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ARTICLE OPEN
Delivery mechanism can enhance probiotic activity against
honey bee pathogens
Brendan A. Daisley
1,2
, Andrew P. Pitek
3
, Christina Torres
4
, Robin Lowery
4
, Bethany A. Adair
3
, Kait F. Al
2
, Bernardo Niño
4,5
,
Jeremy P. Burton
2
, Emma Allen-Vercoe
1
, Graham J. Thompson
3
, Gregor Reid
2,7
and Elina Niño
4,6,7
✉
© The Author(s) 2023
Managed honey bee (Apis mellifera) populations play a crucial role in supporting pollination of food crops but are facing
unsustainable colony losses, largely due to rampant disease spread within agricultural environments. While mounting evidence
suggests that select lactobacilli strains (some being natural symbionts of honey bees) can protect against multiple infections, there
has been limited validation at the field-level and few methods exist for applying viable microorganisms to the hive. Here, we
compare how two different delivery systems—standard pollen patty infusion and a novel spray-based formulation—affect
supplementation of a three-strain lactobacilli consortium (LX3). Hives in a pathogen-dense region of California are supplemented
for 4 weeks and then monitored over a 20-week period for health outcomes. Results show both delivery methods facilitate viable
uptake of LX3 in adult bees, although the strains do not colonize long-term. Despite this, LX3 treatments induce transcriptional
immune responses leading to sustained decreases in many opportunistic bacterial and fungal pathogens, as well as selective
enrichment of core symbionts including Bombilactobacillus,Bifidobacterium,Lactobacillus, and Bartonella spp. These changes are
ultimately associated with greater brood production and colony growth relative to vehicle controls, and with no apparent trade-offs
in ectoparasitic Varroa mite burdens. Furthermore, spray-LX3 exerts potent activities against Ascosphaera apis (a deadly brood
pathogen) likely stemming from in-hive dispersal differences, whereas patty-LX3 promotes synergistic brood development via
unique nutritional benefits. These findings provide a foundational basis for spray-based probiotic application in apiculture and
collectively highlight the importance of considering delivery method in disease management strategies.
The ISME Journal; https://doi.org/10.1038/s41396-023-01422-z
INTRODUCTION
Managed honey bee (Apis mellifera) populations are leveraged
globally to support adequate pollination of food crops and
contribute upwards of US$225 billion annually to agricultural
economies [1]. However, maintaining the health of these crucial
insects (as well as wild pollinators) within agroecosystems is
exceedingly difficult in the face of widespread pesticide use and
diminished floral diversity, which together exacerbates suscept-
ibility to infectious disease [2]. Contributing most to the
unsustainable colony losses reported in the US over recent years
are ectoparasitic mites and a broad range of bacterial, fungal, and
viral pathogens. Despite vigilant efforts of beekeepers in applying
antibiotics (e.g., oxytetracycline, fumagillin) and other hive
chemicals (e.g., varroacides) as prophylactic measures against
disease outbreaks, there has been no clear progression toward
improvement with annual colony losses averaging ~39.4% (more
than twice the sustainable limit of 15%) over the last decade [3].
Moreover, mounting evidence suggests these efforts could have
counterproductive outcomes by promoting antibiotic resistance
and inadvertently damaging the honey bee gut microbiota—a
critical health component orchestrating overall colony-level
survival via effects on digestion [4], immune regulation [5], and
overwintering success [6]. In accordance with these realizations,
there is a growing interest in supporting honey bee health via
microbiota modulation strategies, and relatedly, how probiotics
can be used as sustainable methods of disease control [7].
While fungal communities remain poorly described, bacterial
communities associated with honey bees have been intensively
studied [8,9]. Regarding the bacterial component, honey bees
possess a stable “core”gut microbiota (present in nearly all
healthy individuals worldwide) that is dominated by several lactic
acid-producing genera (Bombilactobacillus,Lactobacillus, and
Bifidobacterium) and select proteolytic genera (Gilliamella and
Snodgrassella)[10]. Other bacteria such as Apilactobacillus,
Frischella,Commensalibacter, and Bartonella spp. found at lower
abundance can also play important health roles [11]. General
consensus suggests that low levels of lactic acid bacteria (LAB) and
high levels of proteobacteria are a sign of gut dysbiosis and
deteriorated health status in honey bees [12]. This trend is
consistently observed in social animals including humans and
mice, despite divergent physiology and gut microbiota composi-
tion [13]. In vitro experiments have shown that LAB (endogenous
Received: 21 September 2022 Revised: 17 April 2023 Accepted: 20 April 2023
1
Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON N1G 2W1, Canada.
2
Department of Microbiology & Immunology, University of Western Ontario,
London, ON N6A 5B7, Canada.
3
Department of Biology, The University of Western Ontario, London, ON N6A 5B7, Canada.
4
Department of Entomology and Nematology,
University of California, Davis, Davis, CA 95616, USA.
5
Agricultural Research Service, United States Department of Agriculture, Davis, CA 95616, USA.
6
University of California
Agriculture and Natural Resources, Oakland, CA 95618, USA.
7
These authors contributed equally: Gregor Reid, Elina Niño. ✉email: elnino@ucdavis.edu
www.nature.com/ismej
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and exogenous strains) exhibit the strongest inhibitory properties
against many important larval pathogens including Paenibacillus
larvae (bacterial agent of American Foulbrood disease), Melisso-
coccus plutonius (bacterial agent of European foulbrood), Asco-
sphaera apis (fungal agent of Chalkbrood disease), and Aspergillus
niger (fungal agent of Stonebrood disease) [14–16]. Despite
variable mechanisms of action, oral supplementation of inhibitory
LAB strains (via larval food inoculation) under laboratory condi-
tions have consistently been shown to improve survival against
the aforementioned pathogens. In contrast, field studies have
shown less reproducibility. For example, a 13-strain mixture of
honey bee-specific lactobacilli failed to prevent American
foulbrood [17] despite showing strong in vitro activity against P.
larvae [14]. Although the complex social behaviors of honey bees
may contribute to the inconsistencies observed between labora-
tory and field study findings, a simpler explanation could be
related to methodological differences in delivery method (see
review [18]).
There has been very limited investigation into how delivery of
probiotics to the hive can impact their efficacy, with predomi-
nantly two main strategies having been tested: sucrose syrup
suspension and pollen patty inoculation. Sucrose syrup suspen-
sions remain the most popular method based on ease of
application, but osmotic stress can induce rapid cell lysis of many
bacteria (e.g., >90% reduction in LAB cell viability within 96 h at
30 °C [19]) making it a poor delivery vehicle in most instances. In
contrast, pollen patty-based methods offer a more suitable system
(viable bacteria shown to reach their intended targets of the
honey bee intestinal tract [20,21]) while also contributing
nutritional benefits that support overall health [22]. A major
drawback to either method is that only adult bees directly
consume the product, meaning that distribution throughout the
brood chamber (where many larval pathogens exist) is primarily
reliant on nutrient flux between nurse bees (which directly
consume the product) and larvae (which are fed by nurse bees via
trophallaxis) in the hive [21]. One way to ensure physical
dispersion through the brood chamber could be through dusting
of hive frames with freeze-dried bacteria [23]. From a practical
perspective though, this would be time-consuming alongside
concerns of clumping and uneven distribution. We herein explore
a potentially promising alternative, namely a spray-based applica-
tion of beneficial bacteria suspended in an isotonic solution.
Spray-based probiotic applications have broad relevance with
recent studies showing success against clinically relevant viral
infections [24] as well as in the prevention of white-nose
syndrome in the little brown bat (Myotis lucifugus)[25]. Notably,
a spray-based approach could largely eliminate any viability
concerns (e.g., bacterial cells are estimated to be viable on a scale
of months to years in phosphate-buffered saline [26]) and has
potential to enable high-throughput dispersal of bacterial
inoculum throughout an entire hive.
In our previous work, we demonstrated that a patty-based hive
supplement comprising a three strain LAB consortium (Lactiplan-
tibacillus plantarum Lp39, Lacticaseibacillus rhamnosus GR-1, and
Apilactobacillus kunkeei BR-1; herein referred to as LX3) could
strongly suppress P. larvae burden in both symptomatic and
asymptomatic colonies, as well as induce beneficial effects on
adult immune and microbiota systems [20,27]. Based on these
multifaceted mechanisms and general improvement of host
constitution, LX3 could be expected to increase resistance toward
a broad range of infectious diseases. The spray-based delivery
system we developed in the current study was used to compare
LX3 application with the previously established patty-based
(“BioPatty”) system for purposes of improving overall colony-
level health in a pathogen-dense region of California. A 24-week
longitudinal field study was performed with analyses focused on
the systems-level characterization of entire bacterial and fungal
communities (rather than single pathogens of interest) to provide
a more complete understanding of the microbial ecology of honey
bee health and disease.
METHODS
Bacterial strains and culture conditions
The three lactobacilli strains used in this study were L. plantarum Lp39
(American Type Culture Collect [ATCC] 14917), L. rhamnosus GR-1 (ATCC
55826), and Api. kunkeei BR-1 (a honey bee gut-derived isolate from a
healthy hive [27]). Routine culturing of these strains was performed under
microaerophilic conditions at 37 °C using de Man, Rogosa, and Sharpe
(catalog number: 288130, BD Difco) broth or agar supplemented with 10 g/
l D-fructose (catalog number: F-3510, Sigma-Aldrich; MRS-F). Harvest of
bacterial cells was similar for patty- and spray-based LX3 treatments.
Briefly, fresh streak plates were incubated overnight and then a single
colony of each strain was used to inoculate multiple broth cultures (i.e.,
LX3 strains were grown separately to ensure standardized dose) that were
subsequently incubated at 37 °C for 24 h using sterile 50 ml polypropylene
conical tubes (catalog number: 339652, Thermo Scientific; MRS-F filled to
50 ml, lids tightly closed). Following incubation, bacterial cells were
centrifuged at 5000 × gfor 10 min (4 °C), washed once with 0.01 M PBS,
centrifuged again at 5000 × gfor 10 min (4 °C), resuspended in 0.01 M PBS,
and then strains were mixed together in a final concentrated volume of
4 ml 0.01 M PBS at equal cell densities (i.e., the mixture contained 5 × 10
10
colony forming units [CFU] of each LX3 strain). Ultimately, all hives treated
with LX3 (using either patty- or spray-based methods) received the same
total amount of bacterial cells (i.e., 5 × 10
10
CFU of each LX3 strain) at W0
and W2 timepoints.
Recipe for patty- and spray-based LX3 treatments
For patty-based treatments, the base nutritional matrix of each 250 g patty
consisted of the following standard pollen substitute ingredients: 28.5 g of
soy flour, 74.1 g of granulated sucrose, 15.4 g of debittered brewer’s yeast,
132.1 g of a 2:1 (w/v) simple sucrose-based syrup solution. For LX3 patties,
4 ml of the concentrated LX3 suspension in 0.01 M PBS was added
followed by vigorous stirring to obtain a homogenous infusion at a final
concentration of 2 × 10
8
CFU/g for each strain. For vehicle patties, 4 ml of
sterile 0.01 M PBS was added instead. Each patty was poured in between
two sheets of wax paper (30 cm × 45 cm) and immediately supplemented
to hives (placed on top of brood chamber) within a 24 h period.
For spray-based treatments, the 4 ml concentrated LX3 suspension was
added to 28 ml of 0.01 M PBS in a sterile spray bottle to obtain a diluted
concentration of 1.56 × 10
9
CFU/ml per strain. We determined that the
nozzles of the bottles discharged 2 ml per spray. Accordingly, 32 ml of the
LX3-containing suspension was administered to the hive via 16 standar-
dized spray actions (2 × 2 ml front and back of each brood frame, for 8
brood frames per hive). The same spray sequence was used for the vehicle
spray, but with 32 ml sterile 0.01 M PBS added to a sterile spray bottle
instead. Supplementation with probiotic treatments occurred at W0 and
W2 timepoints (after hive measurements and sampling were completed in
both instances) and hives were monitored thereafter until W24.
Apiary set-up, experimental overview, and sampling
procedure
Field trials were performed on managed honey bee colonies headed by
naturally mated queens of mixed Italian background (A. m. ligustica). For
the duration of the study, colonies were located at two experimental
apiaries (LTRAZ and RAPTOR) approximately four miles apart near the
University of California, Davis (Davis, California, United States). A total of 33
colonies (17 located in LTRAZ and 16 located in RAPTOR) were used for this
study and were housed in standard Langstroth hives that were elevated
~36 inches above ground level using wooden hive stands. Hives were
randomly distributed across the two sites (to account for spatial
confounders) and divided into five experiments groups: (1) no treatment
control (NTC; n=7 hives), (2) pollen patty vehicle control (P; n=7 hives),
(3) pollen patty containing LX3 (P +LX3; n=7 hives), (4) spray vehicle
control (S; n=6 hives), and (5) spray containing LX3 (S +LX3; n=6 hives).
The relevant hives of interest were treated at the start of the study (W0)
and again 2 weeks later (W2), followed by a 22-week monitoring period
during which none of the groups received any further treatment.
Prior to start of the experiment, all colonies were split and equalized to
contain approximately equal number of frames of bees, brood, and food
stores (pollen and nectar/honey) and were requeened with age-matched
B.A. Daisley et al.
2
The ISME Journal
queens from the same queen producer (Jackie Park-Burris Queens, Inc.,
Palo Cedro, CA). Hives were provided a queen excluder and supered with a
fresh 10-frame deep box to allow for quantification of honey stores.
Colonies were managed according to standard practices suitable for the
dry Northern California climate often lacking natural forage throughout the
year. Specifically, all hives were provisioned with carbohydrate supplement
(ProSweet syrup, Mann Lake Ltd., Woodland, CA) prior to project start (04/
12 and 04/23) and at W2 and W4, and all hives were provisioned with
protein supplement (Bee-Pro Patties+, Mann Lake, LTD., Woodland, CA) at
W6 and W13.
Sampling of adult nurse bees (found in close physical proximity of larvae
in the brood chamber) occurred mid-day at W0, W2, W4, W6, W8, W12, and
W24 timepoints and was achieved by gently dragging a 50 ml conical tube
(catalog number: 339652, Thermo Scientific) along the surface of the most
central brood frame in a hive until the container was approximately half
full (~50 bees in total). Samples were then flash frozen in the field using
liquid nitrogen and subsequently stored at −80 °C until downstream DNA/
RNA extraction steps. All sensible precautions were taken to prevent cross-
contamination of LX3 strains and potential pathogens between hives; new
sets of sterile latex gloves were used for each hive, hive tools were flame
sterilized between opening of hives, and a strict group-wise sampling
order was enforced (e.g., NTC →S→P→S+LX3 →P+LX3).
Estimation of colony sizes
Frames of bees were determined via routine methods at W0, W2, W4, W6,
W8. W12, W16, W20, and W24 timepoints. Under the assumption that each
deep brood frame (43.1 cm × 20.3 cm) can hold ~2430 adult workers [28],
total colony size was calculated as total frame of bees × 2430 workers
per frame.
Determination of capped brood area
To assess how treatments impacted the reproductive performance of
queens, coverage of capped brood cells on hive frames were measured at
W0, W2, W4, W6, W8, W12, and W24 timepoints. Digital photographs were
taken for both sides of the center three frames (labeled 3, 4, and 5 to
ensure consistent order) in the brood chamber of each hive (i.e., 6 photos
per hive, per timepoint). Subsequently, capped brood cells were quantified
from a total of 1386 photographs collected during the field study.
Determination of ectoparasitic mite loads
Varroa destructor was the only ectoparasite detected in the cohort of
honey bees assessed in this study. Hive burden of this parasite was
determined monthly at W0, W4, W8, and W12 timepoints. Routine alcohol
wash was used to measure V. destructor burden. Briefly, a half-cup
measuring device was used to collect ~300 adult bees from active brood
areas, the bees were placed in a jar containing 70% ethanol, shaken
vigorously for 1 min, and then dislodged mites were collected (via size-
based exclusion through a mesh strainer), counted, and recorded. Raw
counts were converted to percent mite population (i.e., number of mites
per 100 bees) for interpretation purposes.
TRIzol-based dual extraction of RNA and DNA
Adult honey bee samples were thawed from −80 °C and dissected in a
cold room at 4 °C. Samples were visually screened for rectums yellow-to-
orange in appearance (indicating a pollen-based diet of nurse bees)
whereas rectums with a translucent appearance (indicating a nectar-based
diet of foragers) were discarded. The dissected hindguts and heads of
selected samples were then pooled in biological triplicate and homo-
genized in TRIzol (Invitrogen) by bead beating, followed by RNA extraction
via the manufacturer’s instructions. From the remaining TRIzol interphase
layer, DNA was extracted using a back-extraction buffer consisting of 4 M
guanidine thiocyanate, 50 mM sodium citrate dihydrate, and 1 M Tris base,
as previously described [20]. Quality of RNA and DNA was assessed using a
microvolume spectrophotometer (DS-11 Spectrophotometer; DeNovix).
Samples with A260/280 absorbance ratios between 1.8 and 2.2 for RNA
and between 1.6 and 2.0 for DNA were considered for further analyses.
Amplicon library construction and sequencing parameters
Targeted amplification of the V3-V4 region of the 16S rRNA gene was
achieved using the established Bakt_341F (5’-CCTACGGGNGGCWGCAG-3’)
and Bakt_805R (5’-GACTACHVGGGTATCTAATCC-3’) primer set, shown to be
optimal for characterization of honey bee-associated bacterial communities
[29]. For fungal community profiling, the ITS1f (5’-CTTGGTCATTTAGAG-
GAAGTAA-3’) and ITS2 (5’-GCTGCGTTCTTCATCGATGC-3’)modified primer set
was used as specified in the Earth Microbiome Project (EMP) ITS amplicon
protocol (https://earthmicrobiome.org/protocols-and-standards/its). Full pri-
mer constructs including 12-mer GOLAY barcodes and Illumina adapters are
provided in Supplementary Data 1A, B). Initial amplicon generation was
similar for both libraries and was achieved by adding 2 µl of sample DNA
(5–50 ng/µl) to a 96-well 0.2 ml PCR plate containing 20 µl of pre-mixed
forward and reverse primers (both at working concentrations of 1.6 µM).
Next, 20 µl of GoTaq 2X Colorless Master Mix (Promega) was added to each
well (final volume of 42µl) and plates were sealed using PCR-grade adhesive
aluminum foil. PCR steps were performed using a Prime Thermal Cycler
(Technie) with 105 °C lid temperature with the following reaction conditions:
95 °C for 3min, followed by 30 cycles of 95 °C for 1 min, 52 °C for 1 min, and
72 °C for 1 min, followed by a final extension step at 72 °C for 5 min, and then
amplicons were stored at −20°C until further processing. Processing of
amplicon libraries was conducted at the London Regional Genomics Centre
(Robarts Research Institute, London, ON, Canada) in which amplicons were
quantified using PicoGreen (Quant-It; Life Technologies, Burlington, ON,
Canada), pooled at equimolar ratios, and sequenced on the MiSeq platform
(Illumina) adapted for 2 × 300 bp paired-end V3 chemistry.
General processing of sequencing datasets
All code and scripts used for processing sequence data have been
uploaded to a repository and made publicly available at https://
github.com/bdaisley/LX3CA1. Briefly, sequencing FASTQ files for both
datasets were demultiplexed using Cutadapt (v3.4) with default settings for
combinatorial dual indexes and 0% error tolerance. Forward and reverse
sequence reads were then dereplicated, denoised, and merged via the
DADA2 (v1.16) pipeline to infer exact (i.e., 100% identity, not clustered)
amplicon sequence variants (ASV) from the datasets (quality profiles
shown in Supplementary Fig. 1). For the 16S rRNA dataset, a total of
6,040,822 filtered reads remained following quality assurance steps and
after denoising, a total of 848 unique bacterial ASVs were identified. For
the ITS dataset, a total of 6,604,619 filtered reads remained following
quality assurance measures and after denoising, a total of 3387 unique
fungal ASVs were identified (Supplementary Data 1C). ASV read counts
were left in their unadulterated state for analysis (i.e., read counts were not
adjusted using predicted genomic copy number differences) based on the
latest best-practice recommendations [30]. Zero counts were adjusted via a
Bayesian-based multiplicative zero-replacement method using the cmul-
tiRepl function of the zCompositions (v1.3.4) package in R (v4.0.1) and
count tables were center log-ratio (CLR) transformed prior to composi-
tional comparisons [31]. Raw FASTQ files were uploaded to the NCBI
Sequence Read Archive and are accessible under BioProject IDs
PRJNA856263 (16S rRNA dataset) and PRJNA856341 (ITS dataset).
Taxonomic annotation of bacterial and fungal sequencing
reads
To assess the biological relevance of sequencing data, bacterial ASVs
were assigned taxonomy using the idtaxa function of the DECIPHER
(v2.20.0) package in R with the BEExact (v2021.0.2; https://github.com/
bdaisley/BEExact) pre-trained V3V4 database [29], which enabled species-
level classification for 97.9% of sequences (including “bxid”annotations
of taxa lacking formal nomenclature; global pairwise identity scores in
Supplementary Data 1D). Fungal ASVs were assigned taxonomy in a
similar manner using the UNITE database (v8.3-RefS; https://doi.org/
10.15156/BIO/1280049), which enabled species-level classification for
35.17% of sequences (Supplementary Data 1E). To enable further
predictive analysis with unclassified ASVs (belonging to bacterial and
fungal dark matter) we applied phylogenetically-consistent placeholder
names based on closest identities with known species representatives as
previously described [29]. Briefly, all unclassified ASVs were cross-
referenced at their lowest common ancestor (LCA) rank using NCBI’s
Bacterial 16S rRNA and Fungal ITS RefSeq Targeted Loci project (https://
ftp.ncbi.nlm.nih.gov/refseq/TargetedLoci) databases, which contain
curated reference sequences from type strain material. Subsequently,
pairwise distance matrices between ASVs were used to generate
probabilistic de novo taxonomic groupings (via a greedy-clustering
algorithm) at each unclassified taxonomic rank based on previously
established thresholds [32,33]. To differentiate between bacterial and
fungal placeholder names, sequences were annotated as either “bSV-###”
or “fSV-###”, respectively. Scripts used for generating de novo taxonomy
are available at https://github.com/bdaisley/LX3CA1.
B.A. Daisley et al.
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The ISME Journal
Microbial diversity, differential abundance, and correlation
analyses
Alpha diversity metrics were calculated using the microbiome (v.1.14.0)
package in R. Unconstrained (PCoA) and constrained (db-RDA) analyses of
beta diversity were determined using the phyloseq (v1.36.0) and philr
(v1.18.0) packages in R. The “deicode”plugin for QIIME2 (v2021.4) was used
to generate robust-CLR (rCLR) ordination plots. Treatment group
comparisons of beta diversity metrics were calculated via PERMANOVA
tests with the “adonis2”function of the vegan (v2.5.7) package in R
(permutations =9999, method =“euclidean”). To assess pairwise multi-
level comparisons, the “pairwise.adonis2”function of the pairwiseAdonis
package (v0.4) in R was used with apiary location (i.e., LTRAZ or RAPTOR)
and hive identity set as strata factors in the model block design. In
addition, the recently developed Wd*-test (a robust alternative for
distance-based multivariate analysis of variance [34]) and associated
Tw2-tests for multiple comparisons were implemented for consensus
purposes. Differential abundance tests were performed using a generalized
linear mixed model approach with the MaAsLin2 package (v1.7.3) in R.
Briefly, CLR-transformed relative abundance values were used as input
values with timepoint, treatment group, capped brood area, and colony
size as fixed effects; the latter factors were determined as potential
confounders in the metadata using the envfit function of the vegan
package (v2.5.7) in R (see Supplementary Data 1F). Relatedly, change point
analyses at the phylum-level were determined using a spline-based
permutation method via the sliding_spliner function of the splinectomeR
package (v0.1.0) in R. For host immune-microbe correlation analyses, the
aldex.corr function of the ALDEx2 package (v1.24.0) in R was utilized with
Ct values (for host gene expression, normalized to the Rp5S endogenous
control) and CLR-transformed relative abundances used as input values.
Co-occurrence network analysis
Networks were constructed and analyzed using the NetCoMi package
(v1.0.2.9001) in R. A recommended prevalence cut-off of 50% (i.e., bacterial
and fungal ASVs were only included as inputs if they were present in least
half of all samples) was utilized to address sparsity limitations of
sequencing data. Remaining zeroes in the datasets were addressed using
the multRepl function (method =”CZM”) prior to normalization via CLR-
transformation [31], and then networks were produced via the netCon-
struct function (multiple comparisons corrected using options: measure =
”spearman”, nboot =1000, adjust =”adaptBH”, alpha =0.05). Global net-
work and largest connected component (LCC) properties were determined
using the netAnalyze and netCompare functions with default settings.
qPCR-based measurements of immune gene expression and
absolute microbial abundances
To determine immune gene expression in hindgut and mouthpart samples,
a total of 1500 ng extracted RNA from each sample was reverse transcribed
to cDNA using the High-Capacity cDNA Reverse Transcription Kit following
manufacturer’s instructions (Applied Biosystems, catalog number: 4368813).
RT-qPCR amplifications (10 µl each in technical triplicate) were performed
with tenfold-diluted cDNA using the Power SYBR Green kit (Applied
Biosystems) following manufacturer’s instructions. Oligonucleotide primers
targeting immune genes and the bacteria of interest are listed in
Supplementary Data 1G. As per MIQE guidelines, honey bee α-tubulin was
used as an endogenous control gene as described in previous work [20]. To
determine bacterial and fungal absolute abundances, qPCR amplifications
reactions (10 µl each in technical triplicate) were performed with tenfold-
diluted DNA (derived directly from TRIzol back extraction) using the Power
SYBR Green kit (Applied Biosystems) following manufacturer’s instructions.
The universal Bakt_341F/Bakt_805R and ITS1f/ITS2 primer sets (described in
the sequencing section) were used to quantify total bacteria and fungi,
respectively. All qPCR-based amplifications were performed in DNase- and
RNase-free 384-well microplates using a QuantStudio 5 Real-Time PCR
System (Applied Biosystems) and analyzed with associated QuantStudio
Design and Analysis software. Relative immune gene expression was
calculated using the 2
−ΔΔCt
method [35]. Absolute microbial quantification
was calculated using an experimentally-determined standard curve as
described previously [20]. Using the honey bee Rp5S gene as a stable
reference point for host DNA concentration, an average Ct value of 20.96
was experimentally determined to correspond to a 10 mg (wet-weight)
hindgut sample after considerations of dilution and extraction efficiency
factors). Accordingly, before performing calculations to estimate DNA copies
of 16S rRNA and ITS, sample ΔCt
Rp5S
(relative to a setpoint of 20.96) was used
to standardize Ct values corresponding to bacterial and fungal loads,
respectively (i.e., absolute abundance data assumes an average hindgut
weight of 10 mg per individual).
Molecular quantification of microscopic parasites
For simplicity, parasite loads in this section refer strictly to Deformed wing
virus (DWV) and Vairimorpha ceranae (formerly known as Nosema ceranae).
Hive burden of these parasites was determined monthly at W0, W4, W8,
and W12 timepoints. For DWV and V. ceranae, burdens were quantified (via
qPCR and species-specific primers) using RNA and DNA, respectively,
extracted from nurse bee hindgut tissue to remain consistent with immune
and microbiota analyses. In each case, three pooled hindgut samples were
assessed from each hive per timepoint (n=396 in total). Cycle threshold
(Ct) values for DWV were normalized to the relative expression of honey
bee α-tubulin (endogenous control; XM_391936) and then expressed as the
change in (normalized) DWV load at W4, W8, and W12 relative to baseline
at W0. Alternatively, Ct values for V. ceranae were converted to absolute
abundance using an experimentally determined standard curve and then
standardized to a hindgut wet-weight of 10 mg using the genomic DNA
abundance of the honey bee Rp5S gene as a reference point (i.e., average
Ct threshold of 20.96 for Rp5S approximately corresponds to a 10 mg
hindgut after consideration of dilution and extraction factors; see bacterial
and fungal quantification sections for further details).
Amino acid measurements and meta-analysis comparisons
Nutrient analysis of pollen patty matrices was performed by SGS
Laboratories (Mississauga, ON). Amino acid profiles were analyzed using
official AOAC 994.12 methodologies for livestock feed analysis. For
purposes of comparing the relative protein quality of LX3 fermented
patties, essential amino acid composition for beebread (a mixture of pollen
with nectar or organic honey) and 16 common pollen sources were
derived from previous reports [36–38]. Principal component analysis (PCA)
was performed using the prcomp function in R (v3.6.0) with amino acid
concentrations (g/16g N) used as input values and data scaled to be zero
centered using the center =TRUE option. PCA results were plotted using
the scatter3D function (options: theta =−202, phi =0, bty =“u”) of the
“plot3D”(v1.4) package in R (v3.6.0).
RESULTS
LX3 effects on colony-level health metrics are modulated by
delivery method
A methodological overview of the study is provided in Fig. 1A.
Firstly, to determine how the different treatments affected colony-
level health outcomes, we quantified the coverage area of capped
brood (established metric for assessing colony strength and
reproduction [39]) from over 1000 hive frame photos taken during
the study. Three-way analysis of variance (ANOVA) identified that
timepoint (F
6,154
=30.42, p< 0.0001) and LX3 treatment
(F
1,154
=41.23, p< 0.0001) had significant main effects, while
vehicle type and LX3 treatment showed an interactive effect
(F
1,154
=3.91, p=0.0498; Fig. 1B, C). Pairwise comparisons showed
that P +LX3 and S +LX3 treatments significantly increased
capped brood between W2–W8 by magnitudes of over 100%
and 70%, respectively, compared to vehicle controls (Fig. 1B).
These findings are corroborated by previous work showing LX3
can rescue antibiotic-induced deficits in capped brood [20].
Complementing capped brood data, colony size (approximate
number of adult bees per hive) also showed temporal
fluctuations (Fig. 1D), which is consistent with expected seasonal
variation [40]. Baseline colony sizes for the NTC group were
smallest at W0 (~15,000 adults/hive) and largest at W20 (~38,000
adults/hive), with the P and S vehicle controls showing similar
temporal dynamics. Pairwise comparisons showed larger colony
sizes for P +LX3relativetoPatW16(p=0.0274), W20
(p=0.0545), and W24 (p=0.0051). S +LX3 treatment also
demonstrated a similar but less pronounced trend toward
increased colony size (Fig. 1D). Thus, early timepoint changes in
capped brood corresponded proportionately with later time-
point colony size measurements for S +LX3 and P +LX3 groups
(Fig. 1B–D). Single timepoint weighing of honey supers
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(additional boxes provided for honey storage used as an
approximation of honey production) further showed a trend
toward increased weight in the P +LX3 group relative to P
group at W12, although the difference did not reach statistical
significance (33.30 ± 1.86 vs. 28.78 ± 2.82 kg/hive, two-tailed t-
test, p=0.2054; Fig. 1E). Honey production nonetheless showed
a positive correlation with changes in capped brood (r
s
=0.4380,
p< 0.0122) and colony size (r
s
=0.5644, p=0.0008, Spearman
correlations; Supplementary Fig. 2), indicating potential colony
performance benefits relevant to commercial beekeeping. These
findings support that both patty- and spray-based LX3 treatment
had positive overall impacts on colony status.
Pollen patty nutritional benefits synergize with LX3 colony
growth-promoting properties
Given L. plantarum (present in LX3) can increase plant protein
digestibility by 92% [41], we sought to determine whether
nutrient factors may explain the pronounced colony effects seen
Fig. 1 Delivery method alters LX3 effects on honey bee colony-level health metrics. A Schematic diagram outlining the experimental
design. The 4 week treatment period consisted of two treatment doses that were administered at W0 and W2. No treatment occurred in any
of the groups from W4 to W24. BCapped brood counts normalized to baseline values at W0 with individual hive estimates calculated from
brood-box frame photos using semi-automated counting software. Data points depict mean ± SE for each group. Statistical comparisons
within patty and spray groups are shown for three-way ANOVA with BH-adjusted pvalues. CRepresentative hive frame images used for
enumeration of capped brood. Each hexagonal cell covered (capped) with wax and displaying a yellow-to-beige appearance depicts a single
larva undergoing pupation to become an adult worker bee. DEstimated colony size of adult bees based on total hive frames containing adult
bees. Data points depict mean ± SE for each group with statistics shown for three-way ANOVA with BH-adjusted pvalues. EHoney yield
estimates at W12. Each data point depicts the weight of a honey super from a distinct hive, with bars representing the mean ± SE for each
group shown. Statistics shown for unpaired two-tailed t-tests. *p< 0.05, **p< 0.01, ***p< 0.001, ns not significant.
B.A. Daisley et al.
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in the P +LX3 group (Fig. 1). In the laboratory, we incubated
pollen patties (with or without LX3 added) for 1 week under
simulated hive conditions and found that LX3 could greatly
improve amino acid profiles. Specifically, 11 amino acids
increased by 2–20% from LX3 addition (Supplementary Fig. 3
and Supplementary Data 1H). A meta-analysis demonstrated
that LX3 shifted essential amino acid (EAA) content closer
toward the “optimal”honey bee diet EAA ratio (as established by
DeGroot et al. [42]) compared to 16 common pollen sources
(Supplementary Fig. 3B). Principal component analysis (PCA;
Supplementary Fig. 3C–F) further revealed EAA profiles of the
LX3-containing patty were highly similar to known compositions
of beebread—similarities which could relate to the fact that Api.
kunkeei (present in LX3) is abundantly found in high-quality
beebreads [43] and plays a key role in facilitating spontaneous
pollen-to-beebread fermentation under natural conditions [44].
Altogether, LX3 fermentation improved nutrient content of the
base patty supplement which likely contributed to distinct
colony-level effects in the P +LX3 group (Fig. 1). While these
findings have major implications related to supplemental
feeding practices in beekeeping, non-nutritive factors of LX3
cannot be discounted on the basis that S +LX3 (containing no
additional nutrients) also demonstrated colony growth-
promoting effects (Fig. 1B–D).
LX3 treatment increases brood populations without trade-offs
in ectoparasite burdens
Since total amount of capped brood in a hive can pose safety
concerns relating to Varroa destructor mite burdens [45], we
investigated the impact of LX3 treatment on infestation levels.
Baseline levels of V. destructor were less than 5% (i.e., 5 mites per
100 adult bees) and similar between all groups at W0, with LX3
treatment (F
1,8
=11.132, p=0.001) but not timepoint
(F
1,3
=0.914, p=0.438) or vehicle type (F
1,8
=0.099, p=0.754,
three-way ANOVA) affecting these levels over time (Fig. 2A).
Pairwise comparisons demonstrated reduced mite burdens at
W8 for P +LX3 (p=0.03) and S +LX3 (p=0.02) groups relative
to vehicle controls. Deformed wing virus (DWV) and Vairimorpha
ceranae (formerly Nosema ceranae) were also assessed given
their co-prevenance with V. destructor [46], although no vehicle
or LX3-specific effects were observable (Fig. 2B, C). These
findings support that LX3 treatment can increase brood
populations without worsening existing parasite burdens, and
furthermore are consistent with a recent 2-year study from
Fig. 2 Effect of LX3 treatment against common honey bee parasites. A Varroa destructor mite populations were measured via standard
alcohol wash method. Each data point represents the estimated change in mite population (i.e., number of mites per 100 bees) for an
individual hive, with approximately n=300 adult bees sampled per hive (n=33 hives total) at each timepoint. Patty and spray group
comparisons shown for three-way ANOVA tests with BH-adjusted pvalues. BViral burden of Deformed wing virus (DWV) and (C)
microsporidian burden of Vairimorpha ceranae were assessed via qPCR-based quantification using RNA and DNA extracted from nurse bee
samples, respectively. Each data point represents a distinct hive with three pooled samples taken from each of the 33 hives per timepoint
(n=396 total). Patty and spray group comparisons shown for three-way ANOVA tests with BH-adjusted pvalues. Trend lines with 95%
confidence bands computed via “loess”method using ggplot2 package in R. ns not significant.
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Argentina demonstrating certain lactobacilli can exert anti-V.
destructor properties [47].
LX3 strains are transiently detectable but do not colonize
Adult “nurse”-aged bees play critical roles in horizontal transfer of
gut microbes through trophallaxis, larval rearing activities, and
pollen processing [48], and thus provide a well-balanced
representation of overall microbial diversity in a hive. To assess
in-hive dispersal of the LX3 strains, we performed qPCR-based
quantification of L. plantarum,L. rhamnosus, and Api. kunkeei in
nurse bees at pre-treatment (W0) and various timepoints post-
treatment (Supplementary Fig. 4). Hindgut samples from both
P+LX3 and S +LX3 groups at W4 (directly after treatment)
demonstrated a detectable increase in L. plantarum and L.
rhamnosus (Supplementary Fig. 4A, B). The S +LX3 group also
showed an increase in Api. kunkeei, whereas a trend toward
increased levels was observed in the P +LX3 group (p=0.1273;
Supplementary Fig. 4C). By W24 (20 weeks post-treatment), all
levels returned to baseline (Supplementary Fig. 4A–C). These
results support the safety of LX3 supplementation, indicate both
delivery methods achieve similar dispersal rates, and highlight
long-term colonization is not required for LX3 supplementation to
be beneficial.
Multivariate analysis reveals distinct and long-lasting effects
of LX3 treatment on nurse bee microbiota
Both 16S rRNA and ITS amplicon sequencing were performed to
elucidate the potential impacts of LX3 treatment on indigenous
microbial communities found in association with honey bees
(Fig. 3). From a sample size of N=594 nurse bee hindguts
(randomly and evenly collected from the brood chamber of each
of the 33 hives at W0, W2, W4, W8, W12, and W24), we detected a
total of 848 bacterial and 3387 fungal unique amplicon sequence
variants (ASVs), respectively (Supplementary Data 1C). Genus-level
agglomeration of ASVs demonstrated that Bombilactobacillus
(previously known as Lactobacillus Firm-4 phylotype [49]),
Lactobacillus (Firm-5 phylotype), Bifidobacterium,Gilliamella,Snod-
grassella,Frischella,Bartonella, and Commensalibacter were the
most dominant bacterial genera (Fig. 3A). Fungal communities
showed greater compositional variability with “fSV-g191”(a
predicted genus formed by unclassified taxa within the family
Myxotrichaceae), Ascosphaera (consisting of a single species, A.
apis—causal agent of chalkbrood), and Cladosporium representing
the dominant genera found in most samples (Fig. 3B).
Aitchison distance (β-diversity metric) comparisons via permu-
tational multivariate analysis of variance (PERMANOVA) tests
identified that timepoint (F
5,186
=4.52, p=0.001) and LX3
treatment (F
1,186
=4.13, p=0.001) had significant main effects
on microbiota composition, while Wd*-tests (robust to hetero-
scedasticity [34]) further supported an interaction between these
explanatory variables (Wd*
5,187
=2.70, p=0.001; Supplementary
Data 1I, J). Similar findings were observed using several other
distance metrics including Bray-Curtis, Jensen-Shannon, UniFrac,
and PhILR (Supplementary Figs. 5 and 6). To visualize the
independent effects of LX3 treatment after adjusting for time-
point, constrained ordination was performed via distance-based
redundancy analysis (db-RDA; Fig. 3E, F). The centroids of each
experimental group clustered around the NTC group on Axis-1
driven by temporal factors (near zero, indicating a good model fit
for corrections with F
2,196
=7.2436 and p< 0.0001 for “anova.cca”
permutation tests), whereas there was significant deviation along
Axis-2 driven by treatment factors as seen for P +LX3 and S +LX3
relative to the vehicle control groups (Fig. 3G, H). A clear
separation in microbiota trajectories persisted in both groups at
W24 (Fig. 3F), with change point analysis (via a spline-based
interpolation method [50]) revealing differences in immediacy of
response between P +LX3 and S +LX3 treatments (~3.5 weeks vs.
~1.4 weeks, respectively; Supplementary Figs. 7E, F and 8). Overall,
LX3 treatment had long-lasting effects on microbiota composition
and delivery method impacted the rate at which these microbial
changes occurred.
Microbial richness and abundance respond to LX3 treatment
and correlate with colony size
Since amplicon sequencing does not inform on total microbial
abundance [31], we performed qPCR experiments to quantify
absolute bacterial and fungal loads. An approximate 100-fold
increase in hindgut bacterial loads was observed between W0 and
W24 for all treatment groups, whereas fungal loads increased by a
lesser degree (<10-fold) over the same period (Fig. 3C). Head
samples (including mouthparts) showed identical trends indicating
systemic microbial density changes in the hive (Supplementary
Fig. 9). In contrast, bacterial α-diversity decreased (−0.32 ± 0.05,
p=1.2e−10), while fungal α-diversity increased (0.88 ± 0.15,
p=1.6e−07) in all groups between W0 and W24 according to
Shannon’s H index (taking species abundance and evenness into
account; Fig. 3D) and several other metrics (Supplementary Data 1K,
L).Colony size (Fig. 1D) showed moderate to strong positive
correlations with bacterial abundance (r
s
=0.57, p=2.2e−10),
fungal abundance (r
s
=0.32, p=5.6e−06), and fungal α-diversity
(r
s
=0.35, p=6e−07), and a negative correlation with bacterial α-
diversity (r
s
=−0.25, p=4.2e−04; Supplementary Fig. 10).
Together, these results depict a scenario whereby as the number
of adult bees increase in a colony, so does the total bacterial and
fungal loads of individual colony members, but with bacterial
communities becoming disproportionately abundant (i.e., greater
number of bacterial to fungal cells) and dominated by fewer unique
taxa as opposed to the concurrent increase in taxonomic richness
seen for fungi (Fig. 3C, D).
All LX3 treatments demonstrated clear timepoint-specific
effects on absolute abundance and α-diversity metrics, however,
these differences were marginal in comparison to the overall
magnitude of seasonal trends observed (Fig. 3C, D). One exception
was that S +LX3 showed a marked increase in bacterial loads
starting at W2 while seasonal increases in the control groups did
not occur until W4 (Fig. 3C). A three-way ANOVA supported the
main effects of LX3 treatment on hindgut bacterial loads
(F
1,22
=5.923, p=0.0235) and that vehicle type (i.e., patty vs
spray delivery method) interacted with LX3 effects (F
1,22
=6.033,
p=0.0224; Fig. 3C). Given that the administered LX3 strains were
detectable at similar levels in both P +LX3 and S +LX3 samples
(Supplementary Fig. 4), it is likely that any LX3-derived contribu-
tions to total hindgut bacterial loads were negligible and not the
responsible factor for the unique differences observed.
LX3 treatment supports symbiont enrichment while reducing
bacterial and fungal pathogen burden
We performed differential abundance tests with MaAsLin2 using a
generalized linear mixed model (GLMM) approach [51]to
delineate species-level microbiota effects of LX3 treatment after
adjusting for randomization stratification factors (e.g., hive identity
and apiary location) and covariates affecting community structure
(e.g., seasonal effects, brood counts, and colony size; see
Supplementary Data 1F for full list of variables).
During active supplementation at W2 and W4, LX3 treatment
groups consistently showed an enrichment in core bacterial
symbionts including Bifidobacterium,Lactobacillus,Bombilactoba-
cillus, and Bartonella spp. (Fig. 4A, B and Supplementary Data 1M)
—changes resembling those that epitomize thriving colonies [52].
Cumulative effect coefficients across all timepoints demonstrated
sustained increases for B. indicum,Bomb. mellis,Bomb. mellifer, and
Bart. apis in both LX3 groups relative to vehicle controls (Fig. 4A,
B). Fructobacillus bxid5666 also showed sustained increases in both
groups, and a pronounced increase in Commensalibacter bxid0093
was uniquely observed in the P +LX3 group (Fig. 4A). Alongside
these changes, several Melissococcus and Fructobacillus spp. were
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co-depleted in both LX3 groups, while the S +LX3 group
additionally showed a reduction in opportunistic pathogens
including Hafnia alvei,Escherichia coli,Paenibacillus alvei, and
Paenibacillus dendritiformis (Fig. 4B). Thus, LX3 had beneficial
impacts on the honey bee microbiota by increasing health-
associated symbionts and reducing disease-associated opportu-
nistic pathogens.
Compared to bacteria, fungi showed greater magnitude of
differences with distinct response patterns to P +LX3 and S +LX3
treatments (Fig. 4C, D). The P +LX3 group demonstrated the
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largest number of negative interactions for broad-ranging fungal
taxa including Fusarium longifundum,Alternaria alternata,Koda-
maea fSV-3, Aspergillus intermedius,Tyrannosorus hystrioides,
Filobasidium chernovii, and others (Fig. 4C). However, the most
striking overall reduction was for Ascosphaera apis in the S +LX3
group, reaching near undetectable levels by W24 (Fig. 4D and
Supplementary Data. 1N). Uniquely, the S +LX3 group also
demonstrated a cumulative enrichment in Aureobasidium pull-
ulans,Buckleyzyma aurantiaca,Meyerozyma guilliermondii (Fig. 4D)
—all of which are expected to be beneficial on the basis of either
Fig. 3 Seasonal trends and LX3 treatment show distinct effects on hindgut microbiota composition. Bar plots shown illustrate the genus-
level composition of bacteria (A) and fungi (B) in nurse bee hindgut samples, as determined via 16S RNA gene and ITS region amplicon
sequencing, respectively. Each bar represents a distinct hive with three pooled samples taken from each of the 33 hives per timepoint (n=594
total). CAbsolute abundance (determined via qPCR-based quantification of the total number of 16S rRNA and ITS copies of DNA) and (D)
alpha diversity (measured via Shannon’s H Index) of bacterial (gray) and fungal (green) communities in nurse bee hindgut samples. Data
depicts the median (line in box), IQR (box), and minimum/maximum (whiskers) values for each treatment group at the specified timepoints.
Comparisons shown between treatment groups at each timepoint with statistics derived from two-way ANOVA with BH-adjusted multiple
comparisons. E,FConstrained ordination plots (determined via Aitchison distance-based redundancy analysis; db-RDA) illustrate that
microbiota differences are influenced by both timepoint- and treatment-specific effects. G,HBoxplots show respective timepoint
comparisons along Axis-1 (explaining 5.0% of variance) and treatment group comparisons along Axis-2 (explaining 1.9% of variance). Each
data point is representative of a distinct hive at a distinct timepoint. Statistics shown for pairwise Wilcoxon tests with BH-adjusted pvalues.
Fig. 4 Differentially abundant taxa between LX3 treatment groups and vehicle controls. Differential abundance tests were performed with
MaAsLin2 in R using a generalized linear mixed model (GLMM) approach to delineate species-level microbiota effects of LX3 treatment after
adjusting for seasonal (timepoint) effects and other covariates (see Supplementary Data 1F). Effect plots shown highlight the top differentially
abundant bacteria (A,B) and fungi (C,D) for patty and spray group comparisons of interest. Each bar depicts the cumulative effect coefficient
of the indicated taxa over the entire 24-week study period (i.e., a sustained difference in taxa abundance was observed following the
treatment period). For a comprehensive list including all transient effects and timepoint-specific significant differences, see MaAsLin2 output
provided in Supplementary Data 1M, N).
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plant growth-promoting or pathogen-inhibiting properties
[53–55]. Another finding of interest was that several antibiotic
producing Penicillium spp. were increased by S +LX3 treatment,
whereas P +LX3 decreased most of them with the exception of P.
corylophilum and P. pagulum (Fig. 4C). These findings highlight
that delivery method can greatly affect how LX3 interacts with
native fungal communities.
LX3 modulates expression of host immune factors with
microbiota-shaping properties
Since innate immune signaling and gut microbiota composition
are intimately linked in honey bees [12], we postulated that LX3
(possessing established immunomodulatory capacities [20,27])
could have elicited the observed microbiota changes via host-
mediated selective pressures on microbial communities. To assess
this possibility, we measured gene expression of five well-
characterized antimicrobial peptides (AMPs) and three oxidative
response enzymes (Fig. 5and Supplementary Data 1O).
The expression pattern of apidacein-1 was unique among AMP
genes in showing a progressive downregulation in control groups
from W8 onward until W24. Notably, the P +LX3 and S +LX3
groups showed significant downregulation of apidacein-1 starting
at the earlier timepoint of W2, relative to vehicle controls (Fig. 5C).
Other important AMP genes such as hymenoptacein,apismin,
defensin-1, and abaecin exhibited less consistent trends over time,
although hymenoptacein and defensin-1 were transiently upregu-
lated in P +LX3 and S +LX3 groups at earlier timepoints (W2 and
W4) during the active supplementation period. The latter results
are consistent with previous observations from a shorter 4-week
supplementation study [20]. Considering oxidative response
genes, catalase paralleled the expression patterns of apidaecin
with similar, but less pronounced time- and treatment-dependent
downregulation (Fig. 5C). To a lesser extent, superoxide dismutase
(SOD) was also downregulated over time but showed no apparent
differences between treatment groups (Fig. 5C). Phenol-oxidase
(PO) expression (traditionally associated with the melanization-
immune response and pathogen encapsulation [56]) showed
distinct expression patterns compared to all other genes
measured and was differentially regulated between LX3 treatment
groups at multiple timepoints between W2 and W24 (Fig. 5C).
Furthermore, gene expression of dissected heads (proxy for social
immunity [57]) were similar hindguts (individual immunity) for
both timepoint- and treatment-specific patterns, demonstrating
the observed effects were systemic rather than tissue-specific
(Supplementary Fig. 7G).
Host immune response to LX3 treatment is associated with
altered microbial network dynamics
To assess how LX3-induced immune effects may have played a
role in the observed microbiota shifts (Fig. 3), we used NetCoMi
[58] to construct an interkingdom (bacterial-fungal) co-occurrence
network (Supplementary Fig. 7A–D and Supplementary Data 1P)
and then analyzed correlations with hindgut tissue gene
expression using a dual RNA/DNA extraction approach (Fig. 5).
Findings elucidated distinct clustering patterns that largely
corresponded with phylogenetic relatedness at the phylum-level,
although several exceptions were apparent. Bacillota and Actino-
mycetota clustered together including the bona fide symbionts
Bombilactobacillus,Lactobacillus (exclusively Firm-5 phylotypes),
and Bifidobacterium spp., all of which were associated with
reduced gene expression for most AMPs (especially apidaecin) but
increased expression of PO (Fig. 5B)—a pattern consistent with the
enrichment of these taxa in LX3 treatment groups (Fig. 4A, B).
Nearly identical trends in host immune-microbiota associations
were seen for a single Pseudomonadota symbiont, Bartonella apis,
which uniquely clustered with the aforementioned taxa from
different phyla (Fig. 5B). Exact opposite immune association
patterns were seen for other Pseudomonadota members (e.g.,
Gilliamella apis,Gilliamella apicola,Frischella perrara, and Snod-
grassella alvi), which supports the in vitro findings that purified
apidaecin and hymenoptaecin derived from honey bee gut tissue
can selectively inhibit the growth of these bacteria [5]. Environ-
mental opportunists and host-adapted pathogens that were
reduced by LX3 treatment (e.g., M. plutonius,A. apis,Kodamaea
spp.) also clustered predominantly with Pseudomonadota (Fig. 5B).
The cumulative findings show that transient immune responses to
LX3 treatment differed slightly based on delivery method but
were overall associated with long-term beneficial effects on
microbiota composition. It is important to highlight that these
microbial relationships do not necessarily indicate a causative
immune response by the host, but rather support the notion that
reciprocal eco-evolutionary dynamics may drive the formation of
distinct microbiota configurations in bees.
DISCUSSION
This study demonstrated a range of health benefits associated
with LX3 supplementation and further identified that some of
these effects were strictly dependent on the way in which the
strains were delivered to the hive. Superior efficacy in reducing
fungal pathogens was a notable advantage of the spray formula,
whereas the patty formula performed considerably better in terms
of increasing capped brood and overall colony growth throughout
the summer season. While the spray-specific effects could be
attributed to physical contact differences in the hive upon
administration, our findings indicate that the patty-specific effects
were driven by nutritional enhancements of the base pollen patty
ingredients via LX3 fermentation, which is a process that aligns
with how bees naturally ferment pollen into beebread for
improved digestibility [59]. These findings directly expand on
past work showing that patty-based LX3 supplementation can
rescue antibiotic-induced brood deficits [20] likely originating
from impaired protein digestion related to gut microbiota damage
[60,61]. It should be noted, however, that spray-based delivery of
LX3 still increased capped brood and colony size beyond baseline
levels observed in the control groups (Fig. 1), thus suggesting that
LX3-mediated immune and microbiota effects alone can con-
tribute to beneficial outcomes at the colony-level.
There were no obvious safety concerns identified. Spray and
patty delivery methods led to detectable increases of the
LX3 strains in honey bee hindguts following treatment at W4,
and these levels returned to baseline by W24 (Supplementary
Fig. 4). This highlights that both delivery methods were efficacious
for their intended purposes of supplementing viable bacteria to
the hive and that colonization was not required for host benefits
to be derived. Notably, probiotics frequently do not colonize the
host and immune stimulation by allochthonous strains is often
effective than autochthonous strains in promoting disease
resistance [62]. Moreover, while both LX3 treatment groups
experienced long-lasting changes in microbiota composition,
there was no evidence to support displacement of core gut
symbionts or any other lasting negative impacts (Figs. 3and 4).
Beyond evaluating the treatment groups of interest, this study
facilitated several discoveries relating to temporal immune-
microbiota dynamics and cross-kingdom microbial interactions
in the honey bee hindgut.
Hindgut bacterial communities play a crucial role in regulating
honey bee immunity [12] and microbiota dysbiosis (often
ambiguously defined, but with the simplest criteria being a
relative depletion of symbiotic gut bacteria) can increase host
susceptibility to a broad range of diseases [63,64]. Here, we show
that in California over the summer months (following almond
harvest) there was a seasonal trend toward improved microbiota
composition (Fig. 3). This was primarily characterized by an
increased absolute abundance of core Bombilactobacillus,Lacto-
bacillus, and Bifidobacterium spp.—all of which are carbohydrate
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utilization specialists [9,65,66] widely associated with healthy
colony outcomes [52,67]. Uniquely, Bart. apis (highly prevalent
Alphaproteobacteria member in honey bees) was also increased
alongside these taxa which may be related to syntrophic
partnerships as seen in other insect-adapted symbionts [68].
Kešnerová et al. [69] observed highly similar trends repeatedly in a
2-year longitudinal study in Switzerland, which highlights the
overall reproducibility of the current findings on a global-scale.
Adding to this knowledge, we show there are clear seasonal
trends in immune gene expression that closely overlap with
Fig. 5 Host immune-microbiota dynamics are influenced by LX3 treatment. A Schematic overview of experimental procedure used for
obtaining DNA/RNA from matched samples and subsequent analysis of microbial communities and host immune gene expression.
BCorrelation matrix between immune gene expression (determined via qPCR) and species-level microbial abundances in hindgut samples, as
determined via the “aldex.corr”function of the ALDEx2 package in R (r=Spearman’s Rho, we.ep =BH-adjusted pvalues). Hierarchal clustering
of samples is shown in dendrogram above and was calculated using the ward.D2 method via the “hclust”function in R. Euc dist =Euclidean
distance. CHeatmap of immune gene expression with patty and spray group-wise comparisons of interest indicated on the right. Data
represent Log2-transformed relative gene expression values (2
−ΔΔCt
method) normalized to W0 baseline for each group. Statistics shown for
LX3 treatment groups (P +LX3 and S +LX3) are based on comparisons to vehicle controls (P and S, respectively) via three-way ANOVA with
BH-adjusted multiple comparisons (n=468 total). •p< 0.1, *p< 0.05, **p< 0.01, ***p< 0.001, ****p< 0.0001.
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microbiota shifts, and that LX3 treatment can modulate immune-
microbiota dynamics in a way that supports further enrichment of
the aforementioned symbionts (Figs. 4and 5). While the under-
lying drivers of this biological phenomenon remain unclear,
cumulative cross-continental evidence indicates a mechanism
distinct from spatially-dependent environmental factors (at least
within temperate climates [70,71]) known to impact honey bee
microbiota composition, such as differences in forage type [72]
and pesticide exposure [73].
Compared with bacteria, little is known about the temporal
dynamics of fungi in honey bees. Our findings support the notion
that most fungi are transient colonizers derived from the
environment [74]. However, we also identified clear seasonal
trends in fungal taxa and a distinct set of highly prevalent species
(found in >80% samples) which suggests the existence of a “core
mycobiome”(Supplementary Data 1C). One unclassified species in
particular, fSV-0 (predicted member of Myxotrichaceae—a fungal
family commonly reported in bees across multi-continental
studies [75–78]), warrants immediate investigation on the basis
it was found in 100% of samples (generally dominant by
abundance) and could conceivably have a major impact on
honey bee health. Notably, almost all fungal taxa (including fSV-0)
showed a strong negative correlation with Bombilactobacillus,
Lactobacillus, and Bifidobacterium spp. (Supplementary Fig. 7A–D).
These findings are consistent with reported inhibitory effects of
lactic acid- and acetic acid-producing bacteria on diverse fungi
[15,79,80], which together could also explain the broad anti-
fungal effects seen in LX3-supplemented groups (Fig. 4).
An illuminating discovery relating to hive disease dynamics was
that spray-based LX3 far outperformed patty-based LX3 in
reducing the two major brood pathogens, M. plutonius and A.
apis (Fig. 4). Bacteriocin production of Kunkecin A by honey bee-
derived Api. kunkeei BR-1 (present in LX3), as has been shown for
other Api. kunkeei strains [81], is a plausible mechanism supporting
activity against M. plutonius but requires further validation. In
contrast, the intrinsic ability of LX3 strains to produce lactic acid
likely explains the strong activity against A. apis [80]. Nonetheless,
localized spray delivery and coating of brood cells (i.e., where M.
plutonius and A. apis are most abundant) appears to be the most
crucial factor influencing overall efficacy of LX3 strains against
these pathogens. This point also extends to opportunistic fungi
colonizing inner hive surfaces, such as Kodamaea spp., which
through producing high levels of isopentyl acetate (a major
component of honey bees’alarm pheromone [82]) induce chronic
stress and (in the case of K. ohmeri) can attract small hive beetle
(Aethina tumida) and other parasites [83]. Our findings show that
K. ohmeri as well as an unclassified Kodamaea sp. (fSV-3) were
dramatically reduced in response to spray-based LX3 (Fig. 4)—an
effect worthy of future study given the conceivable benefits on
hive-level energetic burden. Moreover, both pollen- and spray-
based LX3 approaches demonstrated distinct activities against
Alternaria alternata (fungal agent of leaf spot in 380 plant species
[84]), Fusarium longifundum (fungal agent of canker disease in
almond crops [85]), and several other opportunistic pathogens
(Fig. 4and Supplementary Data 1M, N) for which honey bees are
recognized vectors [74]. Alongside the emerging realization that
multi-host epidemics are especially common within plant-
pollinator networks [86], these results cumulatively suggest that
LX3-mediated inhibition of plant and insect pathogens could
greatly benefit wild bees and aid in crop pest management.
Linking the ideas discussed so far with honey bee immunobiol-
ogy and nutrition, we identified clear phylogenetic clustering of
bacterial and fungal taxa based on correlations with host expression
of AMPs (innate immune effectors closely linked with microbiota
composition [87]aswellasproteinintake[88]) and oxidative stress-
response enzymes (Fig. 5). The strongest signal was for apidaecin-1
(encoding a proline-rich AMP targeting Gram-negative bacteria),
which showed drastic downregulation over time corresponding
most evidently with a decline in Pseudomonadota as well as select
Bacillota (e.g., L. panisapium and L. apis). These results are
corroborated by experimental evidence showing selective toxicity
of apidaecin-1 isoforms against most Pseudomonadota (including G.
apicola,S. alvi,F. perrara, and environmental Escherichia coli)[5]and
that some (but not all) L. apis strains possess unique S-layer proteins
that activate Toll signaling-mediated apidaecin-1 expression [89]. A
noteworthy point to highlight is that LX3 supplementation
accelerated the seasonal downregulation of apidaecin-1 (in
hindguts and also hypopharyngeal tissue in head samples,
demonstrating a systemic effect) in a delivery-independent manner
(i.e., nutrient factors likely not involved), while also transiently
upregulating apisimin,hymenoptacein,anddefensin-1 involved in
bacterial and fungal pathogen resistance (Fig. 5). The latter effects
are highly consistent with past work [20]demonstratingLX3can
broadly upregulate insect AMPs (via peptidoglycan recognition
protein-mediated activation of the IMD pathway), whereas the
contrary findings for apidaecin-1 may be indirectly related to
destabilized microbial networks stemming from antibiotic exposure
in the previous study.
In terms of non-AMP immune genes, phenol-oxidase showed a
strong seasonal upregulation in hindguts (but not head samples;
Supplementary Fig. 7) and could potentially be related to changes
in fungal-derived β-glucan which are known to cause a dose-
dependent increase in phenol-oxidase expression [90]—an effect
also associated with improved survival to DWV [91]. On that note,
no major differences in DWV-immune dynamics were found in the
current study (Fig. 2B). Although, relatedly, Varroa mite infestation
rates showed subtle decreases following LX3 treatment with 7/7
hives responding to patty delivery and 5/6 hives responding spray
delivery (Fig. 2A). Similar findings were observed during a recent
2-year study in Argentina showing a 50–80% reduction in mite
loads via Lactobacillus salivarius A3iob supplementation [47],
however, the mechanism for such effects remains elusive.
Connecting the insect gut-brain axis [92], neuro-immune interac-
tions [93], and how beneficial bacteria influence hygienic
behaviors represent promising future directions.
In summary, this study has: (1) introduced a novel spray-based
delivery method for supplementing beneficial lactobacilli to honey
bees, (2) demonstrated that LX3-induced immune and microbiota
effects can influence multiple pathogen loads, brood development,
and overall colony size, and (3) advanced our understanding of
seasonal microbiota variation with regard to bacterial-fungal
interactions in the hindgut over time. Given the non-overlapping
benefits associated with patty- and spray-based LX3 treatments, a
combined approach is advisable in future studies. Altogether, the
findings emphasize the importance of considering delivery method
in probiotic-focused disease management strategies. Application of
these approaches to address unsustainable colony loss in the
beekeeping industry warrants urgent consideration.
DATA AVAILABILITY
All underlying data from the figures in this study have been made available in a
multi-tabbed excel document with self-contained legends describing each of the
tabs (Supplementary Data 1). Sequencing data for 16S rRNA and ITS datasets have
been uploaded to the NCBI Sequence Read Archive (SRA) database under accession
identifier PRJNA856263 and PRJNA856341, respectively. Code and command line
input settings used for data interpretation, statistical analysis, and figure generation is
available at https://github.com/bdaisley/LX3CA1.
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ACKNOWLEDGEMENTS
This work was supported through a W. Garfield Weston Foundation of Canada grant, a
Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant
(RGPIN-2020–05647), and NSERC Postdoctoral Fellowship Award (PDF-558010–2021).
AUTHOR CONTRIBUTIONS
BAD, APP, GR, and EN conceived the study design. CT, RL, BN, and EN managed the
experimental hives, administered treatments, and collected honey bee samples. BAD
performed dissections, RNA/DNA extractions, qPCR-based quantification of microbial
loads, and analyzed the majority of data for all experiments. BAA and GJT enumerated
capped brood from hive frame photos. APP and GJT provided scientificinputon
biology of honey bee colonies. KFA, JPB, and EAV provided resources for 16S rRNA
gene and ITS sequencing experiments. KFA assisted with interpretation of microbiota
data and figure design. BAD drafted the manuscript and all authors helped to revise it.
COMPETING INTERESTS
GR is a consultant for Seed that sells probiotics not mentioned in this article.
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available at https://doi.org/10.1038/s41396-023-01422-z.
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© The Author(s) 2023
B.A. Daisley et al.
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