Figures and data
A chemical informatics method to predict honey bee repellence
Overview of the steps for the Machine Learning-based cheminformatics pipeline used to model honey bee repellence from the 3D structure of a training set of known repellents, and subsequent in-silico screening for novel repellents from a large chemical space.
Predicted honey bee repellents
A. Testing Chamber containing a one-choice trap to determine, whether an odorant will repel fruit flies, and the mean percentage of fruit flies caught in a trap treated with predicted repellent odorants (10% v/v in Paraffin oil) and baited with 10% apple cider vinegar. N=5-8 trials (∼20 flies/trial). Error bars= s.e.m.
B. Photograph of the two-choice Petri dish arenas used to test the behavior of honey bees to repellent candidates individually, and
C. Table with preference indexes in the two-choice assay for the first choices of honey bee workers offered two containers of honey water (50 μl of 50%) placed on filter paper discs treated with a test chemical or treated with solvent alone. The index was calculated for each compound as (total number of repellent choices minus total number of solvent choices) divided by the sum of all tests. We used McNemar tests for pairwise comparisons.
Reiterative training of ML predictive models and testing honey bee repellents
A. Schematic pipeline for the overall computational-behavior hybrid pipeline showing the two rounds.
B. Mean Preference index of honey bees in making the first choice to move to the repellent treated side in a two-choice plate assay. 70 μl of pure honey was used in the two containers to attract the bees.
C. Average consumption of honey from containers placed on each side in the two-choice plate assays (N = 460 plates). The index is calculated for each compound as which honey pot has greater consumption (total number of repellent side with greater consumption minus total number of solvent side with greater consumption) divided by sum of all choices).
D. Mean percentage of fruit flies caught in a trap treated with predicted repellent odorants at application rates to test in field tests (0.1mg/cm2) and baited with 10% apple cider vinegar. N=5-8 trials (∼20 flies/trial). Error bars= s.e.m.
Field testing of top repellents in the lab using robbing assays
A. Representative photo of a robbing assay.
B. Time-course of the mean numbers of foraging honey bees visiting for each indicated compound tested (0.1 mg/cm2). DEET was used as a positive control and acetone as a negative control in each assay. N=6-7. Error bars=s.e.m.
C. Mean number of honey bees for each compound summed over the time points shown in B. * p<0.05, ** p<0.005, *** p<0.001. We used Kruskal Wallis tests for pairwise comparisons.
Mean number of honey bees for each compound tested at the regular dose (0.1mg/cm2) and half dose (0.05mg/cm2), summed over the time points over the duration of the experiment as above. N=6, Error bars=s.e.m.
(related to Figure 3). Average percentage of two-choice plate assays with honey bees making the first choice to move to the repellent treated side or solvent treated side across different honey bee colonies, N=3-6 colonies (∼6 plates/colony). Error bars =s.e.m., P*<0.05, **<0.001.
Optimized DRAGON physicochemical descriptor set from iterative training in Figure 3A used to predict bee repellent compounds.
Repellency testing in the 2-choice plate assay from initial round of predictions.
