Lactobacillus spp. attenuate antibiotic-induced immune and microbiota dysregulation in honey bees.

Widespread antibiotic usage in apiculture contributes substantially to the global dissemination of antimicrobial resistance and has the potential to negatively influence bacterial symbionts of honey bees (Apis mellifera). Here, we show that routine antibiotic administration with oxytetracycline selectively increased tetB (efflux pump resistance gene) abundance in the gut microbiota of adult workers while concurrently depleting several key symbionts known to regulate immune function and nutrient metabolism such as Frischella perrera and Lactobacillus Firm-5 strains. These microbial changes were functionally characterized by decreased capped brood counts (marker of hive nutritional status and productivity) and reduced antimicrobial capacity of adult hemolymph (indicator of immune competence). Importantly, combination therapy with three immunostimulatory Lactobacillus strains could mitigate antibiotic-associated microbiota dysbiosis and immune deficits in adult workers, as well as maximize the intended benefit of oxytetracycline by suppressing larval pathogen loads to near-undetectable levels. We conclude that microbial-based therapeutics may offer a simple but effective solution to reduce honey bee disease burden, environmental xenobiotic exposure, and spread of antimicrobial resistance.

Novel probiotic approach to counter Paenibacillus larvae infection in honey bees.

American foulbrood (AFB) is a highly virulent disease afflicting honey bees (Apis mellifera). The causative organism, Paenibacillus larvae, attacks honey bee brood and renders entire hives dysfunctional during active disease states, but more commonly resides in hives asymptomatically as inactive spores that elude even vigilant beekeepers. The mechanism of this pathogenic transition is not fully understood, and no cure exists for AFB. Here, we evaluated how hive supplementation with probiotic lactobacilli (delivered through a nutrient patty; BioPatty) affected colony resistance towards a naturally occurring AFB outbreak. Results demonstrated a significantly lower pathogen load and proteolytic activity of honey bee larvae from BioPatty-treated hives. Interestingly, a distinctive shift in the microbiota composition of adult nurse bees occurred irrespective of treatment group during the monitoring period, but only vehicle-supplemented nurse bees exhibited higher P. larvae loads. In vitro experiments utilizing laboratory-reared honey bee larvae showed Lactobacillus plantarum Lp39, Lactobacillus rhamnosus GR-1, and Lactobacillus kunkeei BR-1 (contained in the BioPatty) could reduce pathogen load, upregulate expression of key immune genes, and improve survival during P. larvae infection. These findings suggest the usage of a lactobacilli-containing hive supplement, which is practical and affordable for beekeepers, may be effective for reducing enzootic pathogen-related hive losses.

From gene list to gene network: Recognizing functional connections that regulate behavioral traits.

The study of social breeding systems is often gene focused, and the field of insect sociobiology has been successful at assimilating tools and techniques from molecular biology. One common output from sociogenomic studies is a gene list. Gene lists are readily generated from microarray, RNA sequencing, or other molecular screens that typically aim to prioritize genes based on the differences in their expression. Gene lists, however, are often unsatisfying because the information they provide is simply tabular and does not explain how genes interact with each other, or how genetic interactions change in real time under social or environmental circumstances. Here, we promote a view that is relatively common to molecular systems biology, where gene lists are converted into gene networks that better describe the functional connections that regulate behavioral traits. We present a narrative related to honeybee worker sterility to show how network analysis can be used to reprioritize candidate genes based on connectivity rather than their freestanding expression values. Networks can also reveal multigene modules, motifs, clusters or other system-wide properties that might not be apparent from an ab initio list. We argue that because network analyses are not restricted to "genes" as nodes, their implementation can potentially connect multiple levels of biological organization into a single, progressively complex study system.