Neuromolecular responses to social challenge: common mechanisms across mouse, stickleback fish, and honey bee

Laboratory Assay of Brood Care for Quantitative Analyses of Individual Differences in Honey Bee (Apis mellifera) Affiliative Behavior

Care of offspring is a form of affiliative behavior that is fundamental to studies of animal social behavior. Insects do not figure prominently in this topic because Drosophila melanogaster and other traditional models show little if any paternal or maternal care. However, the eusocial honey bee exhibits cooperative brood care with larvae receiving intense and continuous care from their adult sisters, but this behavior has not been well studied because a robust quantitative assay does not exist. We present a new laboratory assay that enables quantification of group or individual honey bee brood “nursing behavior” toward a queen larva. In addition to validating the assay, we used it to examine the influence of the age of the larva and the genetic background of the adult bees on nursing performance. This new assay also can be used in the future for mechanistic analyses of eusociality and comparative analyses of affilative behavior with other animals.

Early-life experience affects honey bee aggression and resilience to immune challenge

Early-life social experiences cause lasting changes in behavior and health for a variety of animals including humans, but it is not well understood how social information ''gets under the skin'' resulting in these effects. Adult honey bees (Apis mellifera) exhibit socially coordinated collective nest defense, providing a model for social modulation of aggressive behavior. Here we report for the first time that a honey bee's early-life social environment has lasting effects on individual aggression: bees that experienced high-aggression environments during pre-adult stages showed increased aggression when they reached adulthood relative to siblings that experienced low-aggression environments, even though all bees were kept in a common environment during adulthood. Unlike other animals including humans however, high-aggression honey bees were more, rather than less, resilient to immune challenge, assessed as neonicotinoid pesticide susceptibility. Moreover, aggression was negatively correlated with ectoparasitic mite presence. In honey bees, early-life social experience has broad effects, but increased aggression is decoupled from negative health outcomes. Because honey bees and humans share aspects of their physiological response to aggressive social encounters, our findings represent a step towards identifying ways to improve individual resiliency. Pre-adult social experience may be crucial to the health of the ecologically threatened honey bee.

Social Regulation of Gene Expression in Threespine Sticklebacks

Identifying genes that are differentially expressed in response to social interactions is informative for understanding the molecular basis of social behavior. To address this question, we described changes in gene expression as a result of differences in the extent of social interactions. We housed threespine stickleback (Gasterosteus aculeatus) females in either group conditions or individually for one week, then measured levels of gene expression in three brain regions using RNA-sequencing. We found that numerous genes in the hindbrain/cerebellum had altered expression in response to group or individual housing. However, relatively few genes were differentially expressed in either the diencephalon or telencephalon. The list of genes upregulated in fish from social groups included many genes related to neural development and cell adhesion as well as genes with functions in sensory signaling, stress, and social and reproductive behavior. The list of genes expressed at higher levels in individually-housed fish included several genes previously identified as regulated by social interactions in other animals. The identified genes are interesting targets for future research on the molecular mechanisms of normal social interactions.

Neural transcriptome reveals molecular mechanisms for temporal control of vocalization across multiple timescales

Background

Vocalization is a prominent social behavior among vertebrates, including in the midshipman fish, an established model for elucidating the neural basis of acoustic communication. Courtship vocalizations produced by territorial males are essential for reproductive success, vary over daily and seasonal cycles, and last up to hours per call. Vocalizations rely upon extreme synchrony and millisecond precision in the firing of a homogeneous population of motoneurons, the vocal motor nucleus (VMN). Although studies have identified neural mechanisms driving rapid, precise, and stable neuronal firing over long periods of calling, little is known about underlying genetic/molecular mechanisms.

Results

We used RNA sequencing-based transcriptome analyses to compare patterns of gene expression in VMN to the surrounding hindbrain across three daily and seasonal time points of high and low sound production to identify candidate genes that underlie VMN’s intrinsic and network neuronal properties. Results from gene ontology enrichment, enzyme pathway mapping, and gene category-wide expression levels highlighted the importance of cellular respiration in VMN function, consistent with the high energetic demands of sustained vocal behavior. Functionally important candidate genes upregulated in the VMN, including at time points corresponding to high natural vocal activity, encode ion channels and neurotransmitter receptors, hormone receptors and biosynthetic enzymes, neuromodulators, aerobic respiration enzymes, and antioxidants. Quantitative PCR and RNA-seq expression levels for 28 genes were significantly correlated. Many candidate gene products regulate mechanisms of neuronal excitability, including those previously identified in VMN motoneurons, as well as novel ones that remain to be investigated. Supporting evidence from previous studies in midshipman strongly validate the value of transcriptomic analyses for linking genes to neural characters that drive behavior.

Conclusions

Transcriptome analyses highlighted a suite of molecular mechanisms that regulate vocalization over behaviorally relevant timescales, spanning milliseconds to hours and seasons. To our knowledge, this is the first comprehensive characterization of gene expression in a dedicated vocal motor nucleus. Candidate genes identified here may belong to a conserved genetic toolkit for vocal motoneurons facing similar energetic and neurophysiological demands.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1577-2) contains supplementary material, which is available to authorized users.

Integrating motif, DNA accessibility and gene expression data to build regulatory maps in an organism

Characterization of cell type specific regulatory networks and elements is a major challenge in genomics, and emerging strategies frequently employ high-throughput genome-wide assays of transcription factor (TF) to DNA binding, histone modifications or chromatin state. However, these experiments remain too difficult/expensive for many laboratories to apply comprehensively to their system of interest. Here, we explore the potential of elucidating regulatory systems in varied cell types using computational techniques that rely on only data of gene expression, low-resolution chromatin accessibility, and TF–DNA binding specificities (‘motifs’). We show that static computational motif scans overlaid with chromatin accessibility data reasonably approximate experimentally measured TF–DNA binding. We demonstrate that predicted binding profiles and expression patterns of hundreds of TFs are sufficient to identify major regulators of ∼200 spatiotemporal expression domains in the Drosophila embryo. We are then able to learn reliable statistical models of enhancer activity for over 70 expression domains and apply those models to annotate domain specific enhancers genome-wide. Throughout this work, we apply our motif and accessibility based approach to comprehensively characterize the regulatory network of fruitfly embryonic development and show that the accuracy of our computational method compares favorably to approaches that rely on data from many experimental assays.