Nutrition, Hormones, Transcriptional Regulatory Networks and Division of Labor in Honey Bee Colonies

Phenotypic plasticity – one genotype producing alternative phenotypes – is increasingly understood to be an important force in phenotypic evolution, but its mechanistic basis remains poorly understood. This thesis describes research into the molecular mechanisms underlying age-related behavioral and physiological plasticity in worker honey bees. Many animals are able to alter their behavior and physiology in response to changes in the environment. At times, these changes in behavior and physiology are stable for long periods, a phenomenon known as phenotypic plasticity [1]. For instance, short periods of food deprivation stimulate feeding and the mobilization of stored nutrients to meet an individual’s immediate energetic needs. But prolonged food deprivation can also lead to much longer-term effects, causing individuals to enter extended periods of inactivity, alter their reproductive strategy, or lose their position in a dominance hierarchy. In humans, chronic food deprivation early in life may lead to a propensity toward obesity and diabetes in later life (for an expanded and fully-referenced discussion of nutritionally-mediated phenotypic plasticity see Chapter 4). The mechanisms that enable and constrain plasticity in behavior and physiology are not well understood, but it is clear that they often involve coordinated and long-lasting changes in gene expression, brain circuitry, brain chemistry, and endocrine signaling [2]. My doctoral research has focused on understanding the molecular basis for nutritionally- and hormonally-mediated plasticity in the behavior and physiology of worker honey bees. Honey bees are social insects, living together in colonies containing tens of thousands of individuals [3]. Colony life is organized by a complex and sophisticated division of labor. Each colony contains a single queen, who is specialized for reproduction and spends most of her time laying eggs. Males, called drones, are relatively rare, and their sole role is to mate. The vast majority of the individuals in the hive are sterile worker bees that are responsible for all of the other tasks performed by the colony. The tasks performed by worker bees are further divided up among individuals via a process of behavioral maturation that is the focus of this thesis. For the first 2-3 weeks of adult life, worker bees specialize on broodcare (“nursing”). They then switch for a few days to any of a number of more specialized tasks such as building honeycomb cells, storing food in honeycomb cells, or guarding the hive entrance against intruders. Finally, for the remaining 1-2 weeks of their life, worker bees forage outside the hive for nectar and pollen, the colony’s sole sources of food. The work presented in this thesis builds on previous findings demonstrating links between worker honey bee division of labor and nutrition (reviewed in Chapter 4). Behavioral maturation in worker bees is coupled to changes in nutritional physiology, including a dramatic and stable loss of abdominal lipid that occurs prior to the onset of foraging. Moreover, previous studies had demonstrated that nutritional status can have causal influences on the timing of behavioral maturation and manipulations of a few feeding- or nutritionally-related genes accelerates or delays the age at onset of foraging. In the work described here, I first test the hypothesis that worker bee behavioral maturation, a highly derived trait, is regulated, in part, by conserved nutritionally-related hormones (Chapter 1). I demonstrate that genes related to insulin signaling are differentially expressed in the brains and fat bodies of nurses and foragers. Furthermore, I show that manipulation of the insulin-related TOR pathway influences the age at which bees initiate foraging. These results suggest that the evolution of honey bee social behavior involved new roles for ancient nutritionally-related pathways. However, my subsequent work shows that not all nutritionally-related pathways have been coopted in the same way. I describe a more complex, and less resolved, relationship between behavioral state, nutrition and brain gene expression for a second nutritionally-related hormone, Neuropeptide Y (Chapter 2). Next, using transcriptomic experiments, I demonstrate that maturation, as well as age-related stable lipid loss, involve massive changes in gene expression in the fat bodies (Chapter 3). I show that these changes in gene expression involve age-related changes in the responsiveness of hormonally and metabolically related pathways to nutrition, and roles for two evolutionarily novel, non-dietary factors: the storage protein vitellogenin and Queen Mandibular Pheromone, each of which influenced many maturationally-related genes in the fat bodies. These results also suggest the involvement in the responses to all these factors of a single nutritionally-related hormone, juvenile hormone (JH), which had previously been shown to pace behavioral maturation. In Chapter 4, I review my findings from chapters 1-2 of this thesis, and previous studies, and propose a molecular systems biology approach to understanding division of labor. Specifically, I propose that phenotypic plasticity in worker honey bees involves nutritionally- and hormonally-driven changes in transcriptional regulatory networks in the fat bodies (as well in the brain), and I suggest methodologies for their elucidation. Finally, in Chapter 5, I utilize the molecular systems biology approach outlined in Chapter 4 to show that a transcriptional regulatory network in the fat bodies underlies division of labor. I show that a juvenile hormone-related transcription factor, Ultraspiracle (USP), influences the age at onset of foraging. I then use a combination of chromatin immunoprecipitation—genomic tiling microarrays, RNAi and deep mRNA sequencing to develop a model of the USP transcriptional regulatory network in fat cells. My results suggest that JH and USP function together to induce and maintain alternative states of a transcriptional regulatory network. These alternative states may well underlie the two basic phases of worker bee life, the in-hive and foraging phases. Together, the studies presented in this thesis provide insights into the relationship between nutrition, hormones, transcriptional regulation, and phenotypic plasticity. References 1. West-Eberhard, MJ. Developmental Plasticity and Evolution. 2003. Oxford University Press, New York, NY. 794 pp. 2. Robinson, GE, Fernald, RD, Clayton, DF. Genes and social behavior. Science. 2008 Nov 7; 322(5903):896-900. doi:10.1126/science.1159277 3. Winston, ML. The Biology of the Honey Bee. 1987. Harvard University Press, Cambridge, MA. 294 pp

Rare variants implicate NMDA receptor signaling and cerebellar gene networks in risk for bipolar disorder

Bipolar disorder is an often-severe mental health condition characterized by alternation between extreme mood states of mania and depression. Despite strong heritability and the recent identification of 64 common variant risk loci of small effect, pathophysiological mechanisms remain unknown. Here, we analyzed genome sequences from 41 multiply-affected pedigrees and identified variants in 741 genes with nominally significant linkage or association with bipolar disorder. These 741 genes overlapped known risk genes for neurodevelopmental disorders and clustered within gene networks enriched for synaptic and nuclear functions. The top variant in this analysis - prioritized by statistical association, predicted deleteriousness, and network centrality - was a missense variant in the gene encoding D-amino acid oxidase (DAOG131V). Heterologous expression of DAOG131V in human cells resulted in decreased DAO protein abundance and enzymatic activity. In a knock-in mouse model of DAOG131, DaoG130V/+, we similarly found decreased DAO protein abundance in hindbrain regions, as well as enhanced stress susceptibility and blunted behavioral responses to pharmacological inhibition of N-methyl-D-aspartate receptors (NMDARs). RNA sequencing of cerebellar tissue revealed that DaoG130V resulted in decreased expression of two gene networks that are enriched for synaptic functions and for genes expressed, respectively, in Purkinje neurons or granule neurons. These gene networks were also down-regulated in the cerebellum of patients with bipolar disorder compared to healthy controls and were enriched for additional rare variants associated with bipolar disorder risk. These findings implicate dysregulation of NMDAR signaling and of gene expression in cerebellar neurons in bipolar disorder pathophysiology and provide insight into its genetic architecture

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types.

Characterizing the tissue-specific binding sites of transcription factors (TFs) is essential to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting enables the prediction of genome-wide binding sites for hundreds of TFs simultaneously. Despite the public availability of high-quality DNase-seq data from hundreds of samples, a comprehensive, up-to-date resource for the locations of genomic footprints is lacking. Here, we develop a scalable footprinting workflow using two state-of-the-art algorithms: Wellington and HINT. We apply our workflow to detect footprints in 192 ENCODE DNase-seq experiments and predict the genomic occupancy of 1,515 human TFs in 27 human tissues. We validate that these footprints overlap true-positive TF binding sites from ChIP-seq. We demonstrate that the locations, depth, and tissue specificity of footprints predict effects of genetic variants on gene expression and capture a substantial proportion of genetic risk for complex traits

The Transcription Factor Ultraspiracle Influences Honey Bee Social Behavior and Behavior-Related Gene Expression

Behavior is among the most dynamic animal phenotypes, modulated by a variety of internal and external stimuli. Behavioral differences are associated with large-scale changes in gene expression, but little is known about how these changes are regulated. Here we show how a transcription factor (TF), ultraspiracle (usp; the insect homolog of the Retinoid X Receptor), working in complex transcriptional networks, can regulate behavioral plasticity and associated changes in gene expression. We first show that RNAi knockdown of USP in honey bee abdominal fat bodies delayed the transition from working in the hive (primarily “nursing” brood) to foraging outside. We then demonstrate through transcriptomics experiments that USP induced many maturation-related transcriptional changes in the fat bodies by mediating transcriptional responses to juvenile hormone. These maturation-related transcriptional responses to USP occurred without changes in USP's genomic binding sites, as revealed by ChIP–chip. Instead, behaviorally related gene expression is likely determined by combinatorial interactions between USP and other TFs whose cis-regulatory motifs were enriched at USP's binding sites. Many modules of JH– and maturation-related genes were co-regulated in both the fat body and brain, predicting that usp and cofactors influence shared transcriptional networks in both of these maturation-related tissues. Our findings demonstrate how “single gene effects” on behavioral plasticity can involve complex transcriptional networks, in both brain and peripheral tissues

High resolution time-course mapping of early transcriptomic, molecular and cellular phenotypes in Huntington\u27s disease CAG knock-in mice across multiple genetic backgrounds.

Huntington\u27s disease is a dominantly inherited neurodegenerative disease caused by the expansion of a CAG repeat in the HTT gene. In addition to the length of the CAG expansion, factors such as genetic background have been shown to contribute to the age at onset of neurological symptoms. A central challenge in understanding the disease progression that leads from the HD mutation to massive cell death in the striatum is the ability to characterize the subtle and early functional consequences of the CAG expansion longitudinally. We used dense time course sampling between 4 and 20 postnatal weeks to characterize early transcriptomic, molecular and cellular phenotypes in the striatum of six distinct knock-in mouse models of the HD mutation. We studied the effects of the HttQ111 allele on the C57BL/6J, CD-1, FVB/NCr1, and 129S2/SvPasCrl genetic backgrounds, and of two additional alleles, HttQ92 and HttQ50, on the C57BL/6J background. We describe the emergence of a transcriptomic signature in HttQ111/+  mice involving hundreds of differentially expressed genes and changes in diverse molecular pathways. We also show that this time course spanned the onset of mutant huntingtin nuclear localization phenotypes and somatic CAG-length instability in the striatum. Genetic background strongly influenced the magnitude and age at onset of these effects. This work provides a foundation for understanding the earliest transcriptional and molecular changes contributing to HD pathogenesis

Transcriptional regulatory networks underlying gene expression changes in Huntington's disease

Abstract Transcriptional changes occur presymptomatically and throughout Huntington's disease (HD), motivating the study of transcriptional regulatory networks (TRNs) in HD. We reconstructed a genome‐scale model for the target genes of 718 transcription factors (TFs) in the mouse striatum by integrating a model of genomic binding sites with transcriptome profiling of striatal tissue from HD mouse models. We identified 48 differentially expressed TF‐target gene modules associated with age‐ and CAG repeat length‐dependent gene expression changes in Htt CAG knock‐in mouse striatum and replicated many of these associations in independent transcriptomic and proteomic datasets. Thirteen of 48 of these predicted TF‐target gene modules were also differentially expressed in striatal tissue from human disease. We experimentally validated a specific model prediction that SMAD3 regulates HD‐related gene expression changes using chromatin immunoprecipitation and deep sequencing (ChIP‐seq) of mouse striatum. We found CAG repeat length‐dependent changes in the genomic occupancy of SMAD3 and confirmed our model's prediction that many SMAD3 target genes are downregulated early in HD

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups