Biology subjects

Borges, D.

Publications and source records attributed to Borges, D..

5 recordsLinked to original sources

bioRxiv2025

Innovative airborne DNA approach for monitoring honey bee foraging and health

Environmental DNA refers to genetic material collected from the environment and not directly from an organism of interest. It is best known as a tool in aquatic ecology but eDNA has been found associated with almost every substrate examined including soils, surfaces, and riding around on other animals. The collection of airborne eDNA is one of the most recent advances used to monitor a variety of organisms, including plants, animals, and microorganisms. Evidence suggests a high turnover rate providing a recent signal for the presence of DNA associated with an organism. Here, we test whether biological material carried in air in honey bee colonies can be used to evaluate recent foraging and colony health. We sampled air using purpose built "bee safe" filters operating for 5-6 hours at each colony and successfully recovered plant, fungal and microbial DNA from the air within honey bee colonies over a 3-week pilot period. From these data we identified the core honey bee microbiome and plant interaction data representing foraging behaviour. We calculated beta diversity to estimate the effects of apiary sites and sampling date on data recovery. We observed that variance in ITS data was more influenced by sampling date. Given that honey bees are generalist pollinators our ability to detect temporal signals in associated plant sequence data suggest this method opens new avenues into the ecological analysis of short term foraging behavior at the colony level. In comparison variance in microbial 16S sequencing data was more influenced by sampling location. As the assessment of colony health needs to be localized, spatial variance in these data indicate this may be an important tool in detecting infection. This pilot study demonstrates that colony air filtration has strong potential for the rapid screening of honey bee health and for the study of bee behaviour

ecology↗

bioRxiv2024

Pollen foraging mediates exposure to dichotomous stressor syndromes in honey bees

Recent declines in the health of honey bee colonies used for crop pollination pose a considerable threat to global food security. Foraging by honey bee workers represents the primary route of exposure to a plethora of toxins and pathogens known to affect bee health, but it remains unclear how foraging preferences impact colony-level patterns of stressor exposure. Resolving this knowledge gap is crucial for enhancing the health of honey bees and the agricultural systems that rely on them for pollination. To address this, we carried out a national-scale experiment encompassing 456 Canadian honey bee colonies to first characterize pollen foraging preferences in relation to major crops, then explore how foraging behaviour influences patterns of stressor exposure. We used a metagenetic approach to quantify honey bee dietary breadth and found that bees display distinct foraging preferences that vary substantially relative to crop type and proximity, and the breadth of foraging interactions can be used to predict the abundance and diversity of stressors a colony is exposed to. Foraging on diverse plant communities was associated with increased exposure to pathogens, while the opposite was associated with increased exposure to xenobiotics. Our work provides the first large-scale empirical evidence that pollen foraging behaviour plays an influential role in determining exposure to dichotomous stressor syndromes in honey bees. Significance StatementInsect-mediated pollination is an important ecological process that is crucial for food production. Managed honey bee colonies are one of the most important insect pollinators, but their health has been under threat from a variety of stressors. Bee workers are primarily exposed to stressors while foraging and understanding how bee foraging preferences are related to exposure risk could provide pivotal information to improve management efforts. Here, we studied honey bee foraging preferences in relation to prominent Canadian crops and across a gradient of modified environments. We found that honey bees show distinct, measurable foraging preferences and that dietary diversity is a strong predictor of the stressors that colonies are exposed to.

ecology↗

bioRxiv2024

Climatic predictors of prominent honey bee (Apis mellifera) disease agents: Varroa destructor, Melissococcus plutonius, and Vairimorpha spp.

Improving our understanding of how climate influences honey bee parasites and pathogens is critical as weather patterns continue to shift under climate change. While the prevalence of diseases vary according to regional and seasonal patterns, the influence of specific climatic predictors has rarely been formally assessed. To address this gap, we analyzed how occurrence and intensity of three prominent honey bee disease agents (Varroa destructor [-] hereon Varroa [-] Melissococcus plutonius, and Vairimorpha spp.) varied according to regional, temporal, and climatic factors in honey bee colonies across five Canadian provinces. We found strong regional effects for all disease agents, with consistently high Varroa intensity and infestation probabilities and high M. plutonius infection probabilities in British Columbia, and year-dependent regional patterns of Vairimorpha spp. spore counts. Increasing wind speed and precipitation were linked to lower Varroa infestation probabilities, whereas warmer temperatures were linked to higher infestation probabilities. Analysis of an independent dataset shows that these trends for Varroa are consistent within a similar date range, but temperature is the strongest climatic predictor of season-long patterns. Vairimorpha spp. intensity decreased over the course of the summer, with the lowest spore counts found at later dates when temperatures were warm. Vairimorpha spp. intensity increased with wind speed and precipitation, consistent with inclement weather limiting defecation flights. Probability of M. plutonius infection generally increased across the spring and summer, and was also positively associated with inclement weather. These data contribute to building a larger dataset of honey bee disease agent occurrence that is needed in order to predict how epidemiology may change in our future climate.

molecular biology↗

bioRxiv2023

Hierarchical Bayesian modeling identifies key considerations in the development of quantitative loop-mediated isothermal amplification assays

MotivationLoop-mediated isothermal amplification (LAMP) is a rapidly growing, fast, and cost-effective technique for detection of DNA/RNA in point-of-care biomedical applications. However, it remains unclear what factors affect LAMPs quantitative resolution, and experimental optimization of primers presents a major bottleneck in assay design. A lack of model-based frameworks to characterize LAMP data and address these questions presents an unmet need for LAMP assay development. ResultsWe present hierarchical Bayesian models of LAMP amplification based on Gompertz functions, and use these models to infer the effect of RNA variation and other factors on LAMP amplification curves derived from 80 blood samples of patients with suspected acute infection. Our analysis uncovers associations between LAMP assay resolution and characteristics such as primer sequence composition and thermodynamic properties. In addition to correlations between RNA input abundance and time shift of the the LAMP amplification curve, we also detect RNA-dependent assocations with amplification rate. We further investigate associations between primer/target properties and quantitative performance of the assay by generating a set of synthetic RNA samples with systematically varied primer sequences and applying our framework. We find evidence that the associations observed are driven by across-target rather than within-target variation, an important observation for study design. Our findings represent important first steps towards guided development of quantitative LAMP assays. Availability and ImplementationAnalysis and modeling code is available upon reasonable request.

bioinformatics↗

bioRxiv2017

Euplotid: A Linux-based platform to physically edit the genome

1Life continues to shock and amaze us, reminding us that truth is far stranger than fiction. http://Euplotid.io is a quantized geometric model of the eukaryotic cell, an attempt at quantifying the incredible complexity that gives rise to a living cell by beginning from the smallest unit, a quanta. Starting from the very bottom we are able to build the pieces which when hierarchically and combinatorially combined produce the emergent complex behavior that even a single celled organism can show. Euplotid is composed of a set of quantized geometric 3D building blocks and constantly evolving dockerized bioinformatic pipelines enabling a user to build and interact with the local regulatory architecture of every gene starting from DNA-interactions, chromatin accessibility, and RNA-sequencing. Reads are quantified using the latest computational tools and the results are normalized, quality-checked, and stored. The local regulatory architecture of each gene is built using a Louvain based graph partitioning algorithm parameterized by the chromatin extrusion model and CTCF-CTCF interactions. Cis-Regulatory Elements are defined using chromatin accessibility peaks which are mapped to Transcriptional Start Sites based on inclusion within the same neighborhood. Deep Neural Networks are trained in order to provide a statistical model mimicking transcription factor binding, giving the ability to identify all Transcription Factors within a given chromatin accessibility peak. By in-silico mutating and re-applying the neural network we are able to gauge the impact of a transition mutation on the binding of any transcription factor. The annotated output can be visualized in a variety of 1D, 2D, 3D and 4D ways overlaid with existing bodies of knowledge such as GWAS results or PDB structures. Once a particular CRE of interest has been identified a Base Editor mediated transition mutation can then be performed in a relevant model for further study. O_FIG O_LINKSMALLFIG WIDTH=182 HEIGHT=200 SRC="FIGDIR/small/170159v15_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1a55a1borg.highwire.dtl.DTLVardef@bebb0dorg.highwire.dtl.DTLVardef@1ea5b11org.highwire.dtl.DTLVardef@100e735_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 0.1:C_FLOATNO Graphical Abstract C_FIG

biophysics↗