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Brood is critically important in social insect colonies. It carries the colony’s fitness through delivering future reproductive adults as well as workers that will increase the colony’s workforce. Adoption of non-nestmate brood can increase the colony workforce but entails the risk of rearing unrelated sexuals or social parasites. Thus, theory would predict that ant workers will evolve the ability to discriminate between nestmate and alien brood using the chemical cues displayed at the brood’s surface. This appears especially true for eggs and first instar (L1) larvae, which require more resources before becoming adult workers compared to older brood. However, the chemical signature of ant early brood stages and its recognition by workers remains understudied. To fill this gap, we investigated the chemical basis of early brood nestmate and cross-species recognition in six ant species. We also tested the discrimination behaviour of workers in brood retrieval trials. We observed species-level cues and discrimination against hetero-specific brood. We also found that eggs and most L1 larvae displayed a colony signature. However, only some species discriminated against non-nestmate early brood. Interestingly, these species belong to genera subject to brood parasitism. We hypothesize that non-nestmate brood discrimination could arise from species adaptations against brood parasitism.

Cordyceps sinensis is a widely used and highly valuable traditional Chinese medicine. Several dipeptides have been detected in C. sinensis, but current scientific knowledge of its chemical makeup remains limited. In this study, an improved approach that integrates offline two-dimensional liquid chromatography (2D LC) separation, precursor ion list, library screening, and diagnostic ion filtering was established to systematically screen and characterize dipeptides in C. sinensis. Offline 2D LC integrating hydrophilic interaction liquid chromatography and reverse phase separations were established to eliminate interference and identify the target dipeptides. A library containing the potential 400 dipeptides was created, and a precursor ion list with all theoretical precursor ions was adopted to trigger the MS/MS scan with high sensitivity. To identify dipeptides, the type and connection sequence of amino acids were determined according to the product ions. Ile and Leu residues were differentiated for the first time according to the characteristic ion at m/z 69.07. Ultimately, 170 dipeptides were identified or tentatively characterized from C. sinensis, and most are reported for the first time in this species herein. In addition, the identified dipeptides were also applied for discrimination among the three Cordyceps species, and 11 markers were identified. The obtained results provide a deeper understanding of the chemical basis of C. sinensis.

Nowadays, computational approaches have drawn more and more attention when exploring the relationship between sweetness and chemical structure instead of traditional experimental tests. In this work, we proposed a novel multi-layer sweetness evaluation system based on machine learning methods. It can be used to evaluate sweet properties of compounds with different chemical spaces and categories, including natural, artificial, carbohydrate, non-carbohydrate, nutritive and non-nutritive ones, suitable for different application scenarios. Furthermore, it provided quantitative predictions of sweetness. In addition, sweetness-related chemical basis and structure transforming rules were obtained by using molecular cloud and matched molecular pair analysis (MMPA) methods. This work systematically improved the data quality, explored the best machine learning algorithm and molecular characterizing strategy, and finally obtained robust models to establish a multi-layer prediction system (available at: https://github.com/ifyoungnet/ChemSweet ). We hope that this study could facilitate food scientists with efficient screening and precise development of high-quality sweeteners.