Sandra Simões

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.

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.

Honey bees play an important role in agricultural landscapes by providing pollination services. Throughout the season, colonies increase their population and collect resources from the available flowering plants. Besides internal mechanisms, such as the amount of brood or the availability of bees to perform foraging flights, colonies are also influenced by the climate and the surrounding landscape. Therefore, exposure to different environmental contexts leads to distinct development rates. In this study, we show how colonies develop under three different landscape contexts and explore which external variables (mostly climate and resources availability) influence the colonies’ development. We installed three apiaries in three different landscapes in the Iberian Peninsula, with temporal and spatial variation in climatic conditions and resource availability. The availability of resources and their use, as well as the development of colonies throughout the season, were thoroughly investigated. These data were used to take the first step into creating an ecologically relevant landscape by calculating the number of available resources in the landscape at different points in time, based on plants’ beekeeping interest as well as nectar and production. Furthermore, climatic variables were transformed into the amount of available foraging minutes that bees had to collect resources, and a theoretical threshold of optimal vs. sub-optimal conditions was also explored. Interestingly, the main drivers of colony development (measured by daily weight increase) were not the same in the tested apiaries, evidencing how colonies are indeed intrinsically connected with the surrounding environmental scenario. Therefore, results from field testing are extremely context-dependent and should be interpreted with caution when being extrapolated to other environmental scenarios.

A palette of copy number changes in long-term epilepsy-associated tumors (LEATs) have been reported, but the data are heterogeneous. To better understand the molecular basis underlying the development of LEATs, we performed array-comparative genomic hybridization analysis to investigate chromosomal imbalances across the entire genome in 8 cases of LEATs. A high number of aberrations were found in 4 patients, among which deletions predominated. Both whole-chromosome and regional abnormalities were observed, including monosomy 19, deletion of 1p, deletions of 4p, 12p, and 22q, and gain of 20p. The common altered regions are located mainly on chromosomes 19 and 4p, identifying genes potentially involved in biological processes and cellular mechanisms related to tumorigenesis. Our study highlights new genomic alterations and reinforces others previously reported, offering new molecular insights that may help in diagnosis and therapeutic decision-making.