10. Microbiome analysis

10.5. Applications and limitations

In a honey bee colony, each individual plays a specific yet adaptive role, and changes in the microbiome composition (dysbiosis) of even just some individuals may influence the colony's success or failure. Thus, the microbiome should be evaluated more often in bee health studies, as it is known to be affected by pesticides (Kakumanu et al., 2016), pathogens (Paris et al., 2020), food restrictions (Castelli et al., 2020), and even change in environmental cues (Hammer et al., 2021). The majority of the studies have focused efforts on characterizing worker bee gut microbiota, but it is important to incorporate investigations of other castes, tissues, developmental stages, and colony environments.

The honey bee colony is ultimately a superorganism (Moritz & Southwick, 2012), and the microbes harbored in different parts of the colony can also play a direct role in colony fitness or may serve as microbe reservoirs. The microbial community does not exist in isolation, and we encourage readers to consider not only the simple characterization of the communities, but their interactions with social evolution (Liberti et al., 2022; Vernier et al., 2020), development (Hammer & Moran, 2019), diapause (Mushegian & Tougeron, 2019; Santos et al., 2019), and the brain-microbiome axis (Zhang et al., 2022; Zhang et al., 2022).

Amplicon sequencing approaches do, of course, have limitations, one of which is that analyzing hyper-variable regions is not equal sensitive for all bacteria (one hypervariable region is insufficient to differentiate all bacterial species). Most OTUs or ASVs will thus not have the sequence resolution to taxonomically identify the microbes to genus or species, much less strain (Callahan et al., 2017). Even so, this approach is still useful for microbial community characterization and for observing major changes in it.

Different strains of microbes may fill different functional niches, and investigating patterns of strain variation is a growing area of interest. Primers for non-marker genes have already been developed to detect diversity at the bacterial strain level, including the genes minD(Powell et al., 2016), guaA, and gluS (Bobay et al., 2020) for Snodgrassella alvi strain composition, and pflA and rimM for Gilliamella spp. strain composition. This method was termed metagenomic amplicon strain typing (MAST). Although an interesting approach focused on specific members of the microbiome, there are no similar published studies for fungi. Otherwise, to describe the strain-level diversity of the entire microbial community, a shot-gun metagenomics approach would be more appropriate (Ellegaard & Engel, 2019), but it is more expensive and time-consuming for analysis. However, those who utilize this method are rewarded with not only strain-level diversity, but also host genotypes and gene sequences of the microbes, potentially allowing for functional insights.

Conversely, using the amplicon sequencing approach, there is limited knowledge to gain regarding the roles of microbiome members, since there is no information regarding their gene repertoire and metabolic activity. Of course, functions can be predicted based on general knowledge of the described species or sequenced genome using software as PICRUSt (Langille et al., 2013), Tax4Fun2 (Wemheuer et al., 2020), and FUNGuild (Nguyen et al., 2016), but strains may differ with regards to their functional abilities; therefore, these predictions are tenuous at best. One way to alleviate this concern would be to combine culture-dependent approaches to conduct in vitro and in vivo experiments - confirming a microbes' role and interactions. This kind of functional validation of predictions remains one of the field’s biggest challenges.

The field of honey bee microbiome analyses is still underway. In particular, the non-bacterial communities within honey bee guts are not well characterized, with a lack of consensus regarding fungal communities (Hroncova et al., 2015; Khan et al., 2020) and ubiquitous viral communities (Bonilla-Rosso et al., 2020; Kadlečková et al., 2022). And, despite the characterization of the bacterial profiles of honey bee guts, much is still unknown regarding its impact in honey bee colony fitness, such as how microbial communities influence host physiology or how spatial specialization of microbes within colonies occurs (Copeland et al., 2022; Powell et al., 2021; Zheng et al., 2017).