Honey bee sociogenomics: a genome-scale perspective on bee social behavior and health

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Apidologie                                                                                    Review article
* INRA, DIB and Springer-Verlag France, 2013
DOI: 10.1007/s13592-013-0251-4

       Honey bee sociogenomics: a genome-scale perspective
                on bee social behavior and health

                                    Adam G. DOLEZAL1 , Amy L. TOTH1,2
         1
             Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
                           2
                             Department of Entomology, Iowa State University, Ames, IA, USA

                      Received 3 July 2013 – Revised 9 October 2013 – Accepted 18 October 2013

Abstract – The biology of honey bees involves a host of developmental, behavioral, and physiological
components that allow thousands of individual bees to form complex social units. Fueled by a wealth of
information from new genomic technologies, a new approach, sociogenomics, uses a focus on the genome to
integrate the molecular underpinnings and ultimate explanations of social life. This approach has resulted in a
massive influx of data from the honey bee genome and transcriptome, a flurry of research activity, and new
insights into honey bee biology. Here, we provide an up-to-date review describing how the honey bee has been
successfully studied using this approach, highlighting how the integration of genomic information into honey
bee research has provided insights into worker division of labor, communication, caste differences and
development, evolution, and honey bee health. We also highlight how genomic studies in other eusocial insect
species have provided insights into social evolution via comparative analyses. These data have led to several
important new insights about how social behavior is organized on a genomic level, including (1) the fact that
gene expression is highly dynamic and responsive to the social environment, (2) that large-scale changes in
gene expression can contribute to caste and behavioral differences, (3) that transcriptional networks regulating
these behaviors can be related to previously established hormonal mechanisms, and (4) that some genes and
pathways retain conserved roles in behavior across contexts and social insect taxa.

genome / division of labor / behavioral maturation / caste / comparative genomics

   1. INTRODUCTION                                           approaches to bridge gaps between evolutionary
                                                             and mechanistic approaches to studying ani-
   The social life of bees has been of intense               mal societies began little more than a decade
interest to biologists and apiculturists for centu-          ago.
ries. As such, there has been a wealth of studies               This new approach, dubbed sociogenomics
on the evolution, behavior, colony organization,             (Robinson 1999), proposed that the genome can
and development of honey bees and their                      form a centerpiece for linking different levels of
societies. These studies have spanned across                 analysis, allowing researchers to integrate the
levels of analysis, providing insight into both              proximate causes of behavior, like gene expres-
the proximate and ultimate causes behind the                 sion and physiology, with more ultimate analy-
social complexity of honey bee society.                      ses, like behavioral ecology. By using the
However, specific focus on integrating these                 genome as a focal point, sociogenomics seeks
                                                             to provide a more comprehensive method for
                                                             understanding social life, from its evolution to
Corresponding author: A. Dolezal,                            its genetic regulation—and everywhere in be-
adolezal@gmail.com                                           tween (Robinson et al. 2005). Since studies on
Manuscript editor: Stan Schneider                            honey bees have historically run the gamut
A.G. Dolezal and A.L. Toth

across these levels of analysis, it is unsurprising   disease and pathogen responses. Finally, while we
that bees have become an important model for          focus specifically on how sociogenomics has
applying sociogenomic approaches. With the            improved our understanding of bee biology,
increased scope and availability of genomic           throughout the review, we highlight how a
tools (Table I), including the sequencing of the      sociogenomic-minded exploration across social
complete honey bee genome (Weinstock et al.           insect taxa has fueled comparisons for a better
2006), studies grounded in a genomic perspec-         understanding of the evolution of eusociality.
tive have been successful in helping to tease
apart the different components that intersect to        2. WORKER DIVISION OF LABOR
build complex social organisms (Smith et al.
2008).                                                  2.1. Foraging ontogeny
    The sociogenomic approach spans across a
large swathe of applications and has been                 One of the most striking aspects of eusocial
defined broadly (Robinson et al. 2005). Here,         insect societies, and honey bees in particular, is
we restrict our definition to research focusing       the behavioral plasticity found within the
on large numbers of genes or on single genes          worker caste. This flexibility takes the form of
that provide key insights into larger genetic         temporal polyethism, in which workers transition
pathways, and exclude a rich and informative          across different task repertoires as they age. After
literature on single genes (e.g., Ben-Shahar          adult emergence, workers specialize on a variety
2005; Amdam et al. 2010) that are beyond the          of in-nest tasks, such as brood care, and then
scope of this review. We focus our review on          transition through stages of other tasks, such as
large-scale genomic or transcriptomic analy-          nest maintenance and guarding, culminating in
ses, microarrays, or targeted studies that            foraging behavior (Winston 1987). While this
explore or clarify genetic pathways consisting        sequence of behavioral maturation occurs as a
of multiple genes. Furthermore, while we focus        general pattern, workers exhibit a high level of
specifically on how the rise of sociogenomics         flexibility in the rate of behavioral development,
has helped us understand the social life of bees,     which allows individuals to respond to differing
this approach has been very successful in other       colonial demands (Robinson 1992).
eusocial insects (Smith et al. 2008). In fact, one        At this point in time, sociogenomics has been
important strength of sociogenomics is the            more thoroughly applied to the study of worker
capability to make comparisons: the power to          behavioral maturation than any other facet of
search for homologies in genomes across taxa          honey bee biology and, therefore, stands as the
helps find clues to understand the evolution of       best example of how successful this approach can
bee societies (Fischman et al. 2011).                 be. This is largely due to the strong background of
    Here, we review how the use of sociogenomics      literature and expertise spanning behavioral,
has advanced knowledge of many facets of honey        genetic, neurobiological, and physiological
bee biology. We begin with worker temporal            studies on temporal polyethism, which has allowed
polyethism; the behavioral transition from in-hive    researchers to build a more comprehensive
to foraging tasks is arguably the best studied        understanding of this system centering on
honey bee behavioral phenomenon using a               investigation of the genome (Robinson 2002;
sociogenomic approach, and we use it as a             Robinson et al. 2005; Smith et al. 2008) (Figure 1).
benchmark for comparison with other research              Before sequencing of the honey bee genome,
foci. Then, we follow with descriptions of the        most of the studies described as sociogenomic
progress made in understanding the evolution and      were borne from the integration of behavioral,
regulation of caste differences, communication,       neuronal, and physiological mechanisms with
and social immunity, and we also review how the       genomic information generated from partial
approaches and methods pioneered with                 genome resources, which provided vastly more
sociogenomics have been applied to honey bee          information than previous approaches that focused
Table I. How has the sequencing of the honey bee genome changed sociogenomic applications?

      Application                       Pre-genome                              Post-genome                        Gain from genome                         References

Gene discovery              e.g., Degenerate polymerase             Computational analysis                    Whole genome instead               Elsik, Mackey et al. (2007)
                              chain reaction (PCR) and               of entire genome sequence                 of single gene, less
                              cloning on a gene-by-gene                                                        labor-intensive, allows
                              basis                                                                            discovery of novel genes
Identification of           e.g., Rapid amplification of            Computational analysis of                 Whole genome instead               Ament et al. (2012b)
  regulatory sequences        cDNA ends on a gene-by-                entire genome sequence,                   of single gene, less
                              gene basis                             chromatin immunoprecipitation             labor-intensive
                                                                     and sequencing
Epigenetic profiling        e.g., Methylation sensitive             Computational analysis of                 Ability to identify actual         Kronforst, Gilley et al.
 (e.g., identifying           amplified fragment length              CpG observed/expected content             genes and nucleotides              (2008); Herb et al. (2012)
 methylation)                 polymorphism of anonymous              of genome, whole genome                   that are methylated,
                              methylated sites                       bisulfite sequencing                      whole genome coverage
                                                                                                               instead of a small subset
Gene expression             Quantitative PCR for select             Whole-genome microarrays,                 Whole genome instead of            Whitfield et al. (2002, 2003);
 profiling                   genes, EST-based microarrays,           RNA sequencing compared                   a fraction of the genome,          Chen et al. (2012); Liang
                             screening ESTs for a small              to whole genome                           RNA-Seq is less labor-             et al. (2012)
                             number of differentially                                                          intensive and has a greater
                             expressed genes                                                                   dynamic range
Sequence evolution          Complex sequence of molecular           Bioinformatic comparisons                 Whole genome instead of            Hunt et al. (2010); Johnson
                             techniques, with the ability to         across species that include               select genes, enhanced             and Tsutsui (2011)
                                                                                                                                                                                      Honey bee sociogenomics

                             target only a small number              numerous genes and entire                 ability to uncover gene
                             of genes                                gene families                             losses and gains

Many studies on honey bee sociogenomics occurred before the sequencing of the honey bee genome and provided significant insights. However, the existence of an annotated
genome provides researchers with many powerful tools to investigate factors that would be much more difficult, or even impossible, without the ability to look at the whole genome.
This table summarizes how different applications have changed with the advent of the genome
A.G. Dolezal and A.L. Toth

                                                     Environment

                                                                                        Gene
                      Epigenetics
                                                                                      Expression

                                                        Phenotype

                      Regulatory                                                       Hereditary
                       elements                                                        Genetics

Figure 1. An integrative, sociogenomic approach as applied to the study of behavioral maturation into a forager
in worker honey bees. A sociogenomic approach to social behaviors has the potential to integrate many
different forms of genomic mechanisms that interact to affect phenotype. The best example of the use of
sociogenomics is the investigation of the underlying regulation of honey bee worker behavioral maturation, or
foraging onset. Integration of behavioral studies with a variety of genomic tools has provided insights into how
the environment (both external and social) and allelic variation in individuals affects gene expression,
regulatory elements, and epigenetics to form a network of effects that result in a specific behavioral phenotype.
Investigation of other phenotypes with a sociogenomic approach promises to reveal similar networks. Image
©Alex Wild, used by permission.

on one or a few genes (Whitfield et al. 2002).            colonies were used to decouple the nurse-to-
These included expressed sequence tags                    forager transition from chronological age, revealing
(ESTs) and microarrays. For the honey bee, the            changes in over 2,000 genes or about 40 % of genes
combination of these techniques, which provided           assayed. Further, these gene expression profiles
partial sequence information for close to 50 % of         can be used to predict the behavior of individual
the genes in the genome, allowed researchers to           bees (Whitfield et al. 2003). In fact, many of the
identify thousands of genes of interest and then          same molecular processes involved in the nurse-
screen them for expression levels (Whitfield et al.       to-forager transition appear to be conserved in the
2002). Microarrays allowed for large-scale                brain across species within the genus Apis, though
screenings for gene expression differences                others, such as those involved in carbohydrate
between behavioral groups, helping to identify            metabolism, circadian rhythm, and colony defense,
how differences in gene expression are related to         differ between species (Sen Sarma et al. 2007). In
worker division of labor. Comparisons of the              addition, a comparison of thousands of transcripts
brains of young nurses to old foragers (Kucharski         from the brain and abdomen, across nine bee
and Maleszka 2002; Whitfield et al. 2003) found           species, representing three origins of sociality,
clear differences in brain gene expression. Then,         showed that genes involved in carbohydrate
behavioral manipulations using single-cohort              metabolism are more rapidly evolving in eusocial
Honey bee sociogenomics

lineages, supporting arguments for the involve-       in worker behavioral maturation (Ament et al.
ment of metabolic pathways in social evolution        2008).
(Woodard et al. 2011).                                    Studies using the honey bee genome also
   After initial screenings identified correlations   identified several transcription factors that
between behavioral state and gene expression          differ in expression between worker behavioral
differences, more in-depth investigations further     groups. Genes such as Creb, involved in neural
fleshed out the relationship between worker           plasticity in many animals (McClung and
behavioral maturation and brain gene expres-          Nestler 2008), and dorsal, which is involved
sion. By integrating genomic analysis with            in insect developmental patterning and immune
experimental approaches, it has been possible         response (Qiu et al. 1998), were identified as
to build a more complete picture of the               part of a transcriptional network that could
interaction between physiology, environment,          predict the expression of many other behavior-
and genotype on worker behavioral maturation.         ally linked genes (Weinstock et al. 2006;
The brain gene expression differences found           Chandrasekaran et al. 2011). Further genomic
between nurses and foragers appear to be              analyses of the brain showed that transcription
influenced strongly by queen mandibular               factors known to regulate development may be
pheromone (QMP; Grozinger et al. 2003),               also involved in behavioral maturation in honey
which is produced by honey bee queens to              bee workers (Sinha et al. 2006). In particular,
regulate the behavior of workers (Winston and         ultraspiracle (usp), a transcription factor linked
Slessor 1998). Juvenile hormone (JH) signal-          to JH signaling, appears to interact with other
ing, long known to be an important regulator of       transcription factors to help orchestrate a net-
foraging onset (Robinson 1987; Sullivan et al.        work of gene expression that occurs between
2000) had very large effects on gene expression,      the brain and peripheral tissues to regulate
leading to forager-like brain gene expression         worker behavioral changes (Ament et al.
even in bees reared in cages with no prior            2012b). DNA methylation, an epigenetic mod-
foraging experience (Whitfield et al. 2006).          ification to DNA (discussed in more detail in
Further investigation showed how nutrition            Section 4), also appears to have a role in worker
and nutritional signaling pathways, specifically      behavioral change; not only do nurse and
insulin/insulin-like signaling (IIS), are in-         forager bees differ in brain gene methylation
volved in behavior and how other factors are          patterns, but the methylation patterns are also
affected downstream. In insects, IIS acts as a        behaviorally reversible (Herb et al. 2012).
key regulator of feeding behavior and metabo-             Sociogenomic studies on division of labor in
lism and also interacts with target of                other eusocial species have also provided
rapamycin (TOR) (Edgar 2006), another im-             information on how common genetic toolkits
portant metabolic pathway regulator, and JH           could be used to build convergent social
(Tu et al. 2005). Based on single-gene experi-        behaviors. Microarray screening of the brains
mental studies (Ben-Shahar 2005; Nelson et al.        of Polistes metricus wasps showed that the gene
2007), a focus on the effects of these pathways       expression profiles of foraging P. metricus had
showed that changes in IIS and TOR affect             significant overlap with the profiles of foraging
behavioral maturation, and a reanalysis of            honey bees, especially genes related to heat
previous microarray data (Grozinger et al.            stress, locomotion, and lipid metabolism
2003; Whitfield et al. 2006) showed differences       (Toth et al. 2010). Further experiments showed
in energy metabolism between nurses and               that starved P. metricus workers had reduced lipid
foragers (Ament et al. 2008, 2010). In fact,          levels and increased foraging activity (Daugherty
experimental perturbation of IIS causes changes       et al. 2011), similar to the nutritional regulation of
in the timing of foraging initiation, further         foraging in the honey bee (Toth et al. 2005; Toth
showing how IIS and its interaction with              and Robinson 2005). These changes were accom-
nutritional and metabolic pathways are involved       panied by changes in brain gene expression that
A.G. Dolezal and A.L. Toth

significantly overlapped with changes found in         selection regime produced divergent phenotypes
honey bees, including genes involved in insulin        that were highly amenable to genetic mapping
and JH signaling. These two studies together           studies. A series of analyses identified quanti-
provide some support for the idea that a common        tative trait loci (QTLs) associated with pollen
genetic toolkit, centering on nutritional responses,   hoarding, individual forager preferences (Hunt
could have a key role in the evolution of division     et al. 1995), ovary size (Linksvayer et al. 2009;
of labor across eusocial insect lineages (Daugherty    Graham et al. 2011), and JH responsiveness
et al. 2011).                                          (Page et al. 2012) in these high-pollen-hoarding
   As a whole, studies on behavioral maturation        and low-pollen-hoarding strains. Furthermore,
in workers form the most complete picture of           localization of these QTLs using the honey bee
how a sociogenomic approach can link many              genome indicated that the QTLs contained
different factors to better explain a complex          several genes related to IIS signaling, highlight-
biological process. By integrating the wealth of       ing the potential importance of IIS signaling on
behavioral, physiological, and genetic informa-        these behavioral phenotypes (Hunt et al. 2007).
tion with newly developed genomic tools, it has        Microarray screening of the ovaries of high-
been possible to more thoroughly understand            pollen-hoarding and low-pollen-hoarding strains
the molecular underpinnings of behavioral              of bees further identified transcriptional differ-
phenotypes. Even before sequencing of the              ences related to these behavioral phenotypes,
honey bee genome, microarray screens were              particularly tyramine receptor (TYR) and a
able to identify how patterns in gene expression       putative ecdysteroid hormone receptor (HR46)
differ as bees change behaviors (Whitfield et al.      (Wang et al. 2012). Combined with the strong
2003) and in response to pheromonal stimuli            body of experimental studies using single-gene
(Grozinger and Robinson 2002). Sequencing of           and physiological approaches to bee behavior,
the honey bee genome has enabled more                  particularly those focusing on the yolk precur-
complete genomic analyses that include nearly          sor vitellogenin, these studies have provided
all of the genes in the genome and has given           many insights into the physiological regulation
researchers ready access to noncoding regions          and possible evolutionary pathways to eusocial
of the honey bee genome. Table I provides a            insect behaviors (reviewed by Page et al. 2012;
summary of pre-genome and post-genome ap-              for further review, see Rueppell 2013).
proaches to studying the genetic basis of honey
bee biology. For example, full genome infor-             2.3. Guarding, undertaking, and scouting
mation has helped to clarify how transcription
factors and regulatory elements (Ament et al.             While the sociogenomics of foraging onset is
2012a, b) and DNA methylation (Herb et al.             the most investigated, other worker honey bee
2012) are involved in driving a host of pathway        behaviors have also been studied with genomic
changes that influence behavioral maturation           approaches. There are some important differ-
(Figure 1).                                            ences with respect to the aforementioned work
                                                       on behavioral maturation in that some worker
  2.2. Pollen-hoarding syndromes                       behaviors are more short-term responses to
                                                       colony needs and do not appear to involve
   Genomic approaches have also been used to           extensive shifts in gene expression. For exam-
study colony-level pollen-hoarding phenotypes          ple, microarray comparisons of brain tissue
and the individual physiological and behavioral        showed no significant differences in brain gene
differences that accompany them. Researchers           expression of bees exhibiting guarding and
used selective breeding to produce bee strains         undertaking behaviors, which occur for short
exhibiting opposite colony-level traits for pollen     periods of time between nursing and foraging
storage, either high or low levels of pollen           onset, even though these behaviors are clearly
hoarding (Page and Fondrk 1995). Thus, the             discernible, indicating that distinct behavioral
Honey bee sociogenomics

changes can occur even in the absence of large-       3. COMMUNICATION
scale transcriptional changes in the brain (Cash
et al. 2005).                                         3.1. Pheromones
   Another important behavior, which only
some workers ever actually perform, is scouting        The organization and maintenance of com-
behavior, which can take the form of worker         plex social colonies requires intricate systems of
bees scouting for new food sources or new nest      communication. In honey bees, the predominant
sites after a swarming event (Seeley 1985). A       method of communication is through chemicals,
whole-genome microarray comparison of the           mostly in the form of pheromones, which act as
brains of food scouts and non-scouting forager      chemical signals by members of the hive to
bees revealed extensive differences in gene         prevent intracolonial conflict and regulate be-
expression, most notably in genes involved in       havioral plasticity. Primer pheromones affect
neurotransmitter systems known to be involved       long-term physiological changes that result in
in novelty seeking behavior in other insect         delayed behavioral responses, while releaser
species and humans, like catecholamine, gluta-      pheromones act on more short-term processes
mate, and GABA signaling (Liang et al. 2012).       to quickly change behavioral performance (Le
                                                    Conte and Hefetz 2008). Queens produce QMP,
                                                    a primer pheromone that prevents workers from
  2.4. Aggression                                   developing active ovaries and foraging onset
                                                    (Pankiw et al. 1998). Experimental manipula-
    The genetic basis of yet another worker         tion, followed by microarray screening of brain
behavior, defensiveness or aggressive behavior,     gene expression, also showed that QMP treat-
has been a topic of active investigation. Crosses   ment changes brain gene expression in over
of high-defensive-response Africanized honey        2,500 genes of laboratory bees and around 700
bees with low-defensive-response European           genes in bees from field colonies, specifically
honey bees revealed several QTLs linked to          activating genes associated with nursing behav-
increased defensiveness (sting-1, sting-2, and      ior and repressing those associated with forag-
sting-3; Hunt et al. 1998). The use of genomic      ing. In addition, transcription factor genes were
sequencing and linkage mapping on these QTLs        affected at a higher proportion than other genes,
narrowed down the number of candidate genes         suggesting that QMP may act by targeting
associated with defensive responsiveness, iden-     transcription factors to initiate downstream
tifying orthologs of genes involved in nervous      cascades of expression changes (Grozinger et
system development and activity and sensory         al. 2003). Further, individual workers vary in
signaling (Hunt et al. 2007), though another        their attraction to QMP, and their brain gene
study showed that potentially novel genes may       expression reflects these differences. Analysis
also be involved (Lobo et al. 2003). The            showed 960 differentially expressed brain tran-
molecular basis for increased aggressiveness in     scripts between high response and low response
different contexts appears to utilize some con-     to QMP bees, with particular differences in gene
served mechanisms, whether due to heredity          networks related to neural network structure
(i.e., Africanized vs. European strains), age       (Kocher et al. 2010a).
(young vs. old bees), or environment (exposure         Alarm pheromone is a releaser pheromone
to alarm pheromone). The fact that similar          that quickly stimulates an aggressive response
genes expressed in the brain influence aggres-      in workers (Winston 1987). Even though re-
sive response due to these different influences     sponse to alarm pheromone is very fast, gene
supports the argument that changes in the           expression changes still occur in the brain,
regulation of gene expression via cis-regulatory    particularly the immediate early gene c-Jun. In
mechanisms are at the heart of some forms of        addition to affecting c-Jun, a transcription factor
behavioral diversity (Alaux et al. 2009c).          involved in neural circuits, alarm pheromone
A.G. Dolezal and A.L. Toth

also affects behavioral responses to subsequent             In addition to studies in honey bees, the
exposure long after the initial aggressive response     power of a genomic approach to understanding
has ceased (Alaux and Robinson 2007). Further-          the molecular basis of chemical communication
more, while alarm pheromone results in increased        has been exemplified by findings made possible
behavioral activity, it also causes downregulation      by the recent sequencing of the Solenopsis
of genes involved in brain metabolism, posing           invicta fire ant genome. In fire ants, a single
interesting questions regarding the relationship        Mendelian factor in the form of different alleles
between brain metabolic activity and overall            at the Gp-9 locus determines if workers accept
behavior. Genes modulated by alarm pheromone            one or many queens. Gp-9 codes for an odorant-
also show overlap with those that are upregulated       binding protein, so it has been suggested that its
in highly aggressive Africanized honey bees. The        effects are due to modulation of pheromone
fact that the same genes have effects on aggressive     responses (Gotzek and Ross 2007). However,
behaviors in different contexts suggests that alarm     its effects are much more diverse, influencing a
pheromone-regulated genes were likely involved          number of different traits, including female size
in the evolution of different aggressiveness            and fecundity (Keller and Ross 1999; Gotzek
phenotypes in honey bees (Alaux et al. 2009c).          and Ross 2009). Facilitated by the sequencing
   Brood pheromone acts as both a primer                of the genome (Wurm et al. 2011), a recent
pheromone, acting in the long term to delay             investigation of the genomic region where Gp-9
foraging in young bees, and as a releaser               is located found that the Gp-9 allele is part of a
pheromone, stimulating foraging in old bees             heteromorphic chromosome, similar to a Y sex
(Le Conte et al. 2001). Brain gene expression           determination chromosome. Instead of deter-
profiling reflects these effects, as brood phero-       mining sex, these chromosomal differences help
mone causes different effects on bees of                maintain different intraspecific social pheno-
different ages: in young workers, brood phero-          types (Wang et al. 2013). While a similar social
mone upregulates genes associated with nursing          chromosome has not been identified in other
and downregulates genes associated with forag-          social insects, similar systems, increased access
ing, and does the inverse in old bees, supporting       to genomic tools can make novel discoveries
the argument that pheromones affect behavior            such as this possible.
by mediation of gene expression, even in
different contexts (Alaux et al. 2009b).                  3.2. Dance language
   Bees perceive pheromonal signals through an
incredibly sensitive and well-developed olfactory          In addition to a complex system of chemical
system. In insects, pheromones and other odorants       communication, honey bees are well known for
are carried to odorant receptors by odorant-            their dance language, in which returning
binding proteins or chemosensory proteins (Pelosi       foragers use mechanical signals (i.e., sound,
et al. 2005). The sequencing of the honey bee           vibration, and tactile interaction) to communi-
genome afforded an opportunity to explore the           cate the location of food in the environment to
full complement of ORs and OBPs in honey bees           bees within the hive (Dyer 2002).
and indicated that there has been an evolutionary       Transcriptomic profiling of the nervous systems
expansion of the number of olfactory proteins in        of dancing foragers identified gene expression
honey bees compared to other, nonsocial insects         changes linked to dancing, with differences
(Foret and Maleszka 2006). Interestingly, even          particularly found in the mushroom bodies of
though the antennae are the site of odorant             the brain. Comparisons with dancing foragers
sensation, both odorant-binding proteins (Foret         from Apis florea and Apis dorsata identified
and Maleszka 2006) and chemosensory proteins            species-specific and species-consistent genes
(Foret et al. 2007) are commonly expressed in           related to dancing behavior. Further analysis
other tissue, indicating their possible role in other   of between-species differences, like those
physiological functions.                                linked to motor control and metabolism, may
Honey bee sociogenomics

provide further insights into how genetic              which differ in many more aspects than bees
differences between these species underlie the         differentially performing vibrational signals. Inter-
differences in their dance language phenotypes         estingly, some of the genes differentially expressed
(Sen Sarma et al. 2009). Furthermore, dancing          in these signaling bees are those associated with
bees that perceive the location of food to be          motor activities like locomotion courtship (Alaux
further away have different gene expression            et al. 2009a).
profiles than those perceiving food to be
nearby, particularly in the mushroom bodies               4. CASTE POLYPHENISM
and optic lobes, with notable differences in
learning and memory systems (Sen Sarma et                 4.1. Queen–worker developmental
al. 2010). In addition, differences in mushroom        differentiation
body gene expression arise as bees accrue
foraging experience (Lutz et al. 2012), further            In honey bees, the reproductive division of
indicating the importance of genomic changes           labor between queens and workers is based on
in the regulation of behavioral plasticity,            strict caste polyphenism, with extreme differences
especially in the brain.                               in physiology, morphology, and behavior between
    Communication systems have also been impli-        reproductive queens and functionally sterile
cated in the evolution and diversification of          workers. The differences between the castes are
sociality in bees. By using next-generation se-        determined due to differential feeding at critical
quencing for the rapid generation of transcriptomes    stages during larval development; workers are fed
of nine different bee species, spanning three          a restricted diet, while queens receive a diet richer
independent eusocial origins, researchers conduct-     in royal jelly (Winston 1987). Changes in diet
ed genome-scale comparative analyses to deter-         cause a cascade of changes in gene expression and
mine which genes show evidence of more rapid           hormone signaling that result in the production of
rates of protein evolution and how these relate to     different caste phenotypes. Larval consumption of
different levels of sociality. The results indicated   a diet rich in royal jelly, and specifically the
that gland development genes were rapidly evolv-       protein royalactin (Kamakura 2011), results in
ing in eusocial lineages including honey bees. This    increased JH levels (Rembold 1987; Rachinsky
suggests that glandular structures and their chem-     and Hartfelder 1990) which are involved in
ical products, likely used for increased social        triggering queen development (Rembold et al.
communication, were targets of selection during        1974). Screening of whole-body gene expression
eusocial evolution (Fischman et al. 2011; Woodard      showed that many of the genes overexpressed in
et al. 2011). In addition to the work on pheromones    queen-destined larvae were linked to metabolism
and dance, there has been some inquiry into the        and hormone responsiveness (Evans and Wheeler
sociogenomics of honey bee vibrational signals.        2001; Cristino et al. 2006; Barchuk et al. 2007). In
These signals are produced when some bees grasp        particular, insulin receptor and insulin receptor
a nestmate and rapidly vibrate, resulting in the       substrate, components of the IIS pathway, were
recipient bee changing its behavior in a context-      overexpressed in queen-destined larvae (Wheeler
dependent manner (Schneider and Lewis 2004).           et al. 2006) during times where JH content also
Using a microarray, researchers showed that brain      rises (Rembold 1987). The identification of
gene expression differs in over 900 genes, with        metabolic genes as possible modulators of JH
around half upregulated and half downregulated, in     signaling and queen development led to further
bees that send these signals vs. those that do not.    investigations, ultimately showing the importance
This is particularly notable because the number of     the epidermal growth factor receptor pathway as a
differentially expressed genes linked to this vibra-   modulator of queen–worker differentiation, trig-
tional signal is surprisingly substantial. For com-    gered by the ingestion of royalactin (Kamakura
parison, around 1,300 genes are differentially         2011). In addition to the differences found during
expressed between young nurses and old foragers,       development, genomic analyses have shown
A.G. Dolezal and A.L. Toth

significant differences in gene expression between     even though brain methylation does not differ in
adult queens and workers. Microarray screening         newly emerged queens and workers (Herb et al.
of adult brains showed that approximately 2,000        2012). How does methylation affect expression?
genes are differentially expressed in the brains of    Methylation does not appear to be closely tied to
queens and workers and over 200 of these are           differential upregulation or downregulation of
expressed in a more queen-like manner in               genes. Rather, methylation is often clustered in
reproductive workers, identifying a set of genes       areas of genes where splicing occurs, suggesting
likely involved in reproductive activity, regardless   that methylation may be involved in the regulation
of caste (Grozinger and Robinson 2007). Further        of alternative splice variants (Flores et al. 2012;
investigation into the transcriptomic differences      Foret et al. 2012). Also, methylation differences
between queen-destined and worker-destined lar-        occurred on genes coding for some histones,
vae used next-generation RNA-Seq technology to         proteins that are also epigenetically regulated
provide a more complete catalog of transcriptional     and can affect chromatin structure and gene
differences than previously possible using ESTs        expression (Lyko et al. 2010) and which may
or microarrays. These comparisons identified over      have an important role in the regulation of bee
4,000 differentially expressed genes and clarified     development (Dickman et al. 2013). Methylation
the dynamics of TOR expression, showing that           differences are also associated with caste differ-
differences are greatest between queens and            ences of other eusocial insect species, including
workers during the fourth (of five) larval instar      several ant species (Bonasio et al. 2012; Smith et
(Chen et al. 2012).                                    al. 2012) and Polistes wasps (Weiner et al. 2013),
    Another aspect of honey bee development that       indicating the possible importance of epigenetic
has been identified due to the expansion of            modifications in the convergent evolution of
genomic tools is the importance of epigenetic          eusocial societies.
effects caste determination. Epigenetic modifica-
tions occur when chemical modifications to DNA           4.2. Reproductive behavior
take place in response to an environmental
stimulus. Such modifications do not affect the            Genomic tools have also been used to
DNA sequence, but cause structural changes in          investigate the molecular underpinnings of
chromatin that can result in alterations in gene       reproductive activation in both queens and
expression that may last across an individual’s        workers. Since queen bees express extreme
lifetime or even across generations. One form of       differences in behavior and physiology before
epigenetic modification is DNA methylation, in         and after mating, they are an excellent model in
which methyl groups are attached to nucleotides,       which to investigate the changes that occur with
usually CpG dinucleotides, and have the potential      mating. Even though their mating biology is
to affect the expression of methylated sequences       very different, changes in brain and ovary
(Bird 2007). In honey bees, dynamic “de novo”          transcriptional profiles in honey bee queens
methylation is driven by DNA methyltransferase-        overlapped with those observed in Drosophila
3 (DNMT3). Experimental silencing of DNMT3             melanogaster females, indicating that the regu-
in developing larvae prevents the attenuation of       lation of post-mating behavior may be strongly
gene expression and mimics the response to a diet      conserved across insect taxa (Kocher et al.
rich in royal jelly (Kucharski et al. 2008).           2008, 2010b). Though both involve individual
Furthermore, over 2,000 genes are differentially       behavioral changes, there was no clear relation-
methylated in worker-destined larvae compared to       ship between genes associated with queen
queen-destined larvae, with the majority being up-     mating behavior and worker behavioral matura-
methylated in worker-destined larvae (Foret et al.     tion (Kocher et al. 2008).
2012). While not as drastic, there are also               “Anarchistic” bees are an unusual strain of
methylation differences in 550 genes in the brains     honey bees where workers develop ovaries and
of adult queens and workers (Lyko et al. 2010),        lay viable eggs, even in the presence of a laying
Honey bee sociogenomics

queen. This cheating behavior is partially          nisms are involved in reproductive division of
explained by four QTLs found in anarchistic         labor in these species, while worker behavioral
workers (Oxley et al. 2008). Screening of the       regulation shows more conservation (Toth et al.
heads and abdomens showed that wild-type            2010). Similarly, transcriptomic profiling of P.
workers have more genes upregulated than            canadensis queen and worker brains showed
anarchistic workers, and it has thereby been        little overlap with honey bee caste-specific
hypothesized that egg laying may be the default     genes (Ferreira et al. 2013). A comparison of
and that normal workers upregulate genes that       gene expression profiles between two species of
“switch off” ovarian activation (Thompson et al.    adult and pupal fire ant that analyzed the whole
2006). Anarchistic bees do show significant         bodies of queens, workers, and males showed
upregulation of some genes in the head and          that, while gene expression differences occurred
abdomen, particularly in vitellogenin, involved     between queens and workers, the greatest
in ovarian activation, and AdoHycase, which is      interspecific gene expression differences were
possibly involved in the regulation of DNA          found between adult workers (Ometto et al.
methylation (Thompson et al. 2008). Similarly,      2011). P. canadensis RNA-Seq data suggest that
a genome-wide comparison of gene expression         genes that are worker-biased in their expression
in the whole bodies of workers showed that          are more likely to be “novel,” with no homol-
over 1,200 genes are differentially expressed in    ogy to known sequences, further suggesting that
normal workers vs. workers that became repro-       molecular evolution occurs more rapidly in
ductive due to queenlessness. Reproductive          genes of importance to the worker caste
workers overexpressed genes involved in repro-      (Ferreira et al. 2013).
ductive activation, compared to nonreproductive         The use of comparative bioinformatic anal-
workers, which exhibited increased expression       ysis on existing datasets has become a useful
of genes involved in flight metabolism and          tool as genomic technologies have advanced
foraging. Therefore, gene expression compari-       and the amount of sequence data for honey bees
sons indicated differences in reproductive acti-    and other insects has drastically increased. This
vation, as would be expected, but also              approach can be exemplified by studies seeking
differences in overall activity levels and behav-   to better understand rates of gene evolution in
ioral performance between these different phe-      queens and workers. By honing in on previous-
notypes (Cardoen et al. 2011).                      ly identified genes with worker-biased or queen-
   Comparisons of the genomics of caste and         biased expression, it was possible to compare
reproduction between honey bees and other           rates of amino acid substitution of these genes
eusocial insect species has also helped elucidate   across honey bees and various nonsocial insects
how different genomic components could be           for which genomic sequence data were avail-
involved in eusocial evolution. Identification of   able. A comparison of queen-biased genes with
gene expression profiles of whole bodies for        worker-biased or non-biased genes showed that
adult queens and workers of the paper wasp          proteins associated with the queen caste had
Polistes canadensis allowed interspecies com-       evolved more rapidly than other proteins,
parisons, identifying nine genes with conserved     suggesting that selective pressure acted strongly
caste function across species from four different   on queen caste genes (Hunt et al. 2010).
origins of eusociality, including bees, ants, and   Another study, however, predicted that novel
wasps (Sumner et al. 2006). A microarray study      genes would be necessary for the evolution of
of P. metricus brains further showed that, while    complex social behaviors and, given that the
wasps had different gene expression profiles        majority of these behaviors occur in workers,
based on their reproductive status, there was no    worker-biased genes should be more likely to be
significant overlap between wasp reproductive       novel. Their analysis showed that, indeed, the
genes and genes involved in honey bee caste         worker caste expresses more genes specific to
differences. This suggests that different mecha-    social insect taxa than the queen caste. Howev-
A.G. Dolezal and A.L. Toth

er, while novel worker behaviors may have            different genotypes. When high-hygiene and
arisen from novel genes, it is also possible that    low-hygiene worker genotypes are mixed within
rapid evolutionary change still occurred in the      the same colony, indirect social effects cause
queen caste, albeit acting upon ancestral genes      behavioral changes. Specifically, low-hygiene
(Johnson and Tsutsui 2011).                          bees increase their hygienic behavior and
                                                     exhibit changes in brain gene expression when
  5. HONEY BEE HEALTH                                mixed with high-hygiene nestmates (Gempe et
                                                     al. 2012). Therefore, while genotypic effects are
   While the primary goal of sociogenomic            clearly important, changes in the social envi-
studies is to provide a fuller understanding of      ronment have strong potential to increase bee
the evolution and maintenance of sociality, the      hygiene.
tools and approaches that were spawned from             With sequencing of the honey bee genome,
sociogenomics have been applied to other             broad-scale investigation of the genetic path-
questions in honey bee biology. In particular,       ways involved in honey bee immune response
increased applications of genome-level investi-      became more tractable. Comparisons of the
gation has helped provide a better understand-       honey bee genome with that of Drosophila flies
ing of how a variety of factors influence honey      and Anopheles mosquitoes first indicated that
bee health. Therefore, while the original intent     honey bees, despite the higher pathogen risks
of sociogenomics was to investigate basic            associated with colonial living, actually possess
questions in evolution, behavior, and physiolo-      substantially fewer immunity-associated genes.
gy, this work has quickly provided key infor-        This suggests that, among other possibilities,
mation for addressing applied questions. With        selective pressure on disease prevention has
increasing worldwide concerns regarding polli-       acted predominantly on social behavioral re-
nator health (Gallai et al. 2009), a better          sponses to disease (such as hygienic behavior)
understanding of how honey bees respond to           and not individual innate immune responses
health stresses on a genomic scale is of great       (Evans et al. 2006). Despite this, the immune
utility. Stresses implicated in honey bee declines   genes in honey bees show higher rates of
include pesticides (Mullin et al. 2010), nutrition   evolution than those of Drosophila or nonim-
(Naug 2009), and disease. Honey bees suffer          mune honey bee genes (Viljakainen et al. 2009).
from a number of diseases caused by bacteria,        Further investigation showed that bacterial
viruses, and fungi (Evans and Schwarz 2011)          immunostimulation of honey bees results in
and are affected by a number of pests, most          changes in expression of hundreds of genes,
notably the Varroa destructor mite (Rosenkranz       many of which are not normally associated with
et al. 2010).                                        immune response. Changes in some of these,
   Given the highly social nature of honey bee       particularly those related to chemical signaling,
colonies, response to disease stress occurs at       suggest that changes in expression of nonim-
both the individual and group levels. Individual     mune response genes help to orchestrate behav-
bees respond to immune stress via cellular and       ioral changes, such as increased grooming
humoral mechanisms, similar to other insects,        (Wilson-Rich et al. 2009), that mitigate patho-
but colonies also exhibit social mechanisms          gen risks (Richard et al. 2012).
(Wilson-Rich et al. 2009), such as high levels of       This hypothesis was further supported
hygienic behavior (Rothenbuhler 1964), to            through investigation of the effects of Varroa
prevent the spread of disease. Hygienic behav-       infestation on gene expression. When Varroa
ior, characterized by uncapping of pupal cells       infestation occurs, many gene expression
and the removal of diseased pupae, has been          changes occur, but bees with naturally higher
linked to several QTLs (Oxley et al. 2010).          tolerance to Varroa more highly express genes
Interestingly, hygienic behavior is also affected    associated with olfaction and stimulus sensitiv-
by interactions between individual workers of        ity, not immunity (Navajas et al. 2008). A
Honey bee sociogenomics

similar study comparing typical colonies with       parasite interactions (Cornman et al. 2010), and
those from another lineage of Varroa-resistant      RNA deep sequencing has helped to identify
bees, the Varroa-sensitive hygiene (VSH) strain,    novel strains of bee viruses (Cornman et al. 2012,
which have been selected for resistance to mite     2013). Metagenomic analyses, where researchers
infestation via increased hygienic behavior, also   screen diverse genetic material directly from the
did not implicate immune genes in the resis-        environment, have also been useful for under-
tance to mite infestation. Furthermore, compar-     standing bee health. A metagenomic survey of
ison of VSH bees with the Varroa-resistant bees     honey bees from colonies suffering from CCD
from Navajas et al. (2008) showed little overlap    helped to identify pathogens, specifically Israeli
in gene expression between the two sources of       acute paralysis virus, that were associated with
Varroa-resistant bees. Instead, VSH bees had a      that form of colony loss (Cox-Foster et al. 2007).
similar profile to bees stimulated with brood       Further, metagenomic screening of the microbiota
pheromone, indicating a possible connection to      of the honey bee gut suggests that a suite of
brood care phenotypes, and with Africanized         different bacteria in healthy bee guts have a role in
honey bees, which are also very hygienic            pathogen defense and nutrient utilization (Engel et
towards Varroa (Le Conte et al. 2011). Another      al. 2012).
investigation compared Varroa effects on gene
expression in Apis mellifera and the mite-             6. CONCLUSIONS
resistant congener Apis cerana, finding signifi-
cant differences in genes associated with me-          As genomic tools have become available to
tabolism and nerve signaling (Zhang et al.          honey bee researchers, more facets of honey bee
2010). These studies indicate that there may be     biology have been investigated using a
several distinct genomic routes to behavioral       sociogenomic approach. Worker behavioral
mite resistance.                                    maturation, the transition from nurse to forager,
   Further experimentation showed how genes         has received the most attention. Integration of
that are upregulated by pollen consumption, like    genomic studies with the strong background
those involved in protein metabolism, are           knowledge of this system from behavioral,
downregulated due to Varroa infestation (and        ecological, physiological, and genetic studies
the accompanying viruses that mites vector).        has provided the most comprehensive charac-
This provides insight into how mites may stress     terization of any component of honey bee
bees nutritionally and thus, supplies clues that    biology (Figure 1). These investigations stand
may be helpful in preventing some pest or           as the best examples of how fruitful a
pathogen effects (Alaux et al. 2011). Gut           sociogenomic approach can be. In particular,
microarray analysis of bees suffering from          the work exploring how transcription factors are
colony collapse disorder (CCD) identified a list    involved in regulating large-scale gene expres-
of 65 transcripts that may be markers for CCD,      sion changes has helped to focus in on key
as well as the increased presence of ribosomal      transcriptional networks; this information can
RNA fragments in CCD colonies, possibly due         easily be buried in the data deluge from large-
to increased viral infections (Johnson et al.       scale transcriptomic analyses. By understanding
2009).                                              how some transcription factors could act as
   Genomic methods have also been applied to        genomic “hubs” to interact and control net-
the pests, pathogens, and beneficial microorgan-    works of gene expression, this approach has
isms that affect honey bees. While not using the    helped bring new understanding to how com-
honey bee genome itself, these studies still show   plex transcriptional networks regulate pheno-
how the expansion of genomic approaches are         types and how transcription factors could be at
helping build a better understanding of honey bee   the heart of a genetic toolkits that have been
biology. Genomic tools developed for Varroa         used by natural selection to build diverse
provide useful methods for understanding host–      phenotypes (Sinha et al. 2006; Chandrasekaran
A.G. Dolezal and A.L. Toth

et al. 2011; Ament et al. 2012a, b). It is worth      shared and novel genes and pathways that are
noting, however, that most of these studies           involved in the convergent evolution of similar
provide predominantly correlative data (i.e.,         social traits and analyses can help identify genes
between transcriptome and phenotype), and as          that have evolved rapidly to build social
such, functional analyses on these genes and          phenotypes from solitary traits.
pathways are still mostly lacking. Future work
focusing on filling in these gaps will be vital for
building a fuller understanding of the genomic        Glossary of terms: Words rendered in bold
aspects of honey bee biology.                         font in the body of the text are defined here.
   Further genomic investigations will also likely
identify other players in honey bee social organi-    Bioinformatics: The use of computational tech-
zation. For example, the detection of microRNAs       niques to manage and analyze large quantities
in the honey bee genome has only recently begun       of information from biological systems, pre-
to reveal the importance of these small, noncoding    dominantly genomic and transcriptomic data
regions of RNA that regulate gene expression.         (Hogeweg 2011).
After computational identification (Weaver et al.
2007), experimental studies have begun to exam-       Caste: Term used to describe a group of
ine how microRNAs may have a role in behav-           individuals in social insect colonies that spe-
ioral maturation (Behura and Whitfield 2010;          cializes, to some extent, in specific occupations
Greenberg et al. 2012; Liu et al. 2012), showing      as a result of division of labor. Social insect
their potential importance in division of labor.      castes can be associated with differences in age,
Future studies on microRNAs will likely help to       anatomy, and morphology.
clarify their placement in the network of factors
that affect honey bee behavior.                       cis-regulatory elements: A sequence of DNA
   As genomic tools become more advanced              which, via the binding of transcription factors or
and accessible, the study of more honey bee           other proteins, regulates the expression of a gene
phenotypes at the genomic level become more           or genes on the same chromosome (Wray 2007).
tractable, providing a clearer vision of honey
bee behavior, communication, development,             Division of labor: A social system in which
and health. Future work will also likely provide      individuals specialize in specific occupations. In
a more complete picture of the chain from             insect societies, queens mostly reproduce, where-
genome to phenotype. Though the honey bee             as workers engage in all tasks related to colony
genome provided new insights into bee proteo-         growth and development. Young workers tend to
mics (Wolschin and Amdam 2007), most                  work in the nest, whereas older individuals forage
studies assume that changes in mRNA expres-           outside the nest.
sion reliably represent changes in protein
expression, which lies closer to the actual           DNA methylation: A form of epigenetic modifi-
phenotype. As proteomic techniques, like the          cation in which methyl groups are attached to
ability to perform large-scale proteomic analy-       nucleotides, usually CpG dinucleotides, have the
ses (Hernandez et al. 2012), continue to im-          potential to affect the expression of methylated
prove, the link between genes, proteins, and          sequences (Bird 2007).
organismal phenotype should become clearer.
   In addition, genomic studies are becoming          Epigenetics: Environmental mediation of an
increasingly possible in solitary Hymenoptera         individual’s genome and/or its descendants, with-
and other social insects, like wasps, ants, and       out changes in DNA sequence, via mechanisms
bees. Comparisons with these other species            like DNA methylation and histone modification
allow for insights for identification of both         (Crews 2008).
Honey bee sociogenomics

Eusocial: Traditionally defined as social species     Quantitative trait loci (QTL): Sections of
that show three features: extreme asymmetries         DNA sequence (loci) that contain or are
in reproduction, with some individuals repro-         linked to quantitative trait. QTLs can also
ducing a great deal and others little or not at       be mapped to whole or partial genomes to
all; overlapping generations of adults in the         further identify genes associated with the trait
nest; and cooperative care of offspring (Wilson       of interest (Erickson et al. 2004).
1971).
                                                      Queen mandibular pheromone (QMP): Phero-
Expressed sequence tags (EST): ESTs are pro-          mone produced by honey bee queens to regulate
duced by sequencing many clones from cDNA             the behavior and reproductive physiology of
libraries; since the sequences from these cDNA        workers (Winston and Slessor 1998).
libraries are originally derived from mRNA from
the organism of interest, ESTs provide important      RNA-Seq: A form of transcriptomic profiling
information regarding what genes are being            where high-throughput sequencing of all the cDNA
expressed (Gerhold and Caskey 1996).                  contained in a sample provides precise measure-
                                                      ments of gene expression (Wang et al. 2009).
Genome: The complete genetic code for an
organism.                                             Single-cohort colonies: Behavioral manipulation
                                                      in which hives are created solely from young
Genetic toolkit: The concept that conserved genes     workers. This modification of normal age demog-
and pathways have similar roles across a variety      raphy results in newly formed colonies that lack
of taxa, helping to “build” different phenotypes      foragers, and young workers subsequently transi-
from the same “tools,” resulting in diverse           tion to foraging behaviors earlier than normal,
phenotypes regulated by similar factors (Toth         allowing researchers to compare same-aged indi-
and Robinson 2007).                                   viduals that perform different tasks (Nelson 1927;
                                                      Robinson et al. 1989).
Insulin/insulin-like signaling (IIS): Metabolic
pathway that acts as a key regulator of growth,       Target of rapamycin (TOR): An important meta-
feeding behavior, and metabolism; in insects, it      bolic regulator that interacts with the IIS pathway
also interacts with target of TOR and JH (Edgar       (Tu et al. 2005).
2006, Tu et al. 2005).
                                                      Transcription factor: A protein that binds to a
Juvenile hormone (JH): Insect hormone involved        regulatory DNA segment, regulating the transcrip-
in many behavioral and developmental processes,       tion of specific target genes into mRNA.
including onset of foraging behavior in honey bees
(Hartfelder 2000).

Microarray: Technology that allow for the quan-       Sociogénomique de l’abeille: une perspective à
tification of gene expression via the hybridization   l’échelle génomique sur le comportement social et la
                                                      santé de l’abeille
of cDNA to complementary sequences on a chip
(Schena et al. 1995), used in conjunction with        Génome / division du travail / maturation
ESTs to quantify known genes.                         comportementale / caste / génomique comparative
                                                      Honigbienen-Soziogenomik: Eine genomweite Sicht
MicroRNA: A small section of noncoding                auf das Sozialverhalten und die Gesundheit von
                                                      Honigbienen
RNA that has transcriptional and posttransla-
tional effects on gene expression (Chen and           Genom / Arbeitsteilung / altersbedingte
Rajewsky 2007).                                       Verhaltensreifung / Kaste / vergleichende Genomik
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