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  Neonicotinoid-induced signature dysbiosis identified via metagenomic sequencing of the honey bee gut microbiome

  (Springer Nature, 2026) Tran, Lan; Deckers, Thomas B.; Ho, Jonathan; Lansing, Lance; Cunningham, Morgan; Morfin, Nuria; Pepinelli, Mateus; De la Mora, Alvaro; Conflitti, Ida M.; Gregoris, Amanda; Wu, Linzhi; Trepanier-Leroux, Daphne; Muntz, Laura; Newman, Tara; Vishwakarma, Shefali; Bixby, Miriam; Jabbari, Hosna; Guzman-Novoa, Ernesto; Hoover, Shelley E.; Currie, Robert W.; Pernal, Stephen F.; Giovenazzo, Pierre; Foster, Leonard J.; Zayed, Amro; Ortega Polo, Rodrigo; Guarna, M. Marta

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  The Western honey bee (Apis mellifera) plays an essential role in agriculture around the world. In Canada, honey bees contribute up to $7 billion in economic value annually by pollinating crops and producing honey. However, since 2006–2007 North American beekeepers have lost more than a quarter of their colonies each winter. In recent years, the losses have been up to 50% in some regions. The causes of losses are complex, including the interacting effects of nutrition, pathogens, and pesticides. Although the bee gut microbiome plays a crucial role in colony health and disease, studies on the effects of agricultural pesticides on the bee microbial community are sparse. We report the use of shotgun metagenomic sequencing to investigate bee gut microbiota changes, or dysbiosis, in response to two neonicotinoid insecticides, clothianidin and thiamethoxam. Common dysbiosis signatures included an increase in Bifidobacterium spp. after chronic sublethal exposure and an increase in Apibacter adventoris after short-term acute exposure. Other dysbiosis signatures were unique to each compound, such as an increase in Snodgrassella alvi for clothianidin and a decrease in Lactobacillus spp. for thiamethoxam. These findings enhance our understanding of how the honey bee gut microbiome responds to stressors and highlight identifiable microbial profile signatures which underscores the potential utility of gut microbiome profiling as a bee health diagnostic tool. Access to timely and accurate bee health diagnosis will inform regulatory actions to decrease and mitigate exposure to stressors and and will facilitate managing and improving bee health.

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  Omics insights Into the effects of highbush blueberry and cranberry crop agroecosystems on honey bee health and physiology

  (Wiley, 2025) Zhong, Huan; Shi, Yuming; Kozlova, Aleksandra; Moravcova, Renata; Rogalski, Jason C.; Jamieson, Aidan; Lansing, Lance; Moon, Kyung-Mee; Yuan, Xiaojing; Gregoris, Amanda S.; Higo, Heather; Common, Julia; Conflitti, Ida M.; Pepinelli, Mateus; Tran, Lan; Cunningham, Morgan; Jabbari, Hosna; Bukhari, Syed Abbas; French, Sarah K.; Ortega Polo, Rodrigo; Hoover, Shelley E.; Pernal, Stephen F.; Giovenazzo, Pierre; Guarna, M. Marta; Zayed, Amro; Foster, Leonard J.

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  Honey bees (Apis mellifera) are vital pollinators in fruit-producing agroecosystems like highbush blueberry (HBB) and cranberry (CRA). However, their health is threatened by multiple interacting stressors, including pesticides, pathogens, and nutritional changes. We tested the hypothesis that distinct agricultural ecosystems—with different combinations of agrochemical exposure, pathogen loads, and floral resources—elicit ecosystem-specific, tissue-level molecular responses in honey bees. We conducted an integrated multi-omics analysis using RNA-sequencing (RNA-seq), proteomics, and gut microbiome profiling across three key tissue types (head, abdomen, and gut) of honey bees collected from two agroecosystems over two field seasons. Quantification was performed for pesticide residues, pathogen loads (Nosema spp., Varroa destructor, and multiple viruses), and gut microbiota. Weighted gene co-expression network analysis (WGCNA) revealed tissue-specific protein modules with ecosystem-associated patterns, which differed from RNA co-expression networks. Microbiome composition also varied, with key genera like Gilliamella, Snodgrassella, and Bartonella correlating with metabolic modules. These findings underscore the complex, environment-dependent impacts of agroecosystem conditions on bee health. Our study provides a system-level understanding of how combined pesticide, pathogen, and parasitic stressors, mediated by diet and microbiome, shape molecular phenotypes in honey bees—informing strategies for pollinator protection in managed landscapes.

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  Investigating the pre-mortem diagnostic potential of neurofilament light chain in bovine spongiform encephalopathy

  (Lethbridge, Alta. : University of Lethbridge, Dept. of Biological Sciences, 2026) Dabbas, Lena; University of Lethbridge. Faculty of Arts and Science; Kovalchuk, Igor; Tahir, Waqas

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  Transmissible spongiform encephalopathy is a group of neurodegenerative diseases that affect the central nervous system. They result from the misfolding of a normal cellular prion protein (PrPC) into its abnormal isoform called scrapie prion protein (PrPSc). These groups of diseases affect different species, including chronic wasting disease (CWD) in cervids, scrapie in sheep and goats, Creutzfeldt-Jakob disease (CJD) in humans, and bovine spongiform encephalopathy (BSE) in cattle. BSE is particularly concerning because it is zoonotic and may cause variant CJD in humans when BSE-contaminated meat is consumed. Diagnosing BSE involves post-mortem testing that requires euthanasia and brain extraction, which is invasive, time-consuming, and expensive. Given these limitations, it is paramount to explore the potential for BSE detection in live cattle, with surrogate biomarkers being the most promising approach. A candidate biomarker that should be easily detectable in live subjects using a less invasive method, such as blood sampling. In this context, neurofilament light (NfL), a protein biomarker, is well documented in the literature and has been successfully identified in both the cerebrospinal fluid (CSF) and blood in cases of scrapie and CJD. Since neuronal loss is a key hallmark of prion diseases, and NfL is a marker of neuronal damage, it can be a favourable candidate biomarker. This study examined the potential of NfL as a premortem diagnostic tool for detecting BSE. This was accomplished by assessing NfL regulation in relation to PrPSc deposition in the brains, followed by analysis of NfL levels in the CSF and blood of both BSE-positive and BSE-negative cattle. Overall, this study is the first to provide evidence of NfL's potential to differentiate between BSE-positive and healthy cattle premortem.

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  Elevated virus infection of honey bee queens reduces methyl oleate production and destabilizes colony-level social structure

  (Oxford University Press, 2025) McAfee, Alison; Chapman, Abigail; Magana, Armando Alcazar; Marshall, Katie E.; Hoover, Shelley E.; Tarpy, David R.; Foster, Leonard J.

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  Pathogenic threats to reproductive individuals pose a profound challenge to the stability of insect societies. In honey bees (Apis mellifera L.), severe virus infections in queens can trigger worker-initiated supersedure, a socially coordinated replacement of the queen that, while risky, is essential when her reproductive competence is compromised. How viruses impact the physiology of queen hosts, who bear unique reproductive burdens within their colonies, and how this perturbs colony social order remains poorly understood. We hypothesized that the supersedure response is mediated by pathogen-induced, intensity-dependent changes in queen pheromonal signaling. Laboratory infection experiments revealed that queens challenged with deformed wing virus B and black queen cell virus infections demonstrated a reduction in methyl oleate, a key component of the queen retinue pheromone, and field data corroborated this association. Lipidomics analysis demonstrated that infection coincides with a systemic lipid deficiency, especially in triacylglycerides (major energy reserves), providing a physiological link among viral stress, ovarian atrophy, and altered pheromone output. Notably, artificial suppression of ovary investment via restricted laying also caused methyl oleate production to decline; therefore, high virus infection likely indirectly suppresses methyl oleate production by reducing ovary mass. In field trials, we further show that synthetic pheromone blends containing methyl oleate significantly suppressed queen cell rearing compared to no-pheromone controls, whereas blends lacking this compound yielded an intermediate effect. These results demonstrate that virus-induced reproductive decline disrupts pheromone signaling, revealing a plausible mechanistic pathway by which pathogens can erode social cohesion.

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  Detecting 2D and 3D clusters through feature extractions in deep convolutional neural networks

  (Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, 2026) Schafthuizen, Devin C.; University of Lethbridge. Faculty of Arts and Science; Zhang, John Z.

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  Clustering data is a complex and computationally expensive task, and in some approaches, it requires multiple passes over the same data to ensure globally optimal results. Clustering is an unsupervised learning method, meaning the distinction between points is made solely from the dataset being explored, with no prior knowledge or examples from previously clustered data. Using previously analyzed work to guide future analysis is supervised learning, which is typically applied to other problem categories, such as classification. Our research investigates the application of supervised learning techniques, specifically Convolutional Neural Networks (CNN), to perform clustering on projections of datasets in 2D and 3D visual representations using graphs and voxelization representations of data. These CNN models are designed for categorical output and can be used to guide the training process and leverage previously clustered data to learn representations in new, previously unseen datasets. However, that categorical output can only represent the number of clusters present. To extend this approach further, we explore extracting information from the CNN's processing layers to analyze the activation maps between the convolutional layers using our proposed SilhouetteGen algorithm to delineate cluster shapes and locations within the original input space. In later models, our algorithm also replaces the CNN's categorical output after training is complete to remove any restrictions on the prediction range. Various benchmark datasets and cluster quality metrics are used to assess the feasibility of this approach relative to a widely used and well-researched clustering method. The primary goal of this analysis is to demonstrate the feasibility of deep CNN feature extraction for detecting cluster information in distance and density-based clustering problems without requiring individual point-wise distance calculations.

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