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1. Evolutionary trends in Bombella apis CRISPR-Cas systems

   https://doi.org/10.1128/msystems.00166-25  

   Ganote, Carrie L; Caesar, Lílian; Rice, Danny W; Whitaker, Rachel J; Newton, Irene_L G (July 2025, mSystems)

   Gilbert, Jack A (Ed.)

   ABSTRACT Bacteria and archaea employ a rudimentary immune system, CRISPR-Cas, to protect against foreign genetic elements such as bacteriophage. CRISPR-Cas systems are found inBombella apis.B. apisis an important honey bee symbiont, found primarily in larvae, queens, and hive compartments.B. apisis found in the worker bee gut but is not considered a core member of the bee microbiome and has therefore been understudied with regard to its importance in the honey bee colony. However,B. apisappears to play beneficial roles in the colony, by protecting developing brood from fungal pathogens and by bolstering their development under nutritional stress. Previously, we identified CRISPR-Cas systems as being acquired byB. apisin its transition to bee association, as they are absent in a sister clade. Here, we assess the variation and distribution of CRISPR-Cas types acrossB. apisstrains. We found multiple CRISPR-Cas types, some of which have multiple arrays, within the sameB. apisgenomes and also in the honey bee queen gut metagenomes. We analyzed the spacers between strains to identify the history of mobile element interaction for eachB. apisstrain. Finally, we predict interactions between viral sequences and CRISPR systems from different honey bee microbiome members. Our analyses show that theB. apisCRISPR-Cas systems are dynamic; that microbes in the same niche have unique spacers, which supports the functionality of these CRISPR-Cas systems; and that acquisition of new spacers may be occurring in multiple locations in the genome, allowing for a flexible antiviral arsenal for the microbe. IMPORTANCEHoney bee worker gut microbes have been implicated in everything from protection from pathogens to breakdown of complex polysaccharides in the diet. However, there are multiple niches within a honey bee colony that host different groups of microbes, including the acetic acid bacteriumBombella apis.B. apisis found in the colony food stores, in association with brood, in worker hypopharyngeal glands, and in the queen’s digestive tract. The roles thatB. apismay serve in these environments are just beginning to be discovered and include the production of a potent antifungal that protects developing bees and supplementation of dietary lysine to young larvae, bolstering their nutrition. Niche specificity inB. apismay be affected by the pressures of bacteriophage and other mobile elements, which may target different strains in each specific bee environment. Studying the interplay betweenB. apisand its mobile genetic elements (MGEs) may help us better understand microbial community dynamics within the colony and the potential ramifications for the honey bee host. 

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2. Diet quantity influences caste determination in honeybees ( Apis mellifera )

   https://doi.org/10.1098/rspb.2020.0614  

   Slater, Garett P.; Yocum, George D.; Bowsher, Julia H. (May 2020, Proceedings of the Royal Society B: Biological Sciences)
3. Metabolomic analysis of honey bee ( Apis mellifera L.) response to glyphosate exposure

   https://doi.org/10.1039/D2MO00046F  

   Wang, Bo; Habermehl, Calypso; Jiang, Lin (May 2022, Molecular Omics)

   Glyphosate is among the world's most commonly used herbicides in agriculture and weed control. The use of this agrochemical has unintended consequences on non-target organisms, such as honey bees ( Apis mellifera L. ), the Earth's most prominent insect pollinator. However, detailed understanding of the biological effects in bees in response to sub-lethal glyphosate exposure is still limited. In this study, 1 H NMR-based metabolomics was performed to investigate whether oral exposure to an environmentally realistic concentration (7.12 mg L −1 ) of glyphosate affects the regulation of honey bee metabolites in 2, 5, and 10 days. On Day 2 of glyphosate exposure, the honey bees showed significant downregulation of several essential amino acids, including leucine, lysine, valine, and isoleucine. This phenomenon indicates that glyphosate causes an obvious metabolic perturbation when the honey bees are subjected to the initial caging process. The mid-term (Day 5) results showed negligible metabolite-level perturbation, which indicated the low glyphosate impact on active honeybees. However, the long-term (Day 10) data showed evident separation between the control and experimental groups in the principal component analysis (PCA). This separation is the result of the combinatorial changes of essential amino acids such as threonine, histidine, and methionine, while the non-essential amino acids glutamine and proline as well as the carbohydrate sucrose were all downregulated. In summary, our study demonstrates that although no significant behavioral differences were observed in honey bees under sub-lethal doses of glyphosate, metabolomic level perturbation can be observed under short-term exposure when met with other environmental stressors or long-term exposure. 

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4. Tissue‐specific transcriptional patterns underlie seasonal phenotypes in honey bees ( Apis mellifera )

   https://doi.org/10.1111/mec.16220  

   Bresnahan, Sean T.; Döke, Mehmet A.; Giray, Tugrul; Grozinger, Christina M. (January 2022, Molecular Ecology)
5. Can I Solve It? Identifying APIs Required to Complete OSS Tasks

   https://doi.org/10.1109/MSR52588.2021.00047  

   Santos, Fabio; Wiese, Igor; Trinkenreich, Bianca; Steinmacher, Igor; Sarma, Anita; Gerosa, Marco A. (May 2021, Mining Software Repositories Conference (MSR 2021))

   null (Ed.)

   Open Source Software projects add labels to open issues to help contributors choose tasks. However, manually labeling issues is time-consuming and error-prone. Current automatic approaches for creating labels are mostly limited to classifying issues as a bug/non-bug. In this paper, we investigate the feasibility and relevance of labeling issues with the domain of the APIs required to complete the tasks. We leverage the issues' description and the project history to build prediction models, which resulted in precision up to 82% and recall up to 97.8%. We also ran a user study (n=74) to assess these labels' relevancy to potential contributors. The results show that the labels were useful to participants in choosing tasks, and the API-domain labels were selected more often than the existing architecture-based labels. Our results can inspire the creation of tools to automatically label issues, helping developers to find tasks that better match their skills. 

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