Animals use a variety of sensory modalities-including visual, acoustic, and chemical-to sense their environment and interact with both conspecifics and other species. Such communication is especially critical in eusocial insects such as honey bees and ants, where cooperation is critical for survival and reproductive success. Various classes of chemoreceptors have been hypothesized to play essential roles in the origin and evolution of eusociality in ants, through their functional roles in pheromone detection that characterizes reproductive status and colony membership. To better understand the molecular mechanisms by which chemoreceptors regulate social behaviors, we investigated the roles of a critical class of chemoreceptors, the odorant receptors (ORs), from the ponerine ant Harpegnathos saltator in detecting cuticular hydrocarbon pheromones. In light of the massive OR expansion in ants (∼400 genes per species), a representative survey based on phylogenetic and transcriptomic criteria was carried out across discrete odorant receptor subfamilies. Responses to several classes of semiochemicals are described, including cuticular hydrocarbons and mandibular gland components that act as H. saltator pheromones, and a range of more traditional general odorants. When viewed through the prism of caste-specific OR enrichment and distinctive OR subfamily odorant response profiles, our findings suggest that whereas individual HsOrs appear to be narrowly tuned, there is no apparent segregation of tuning responses within any discrete HsOr subfamily. Instead, the HsOR gene family as a whole responds to a broad array of compounds, including both cuticular hydrocarbons and general odorants that are likely to mediate distinct behaviors.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph-based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly "Question-of-the-Month (QotM) Challenge" series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.