Conservation in Mammals of Genes Associated with Aggression-Related Behavioral Phenotypes in Honey Bees

Machine Learning models identify gene predictors of waggle dance behaviour in honeybees

The molecular characterisation of complex behaviours is a challenging task as a range of different factors are often involved to produce the observed phenotype. An established approach is to look at the overall levels of expression of brain genes - or 'neurogenomics' - to select the best candidates that associate with patterns of interest. However, traditional neurogenomic analyses have some well-known limitations; above all, the usually limited number of biological replicates compared to the number of genes tested - known as "curse of dimensionality". In this study we implemented a Machine Learning (ML) approach that can be used as a complement to more established methods of transcriptomic analyses. We tested three supervised learning algorithms (Random Forests, Lasso and Elastic net Regularized Generalized Linear Model, and Support Vector Machine) for their performance in the characterization of transcriptomic patterns and identification of genes associated with honeybee waggle dance. We then intersected the results of these analyses with traditional outputs of differential gene expression analyses and identified two promising candidates for the neural regulation of the waggle dance: boss and hnRNP A1. Overall, our study demonstrates the application of Machine Learning to analyse transcriptomics data and identify candidate genes underlying social behaviour. This approach has great potential for application to a wide range of different scenarios in evolutionary ecology, when investigating the genomic basis for complex phenotypic traits and can present some clear advantages compared to the established tools of gene expression analysis, making it a valuable complement for future studies.

Defense in Social Insects: Diversity, Division of Labor, and Evolution

All social insects defend their colony from predators, parasites, and pathogens. In Oster and Wilson's classic work, they posed one of the key paradoxes about defense in social insects: Given the universal necessity of defense, why then is there so much diversity in mechanisms? Ecological factors undoubtedly are important: Predation and usurpation have imposed strong selection on eusocial insects, and active defense by colonies is a ubiquitous feature of all social insects. The description of diverse insect groups with castes of sterile workers whose main duty is defense has broadened the purview of social evolution in insects, in particular with respect to caste and behavior. Defense is one of the central axes along which we can begin to organize and understand sociality in insects. With the establishment of social insect models such as the honey bee, new discoveries are emerging regarding the endocrine, neural, and gene regulatory mechanisms underlying defense in social insects. The mechanisms underlying morphological and behavioral defense traits may be shared across diverse groups, providing opportunities for identifying both conserved and novel mechanisms at work. Emerging themes highlight the context dependency of and interaction between factors that regulate defense in social insects.

The fight to understand fighting: neurogenetic approaches to the study of aggression in insects

Aggression is an evolutionarily conserved behavior that evolved in the framework of defending or obtaining resources. When expressed out of context, unchecked aggression can have destructive consequences. Model systems that allow examination of distinct neuronal networks at the molecular, cellular, and circuit levels are adding immensely to our understanding of the biological basis of this behavior and should be relatable to other species up to and including man. Investigators have made particular use of insect models to both describe this quantifiable and stereotyped behavior and to manipulate genes and neuron function via numerous genetic and pharmacological tools. This review discusses recent advances in techniques that improve our ability to identify, manipulate, visualize, and compare the genes, neurons, and circuits that are required for the output of this complex and clinically relevant social behavior.

Biomarkers in aggression

Aggressive behavior exerts an enormous impact on society remaining among the main causes of worldwide premature death. Effective primary interventions, relying on predictive models of aggression that show adequate sensitivity and specificity are currently lacking. One strategy to increase the accuracy and precision of prediction would be to include biological data in the predictive models. Clearly, to be included in such models, biological markers should be reliably associated with the specific trait under study (i.e., diagnostic biomarkers). Aggression, however, is phenotypically highly heterogeneous, an element that has hindered the identification of reliable biomarkers. However, current research is trying to overcome these challenges by focusing on more homogenous aggression subtypes and/or by studying large sample size of aggressive individuals. Further advance is coming by bioinformatics approaches that are allowing the integration of inter-species biological data as well as the development of predictive algorithms able to discriminate subjects on the basis of the propensity toward aggressive behavior. In this review we first present a brief summary of the available evidence on neuroimaging of aggression. We will then treat extensively the data on genetic determinants, including those from hypothesis-free genome-wide association studies (GWAS) and candidate gene studies. Transcriptomic and neurochemical biomarkers will then be reviewed, and we will dedicate a section on the role of metabolomics in aggression. Finally, we will discuss how biomarkers can inform the development of new pharmacological tools as well as increase the efficacy of preventive strategies.

Evolutionary diversity as a catalyst for biological discovery

The tremendous diversity of animal behaviors has inspired generations of scientists from an array of biological disciplines. To complement investigations of ecological and evolutionary factors contributing to behavioral evolution, modern sequencing, gene editing, computational and neuroscience tools now provide a means to discover the proximate mechanisms upon which natural selection acts to generate behavioral diversity. Social behaviors are motivated behaviors that can differ tremendously between closely related species, suggesting phylogenetic plasticity in their underlying biological mechanisms. In addition, convergent evolution has repeatedly given rise to similar forms of social behavior and mating systems in distantly related species. Social behavioral divergence and convergence provides an entry point for understanding the neurogenetic mechanisms contributing to behavioral diversity. We argue that the greatest strides in discovering mechanisms contributing to social behavioral diversity will be achieved through integration of interdisciplinary comparative approaches with modern tools in diverse species systems. We review recent advances and future potential for discovering mechanisms underlying social behavioral variation; highlighting patterns of social behavioral evolution, oxytocin and vasopressin neuropeptide systems, genetic/transcriptional "toolkits," modern experimental tools, and alternative species systems, with particular emphasis on Microtine rodents and Lake Malawi cichlid fishes.

Deep evolutionary conservation of autism-related genes

E. O. Wilson proposed in Sociobiology that similarities between human and animal societies reflect common mechanistic and evolutionary roots. When introduced in 1975, this controversial hypothesis was beyond science's ability to test. We used genomic analyses to determine whether superficial behavioral similarities in humans and the highly social honey bee reflect common molecular mechanisms. Here, we report that gene expression signatures for individual bees unresponsive to various salient social stimuli are significantly enriched for autism spectrum disorder-related genes. These signatures occur in the mushroom bodies, a high-level integration center of the insect brain. Furthermore, our finding of enrichment was unique to autism spectrum disorders; brain gene expression signatures from other honey bee behaviors do not show this enrichment, nor do datasets from other human behavioral and health conditions. These results demonstrate deep conservation for genes associated with a human social pathology and individual differences in insect social behavior, thus providing an example of how comparative genomics can be used to test sociobiological theory.