Reference Transcriptome for a Facultatively Eusocial Bee, Megalopta genalis

This article analyses gendering processes in two distinct models of work organization. It is a widespread belief that, compared to hierarchical (Tayloristic) organizations, team-based work offers opportunities for a high quality of working life to a broader range of employees, both men and women. Our research, however, suggests that gender inequality is (re)produced in both settings and results from the so-called gender subtext. The gender subtext is the set of often concealed power-based processes (re)producing gender distinction in social practices through organizational and individual arrangements. We draw a comparison between the gender subtext of Tayloristic and team-based work organizations through a theoretical analysis, illustrated by empirical data concerning the functioning of the gender subtext in organizations in the Dutch banking sector. Taylorism and team-based work differ in their conceptualization of organization and job design, but, when it comes to the gender subtext, it is six of one and half a dozen of the other. We argue that in both approaches a gender subtext contributes to the emergence of different but gendered notions of the ‘disembodied worker’. In both cases the notion of the abstract worker is implicitly loaded with masculine connotations. This gender bias is supported by two factors influencing the gendering of jobs: the gender connotations of care responsibilities and of qualification profiles. These implicit connotations produce and reinforce unequal opportunities for men and women to get highly qualified or management jobs. Our research, therefore, questions the self-evidence of stating that team-based work will offer opportunities for a higher quality of working life for women

Determining the Causal Link of Honey Bee Gut Microbial Composition on Behavioral Maturation

Emerging studies have supported the association between gut microbiome and host behaviors. However, it is unclear whether changes in the gut microbiome cause changes in host behaviors or vice versa. The European honey bee, Apis mellifera, is an excellent animal model for identifying the causal link between microbiome and behavioral changes over the lifetime of the host as the honey bee gut contains a simple microbiome composed of only nine bacterial taxa clusters. In honey bees, division of labor occurs through behavioral maturation where age determines what task a bee does. For example, older bees forage while younger bees perform brood care (nursing) and other in-hive tasks. Single cohort colonies (SCCs), or colonies composed of individuals of the same age, uncouple chronological age effects on honey bee behavioral maturation (nursing → foraging). SCCs results from our previous experiment reveal a highly significant difference in the gut microbiota between nurses and foragers, independent of age, specifically in the abundance of Lactobacillus mellis and Bifidobacterium asteroides

The Effect of Imidacloprid on Honey Bee Queen Fecundity

Imidacloprid is a neonicotinoid insecticide commonly used in agricultural settings to control insect pests by acting as an agonist of acetylcholine receptors and inducing paralysis and mortality. In small doses, imidacloprid can cause loss of memory and foraging ability along with impaired learning and a lowered immune response in western honey bees (Apis mellifera). Effects of neonicotinoid insecticides on colony reproduction have been documented including decreased colony expansion, queen failure and replacement, and decreased queen egg laying. For this study, we examined the effects of imidacloprid on the fecundity of queen bees when their worker attendants were exposed to low doses of imidacloprid through their food source using a novel, labbased, Queen Monitoring Cage (QMC) system. Our results will help elucidate the effect of imidacloprid on the egg laying behaviors of honey bee queens. By comparing the results generated using QMCs to previous studies using full-sized colonies, we will attempt to validate the use of QMCs as a risk assessment tool

Meta-analysis of honey bee neurogenomic response links deformed wing virus type A to precocious behavioral maturation

Crop pollination by the western honey bee Apis mellifera is vital to agriculture but threatened by alarmingly high levels of colony mortality, especially in Europe and North America. Colony loss is due, in part, to the high viral loads of Deformed wing virus (DWV), transmitted by the ectoparasitic mite Varroa destructor, especially throughout the overwintering period of a honey bee colony. Covert DWV infection is commonplace and has been causally linked to precocious foraging, which itself has been linked to colony loss. Taking advantage of four brain transcriptome studies that unexpectedly revealed evidence of covert DWV-A infection, we set out to explore whether this effect is due to DWV-A mimicking naturally occurring changes in brain gene expression that are associated with behavioral maturation. Consistent with this hypothesis, we found that brain gene expression profiles of DWV-A infected bees resembled those of foragers, even in individuals that were much younger than typical foragers. In addition, brain transcriptional regulatory network analysis revealed a positive association between DWV-A infection and transcription factors previously associated with honey bee foraging behavior. Surprisingly, single-cell RNA-Sequencing implicated glia, not neurons, in this effect; there are relatively few glial cells in the insect brain and they are rarely associated with behavioral plasticity. Covert DWV-A infection also has been linked to impaired learning, which together with precocious foraging can lead to increased occurrence of infected bees from one colony mistakenly entering another colony, especially under crowded modern apiary conditions. These findings provide new insights into the mechanisms by which DWV-A affects honey bee health and colony survival

Meta-analysis of genome-wide expression patterns associated with behavioral maturation in honey bees

<p>Abstract</p> <p>Background</p> <p>The information from multiple microarray experiments can be integrated in an objective manner <it>via </it>meta-analysis. However, multiple meta-analysis approaches are available and their relative strengths have not been directly compared using experimental data in the context of different gene expression scenarios and studies with different degrees of relationship. This study investigates the complementary advantages of meta-analysis approaches to integrate information across studies, and further mine the transcriptome for genes that are associated with complex processes such as behavioral maturation in honey bees. Behavioral maturation and division of labor in honey bees are related to changes in the expression of hundreds of genes in the brain. The information from various microarray studies comparing the expression of genes at different maturation stages in honey bee brains was integrated using complementary meta-analysis approaches.</p> <p>Results</p> <p>Comparison of lists of genes with significant differential expression across studies failed to identify genes with consistent patterns of expression that were below the selected significance threshold, or identified genes with significant yet inconsistent patterns. The meta-analytical framework supported the identification of genes with consistent overall expression patterns and eliminated genes that exhibited contradictory expression patterns across studies. Sample-level meta-analysis of normalized gene-expression can detect more differentially expressed genes than the study-level meta-analysis of estimates for genes that were well described by similar model parameter estimates across studies and had small variation across studies. Furthermore, study-level meta-analysis was well suited for genes that exhibit consistent patterns across studies, genes that had substantial variation across studies, and genes that did not conform to the assumptions of the sample-level meta-analysis. Meta-analyses confirmed previously reported genes and helped identify genes (e.g. <it>Tomosyn</it>, <it>Chitinase 5, Adar, Innexin 2, Transferrin 1</it>, <it>Sick</it>, <it>Oatp26F</it>) and Gene Ontology categories (e.g. purine nucleotide binding) not previously associated with maturation in honey bees.</p> <p>Conclusion</p> <p>This study demonstrated that a combination of meta-analytical approaches best addresses the highly dimensional nature of genome-wide microarray studies. As expected, the integration of gene expression information from microarray studies using meta-analysis enhanced the characterization of the transcriptome of complex biological processes.</p

Semiparametric approach to characterize unique gene expression trajectories across time

BACKGROUND: A semiparametric approach was used to identify groups of cDNAs and genes with distinct expression profiles across time and overcome the limitations of clustering to identify groups. The semiparametric approach allows the generalization of mixtures of distributions while making no specific parametric assumptions about the distribution of the hidden heterogeneity of the cDNAs. The semiparametric approach was applied to study gene expression in the brains of Apis mellifera ligustica honey bees raised in two colonies (A. m. mellifera and ligustica) with consistent patterns across five maturation ages. RESULTS: The semiparametric approach provided unambiguous criteria to detect groups of genes, trajectories and probability of gene membership to groups. The semiparametric results were cross-validated in both colony data sets. Gene Ontology analysis enhanced by genome annotation helped to confirm the semiparametric results and revealed that most genes with similar or related neurobiological function were assigned to the same group or groups with similar trajectories. Ten groups of genes were identified and nine groups had highly similar trajectories in both data sets. Differences in the trajectory of the reminder group were consistent with reports of accelerated maturation in ligustica colonies compared to mellifera colonies. CONCLUSION: The combination of microarray technology, genomic information and semiparametric analysis provided insights into the genomic plasticity and gene networks linked to behavioral maturation in the honey bee