, then cofostered in unrelated common wild-type colonies from which to forage. Genotype, but not rearing environment, had strong effects on the likelihood to forage for pollen or nectar, the size of pollen or nectar load, and the concentration of sugar in the nectar they collected. Genotype and rearing environment affected adult wet weights and sucrose concentration response threshold, as measured with the proboscis extension response assay. Bees from the high pollen-hoarding strain were more sensitive to conditions of the rearing environment than were bees of the low strain. High- and low-strain bees produced different colony environments that affected developmental, behavioural and physical traits of the individuals they reared. This demonstrates how genotype and colony environment correlate and affect phenotype. Copyright 2002 The Association for the Study of Animal Behaviour. Published by Elsevier Science Ltd. All rights reserved.     

Societies to genes: can we get there from here?

Understanding the organization and evolution of social complexity is a major task because it requires building an understanding of mechanisms operating at different levels of biological organization from genes to social interactions. I discuss here, a unique forward genetic approach spanning more than 30 years beginning with human-assisted colony-level selection for a single social trait, the amount of pollen honey bees (Apis mellifera L.) store. The goal was to understand a complex social trait from the social phenotype to genes responsible for observed trait variation. The approach combined the results of colony-level selection with detailed studies of individual behavior and physiology resulting in a mapped, integrated phenotypic architecture composed of correlative relationships between traits spanning anatomy, physiology, sensory response systems, and individual behavior that affect individual foraging decisions. Colony-level selection reverse engineered the architecture of an integrated phenotype of individuals resulting in changes in the social trait. Quantitative trait locus (QTL) studies combined with an exceptionally high recombination rate (60 kb/cM), and a phenotypic map, provided a genotype–phenotype map of high complexity demonstrating broad QTL pleiotropy, epistasis, and epistatic pleiotropy suggesting that gene pleiotropy or tight linkage of genes within QTL integrated the phenotype. Gene expression and knockdown of identified positional candidates revealed genes affecting foraging behavior and confirmed one pleiotropic gene, a tyramine receptor, as a target for colony-level selection that was under selection in two different tissues in two different life stages. The approach presented here has resulted in a comprehensive understanding of the structure and evolution of honey bee social organization.     

The gene vitellogenin has multiple coordinating effects on social organization

Temporal division of labor and foraging specialization are key characteristics of honeybee social organization. Worker honeybees (Apis mellifera) initiate foraging for food around their third week of life and often specialize in collecting pollen or nectar before they die. Variation in these fundamental social traits correlates with variation in worker reproductive physiology. However, the genetic and hormonal mechanisms that mediate the control of social organization are not understood and remain a central question in social insect biology. Here we demonstrate that a yolk precursor gene, vitellogenin, affects a complex suite of social traits. Vitellogenin is a major reproductive protein in insects in general and a proposed endocrine factor in honeybees. We show by use of RNA interference (RNAi) that vitellogenin gene activity paces onset of foraging behavior, primes bees for specialized foraging tasks, and influences worker longevity. These findings support the view that the worker specializations that characterize hymenopteran sociality evolved through co-option of reproductive regulatory pathways. Further, they demonstrate for the first time how coordinated control of multiple social life-history traits can originate via the pleiotropic effects of a single gene that affects multiple physiological processes.     

The Gene vitellogenin Has Multiple Coordinating Effects on Social Organization

Temporal division of labor and foraging specialization are key characteristics of honeybee social organization. Worker honeybees (Apis mellifera) initiate foraging for food around their third week of life and often specialize in collecting pollen or nectar before they die. Variation in these fundamental social traits correlates with variation in worker reproductive physiology. However, the genetic and hormonal mechanisms that mediate the control of social organization are not understood and remain a central question in social insect biology. Here we demonstrate that a yolk precursor gene, vitellogenin, affects a complex suite of social traits. Vitellogenin is a major reproductive protein in insects in general and a proposed endocrine factor in honeybees. We show by use of RNA interference (RNAi) that vitellogenin gene activity paces onset of foraging behavior, primes bees for specialized foraging tasks, and influences worker longevity. These findings support the view that the worker specializations that characterize hymenopteran sociality evolved through co-option of reproductive regulatory pathways. Further, they demonstrate for the first time how coordinated control of multiple social life-history traits can originate via the pleiotropic effects of a single gene that affects multiple physiological processes.     

PKA and PKC content in the honey bee central brain differs in genotypic strains with distinct foraging behavior

Selection of honey bees for pollen storage resulted in high and low pollen-hoarding strains differing in foraging behavior traits including resource choice and quality, load size, sucrose responsiveness, age of foraging initiation, and learning performance. To determine how these genotypic differences correlate with changes at the level of proteins involved in neuronal function, we measured the content of protein kinase A, protein kinase C, and synapsin in the brains of high- and low-strain bees. In the central brain protein kinase A and protein kinase C levels were greater in high-strain bees and increased from emergence to 5 days in both strains. By 15 days, high-strain bees retained significantly higher levels of protein kinase C than low-strain bees, but overall protein kinase C content decreased in both strains. Synapsin levels increased from emergence to 5 days but did not differ between the two strains. In contrast to the protein kinase A content in the central brain, the basal protein kinase A activity did not differ between the strains or between the two age groups. This provides first evidence that the two genetic strains of honey bees show characteristic differences in the regulation of protein expression that may contribute to the behavioral differences between them.Abbreviations PKA protein kinase A - PKC protein kinase C     

Support for the reproductive ground plan hypothesis of social evolution and major QTL for ovary traits of Africanized worker honey bees (Apis mellifera L.)

Background  

The reproductive ground plan hypothesis of social evolution suggests that reproductive controls of a solitary ancestor have been co-opted during social evolution, facilitating the division of labor among social insect workers. Despite substantial empirical support, the generality of this hypothesis is not universally accepted. Thus, we investigated the prediction of particular genes with pleiotropic effects on ovarian traits and social behavior in worker honey bees as a stringent test of the reproductive ground plan hypothesis. We complemented these tests with a comprehensive genome scan for additional quantitative trait loci (QTL) to gain a better understanding of the genetic architecture of the ovary size of honey bee workers, a morphological trait that is significant for understanding social insect caste evolution and general insect biology.     

Do large effect QTL fractionate? A case study at the maize domestication QTL teosinte branched1

Quantitative trait loci (QTL) mapping is a valuable tool for studying the genetic architecture of trait variation. Despite the large number of QTL studies reported in the literature, the identified QTL are rarely mapped to the underlying genes and it is usually unclear whether a QTL corresponds to one or multiple linked genes. Similarly, when QTL for several traits colocalize, it is usually unclear whether this is due to the pleiotropic action of a single gene or multiple linked genes, each affecting one trait. The domestication gene teosinte branched1 (tb1) was previously identified as a major domestication QTL with large effects on the differences in plant and ear architecture between maize and teosinte. Here we present the results of two experiments that were performed to determine whether the single gene tb1 explains all trait variation for its genomic region or whether the domestication QTL at tb1 fractionates into multiple linked QTL. For traits measuring plant architecture, we detected only one QTL per trait and these QTL all mapped to tb1. These results indicate that tb1 is the sole gene for plant architecture traits that segregates in our QTL mapping populations. For most traits related to ear morphology, we detected multiple QTL per trait in the tb1 genomic region, including a large effect QTL at tb1 itself plus one or two additional linked QTL. tb1 is epistatic to two of these additional QTL for ear traits. Overall, these results provide examples for both a major QTL that maps to a single gene, as well as a case in which a QTL fractionates into multiple linked QTL.     

Using progenitor strain information to identify quantitative trait nucleotides in outbred mice

We have developed a fast and economical strategy for dissecting the genetic architecture of quantitative trait loci at a molecular level. The method uses two pieces of information: mapping data from crosses that involve more than two inbred strains and sequence variants in the progenitor strains within the interval containing a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any sequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals except at a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis. We apply the method to a 4.8-Mb region on mouse chromosome 1 that contains a QTL influencing anxiety segregating in a heterogeneous stock and show that, under the assumption that a single QTN is present and lies in a region conserved between the human and mouse genomes, it is possible to reduce the number of variants likely to be the quantitative trait nucleotide from many thousands to <20.     

Failure to replicate two mate preference QTLs across multiple strains of Drosophila pseudoobscura

Behavioral genetic mapping studies in model organisms predominantly use crosses originating from a single pair of inbred lines to determine the location of alleles that confer genetic variation in the trait of interest, and they often make sweeping generalizations about the genetic architecture of the trait based on these results. A previous study fine mapped mate preference variation between one pair of Drosophila pseudoobscura lines and identified 2 strong-effect behavioral quantitative trait loci (QTLs). Here, we replicated the previous study's mapping design to examine the extent of variation at these behavioral QTLs across 6 pairs of lines, but we were unable to detect effects of either QTL region in the pairs of lines studied. We suggest that the low-discrimination alleles at these 2 QTLs may occur at low frequency within D. pseudoobscura, although other explanations for the inconsistency are possible. These results underscore the need to examine multiple strains across a species when describing the genetic variation underlying behavioral traits.     

Bayesian analysis for genetic architecture of dynamic traits

The dissection of the genetic architecture of quantitative traits, including the number and locations of quantitative trait loci (QTL) and their main and epistatic effects, has been an important topic in current QTL mapping. We extend the Bayesian model selection framework for mapping multiple epistatic QTL affecting continuous traits to dynamic traits in experimental crosses. The extension inherits the efficiency of Bayesian model selection and the flexibility of the Legendre polynomial model fitting to the change in genetic and environmental effects with time. We illustrate the proposed method by simultaneously detecting the main and epistatic QTLs for the growth of leaf age in a doubled-haploid population of rice. The behavior and performance of the method are also shown by computer simulation experiments. The results show that our method can more quickly identify interacting QTLs for dynamic traits in the models with many numbers of genetic effects, enhancing our understanding of genetic architecture for dynamic traits. Our proposed method can be treated as a general form of mapping QTL for continuous quantitative traits, being easier to extend to multiple traits and to a single trait with repeat records.     

A unified statistical model for functional mapping of environment-dependent genetic expression and genotype x environment interactions for ontogenetic development

The effects of quantitative trait loci (QTL) on phenotypic development may depend on the environment (QTL x environment interaction), other QTL (genetic epistasis), or both. In this article, we present a new statistical model for characterizing specific QTL that display environment-dependent genetic expressions and genotype x environment interactions for developmental trajectories. Our model was derived within the maximum-likelihood-based mixture model framework, incorporated by biologically meaningful growth equations and environment-dependent genetic effects of QTL, and implemented with the EM algorithm. With this model, we can characterize the dynamic patterns of genetic effects of QTL governing growth curves and estimate the global effect of the underlying QTL during the course of growth and development. In a real example with rice, our model has successfully detected several QTL that produce differences in their genetic expression between two contrasting environments. These detected QTL cause significant genotype x environment interactions for some fundamental aspects of growth trajectories. The model provides the basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments and genetic relationships for growth rates and the timing of life-history events for any organism.