Allele Frequencies of Genetic Variants Associated with Varroa Drone Brood Resistance (DBR) in Apis mellifera Subspecies across the European Continent.

Implementation of marker-assisted selection (MAS) in modern beekeeping would improve sustainability, especially in breeding programs aiming for resilience against the parasitic mite Varroa destructor. Selecting honey bee colonies for natural resistance traits, such as brood-intrinsic suppression of varroa mite reproduction, reduces the use of chemical acaricides while respecting local adaptation. In 2019, eight genomic variants associated with varroa non-reproduction in drone brood were discovered in a single colony from the Amsterdam Water Dune population in the Netherlands. Recently, a new study tested the applicability of these eight genetic variants for the same phenotype on a population-wide scale in Flanders, Belgium. As the properties of some variants varied between the two studies, one hypothesized that the difference in genetic ancestry of the sampled colonies may underly these contribution shifts. In order to frame this, we determined the allele frequencies of the eight genetic variants in more than 360 Apis mellifera colonies across the European continent and found that variant type allele frequencies of these variants are primarily related to the A. mellifera subspecies or phylogenetic honey bee lineage. Our results confirm that population-specific genetic markers should always be evaluated in a new population prior to using them in MAS programs.

Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.

Genetic and phenotypic analysis of the pathogenic potential of two novel Chlamydia gallinacea strains compared to Chlamydia psittaci.

Chlamydia gallinacea is an obligate intracellular bacterium that has recently been added to the family of Chlamydiaceae. C. gallinacea is genetically diverse, widespread in poultry and a suspected cause of pneumonia in slaughterhouse workers. In poultry, C. gallinacea infections appear asymptomatic, but studies about the pathogenic potential are limited. In this study two novel sequence types of C. gallinacea were isolated from apparently healthy chickens. Both isolates (NL_G47 and NL_F725) were closely related to each other and have at least 99.5% DNA sequence identity to C. gallinacea Type strain 08-1274/3. To gain further insight into the pathogenic potential, infection experiments in embryonated chicken eggs and comparative genomics with Chlamydia psittaci were performed. C. psittaci is a ubiquitous zoonotic pathogen of birds and mammals, and infection in poultry can result in severe systemic illness. In experiments with embryonated chicken eggs, C. gallinacea induced mortality was observed, potentially strain dependent, but lower compared to C. psittaci induced mortality. Comparative analyses confirmed all currently available C. gallinacea genomes possess the hallmark genes coding for known and potential virulence factors as found in C. psittaci albeit to a reduced number of orthologues or paralogs. The presence of potential virulence factors and the observed mortality in embryonated eggs indicates C. gallinacea should rather be considered as an opportunistic pathogen than an innocuous commensal.