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The evolution of mating signals is shaped by divergent roles and selective forces, which allow these signals to become multifunctional. Sexual dimorphism in mating signals can reflect this multifunctionality, where such dimorphism could contribute to both mate recognition and mate choice. Sexual dimorphism in mating signals is thought to arise due to divergent sex roles, driven by the interactions of several selective pressures. It has been suggested that, across taxa, both sexes can be choosy and result in sexual selection. However, whether sexual dimorphism in mating signals can predict its role in male courtship behaviour is still unclear. In this study, we used cuticular hydrocarbons (CHCs) in Drosophila species as a model to investigate the question, with CHCs serving as key chemical cues during courtship. This study investigates the relationship between CHC sexual dimorphism and its role in male courtship behaviour across 10 Drosophila species. Our results reveal variations in the degree of CHC sexual dimorphism across the test species. In addition, CHC detection was found to contribute to courtship initiation in most of the test species, but CHC sexual dimorphism did not predict male courtship behaviour. Notably, a longer courtship latency was observed following the loss of CHC detection, indicating that CHCs may convey information on mate quality. Our study suggests that sexual dimorphism in CHCs is not directly linked to its role in mating signal recognition and highlights the species-specific evolution of chemical signals in Drosophila courtship. 

To address the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have become a critical tool for integrating large volumes of heterogeneous data to enable efficient information retrieval and automated knowledge discovery. However, transforming unstructured scientific literature into KGs remains a significant challenge, with previous methods unable to achieve human-level accuracy. Here we used an information extraction pipeline that won first place in the LitCoin Natural Language Processing Challenge (2022) to construct a large-scale KG named iKraph using all PubMed abstracts. The extracted information matches human expert annotations and significantly exceeds the content of manually curated public databases. To enhance the KG’s comprehensiveness, we integrated relation data from 40 public databases and relation information inferred from high-throughput genomics data. This KG facilitates rigorous performance evaluation of automated knowledge discovery, which was infeasible in previous studies. We designed an interpretable, probabilistic-based inference method to identify indirect causal relations and applied it to real-time COVID-19 drug repurposing from March 2020 to May 2023. Our method identified around 1,200 candidate drugs in the first 4 months, with one-third of those discovered in the first 2 months later supported by clinical trials or PubMed publications. These outcomes are very challenging to attain through alternative approaches that lack a thorough understanding of the existing literature. A cloud-based platform (https://biokde.insilicom.com) was developed for academic users to access this rich structured data and associated tools. 

Desert adaptation in Drosophila is due to evolutionary changes in a single gene. 

Maintaining water balance is a universal challenge for organisms living in terrestrial environments, especially for insects, which have essential roles in our ecosystem. Although the high surface area to volume ratio in insects makes them vulnerable to water loss, insects have evolved different levels of desiccation resistance to adapt to diverse environments. To withstand desiccation, insects use a lipid layer called cuticular hydrocarbons (CHCs) to reduce water evaporation from the body surface. It has long been hypothesized that the water-proofing capability of this CHC layer, which can confer different levels of desiccation resistance, depends on its chemical composition. However, it is unknown which CHC components are important contributors to desiccation resistance and how these components can determine differences in desiccation resistance. In this study, we used machine-learning algorithms, correlation analyses, and synthetic CHCs to investigate how different CHC components affect desiccation resistance in 50 Drosophila and related species. We showed that desiccation resistance differences across these species can be largely explained by variation in CHC composition. In particular, length variation in a subset of CHCs, the methyl-branched CHCs (mbCHCs), is a key determinant of desiccation resistance. There is also a significant correlation between the evolution of longer mbCHCs and higher desiccation resistance in these species. Given that CHCs are almost ubiquitous in insects, we suggest that evolutionary changes in insect CHC components can be a general mechanism for the evolution of desiccation resistance and adaptation to diverse and changing environments.