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

Abstract

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.

Full text

ARTICLE
Lactobacillus spp. attenuate antibiotic-induced
immune and microbiota dysregulation in honey
bees
Brendan A. Daisley 1,2, Andrew P. Pitek3, John A. Chmiel 1,2, Shaeley Gibbons1, Anna M. Chernyshova3,
Kait F. Al 1,2, Kyrillos M. Faragalla3, Jeremy P. Burton 1,2,4, Graham J. Thompson 3& Gregor Reid 1,2,4✉
Widespread antibiotic usage in apiculture contributes substantially to the global dis-
semination 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.
https://doi.org/10.1038/s42003-020-01259-8 OPEN
1Centre for Human Microbiome and Probiotic Research, Lawson Health Research Institute, London, ON, Canada. 2Department of Microbiology and
Immunology, The University of Western Ontario, London, ON, Canada. 3Department of Biology, The University of Western Ontario, London, ON, Canada.
4Department of Surgery, The University of Western Ontario, London, ON, Canada. ✉email: gregor@uwo.ca
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Pathogens are considered one of the largest contributing
factors in the global decline of honey bees (Apis mellifera)
and other wild pollinators, which together help support
agricultural economies and international food supplies1–3. Man-
aged honey bees represent a substantial fraction of total pollina-
tors and are an important reservoir of enzootic pathogens that
can affect disease epidemiology in various animal communities4.
To address this issue, beekeepers frequently administer antibiotics
to their hives in an attempt to prevent or reduce disease occur-
rence and control intraspecies pathogen transmission between
nearby apiaries.
Oxytetracycline (OTC), tylosin, and fumagillin are the most
commonly used antibiotics in apiculture5. Tetracycline-based
agents are of particular concern given their extensive usage in
human medicine and as bacteriostatic feed additives in livestock.
Though indispensable under certain circumstances, overuse of
tetracyclines pollutes the environment and inadvertently leads to
the accumulation of antibiotic-resistance genes in many bacteria,
including a multitude of pathogens relevant to human and honey
bee health6. Antibiotic exposure can also negatively impact key
symbiotic bacteria within the microbiota (community of micro-
organisms residing on or within a multicellular organism) of
honey bees7. This compromises overall health status in hives as
the distinct microbiota in healthy bees is crucial to host metabolic
competency, immune regulation, growth and development, and
resistance towards parasites and pathogens8.
In many parts of the world, prophylactic usage of OTC is
recommended under best management practices for beekeepers
in the prevention of foulbrood diseases caused by Paenibacillus
larvae and Melissococcus plutonius9. In the case of P. larvae,
which causes American foulbrood (AFB), there have been reports
of widespread tetracycline resistance occurring since the early
2000’s10. Identical sequence homology of tetracycline-resistance
loci found in P. larvae and core members of the honey bee
microbiota suggests that there is horizontal gene transfer between
commensals and pathogens via mobile genetic elements such as
transposons and plasmids11. Importantly, despite susceptibility or
resistance, OTC is unable to treat P. larvae spores in the comb,
which can act as a source of reinfection. To address these issues
and guard against the potentially rapid evolution of resistance, it
is prudent to test alternative control measures, which so far
include hygienic breeding12, use of bioactive essential oils13,
bacteriophage therapy14, synthetic indoles with anti-germination
properties15, and supplementation of hives with lactic acid bac-
teria16–18. The promise of this latter approach is up-held by
studies demonstrating that lactobacilli can promote insect innate
immunity and detoxification19–21, extend longevity of adult
honey bees22, and stimulate queen brood production23,24.
In our previous work, we demonstrated that pollen patty hive
supplements infused with three select strains of lactobacilli
(Lactobacillus rhamnosus GR-1, Lactobacillus plantarum Lp39,
and Lactobacillus kunkeei BR-1; LX3) could reduce P. larvae loads
in honey bee hives experiencing active AFB outbreak and
improve honey bee survival towards P. larvae infection in vitro25.
Here, we evaluated OTC treatment efficacy in subclinically
infected hives and characterized how routine exposure to this
common antibiotic impacts immune and microbiota dynamics in
honey bees. In addition, we evaluated how treatment augmenta-
tion with LX3 following OTC exposure influences antibiotic
recovery rates as evaluated by immune functionality, microbial
homeostasis, and hive productivity.
Results
LX3 enhances larval pathogen eradication by antibiotics. Pro-
phylactic administration of OTC to honey bees is a common
practice in beekeeping for the prevention of AFB. To evaluate the
efficacy of this long-standing apiculture management strategy, we
monitored a 2-week treatment regimen with OTC under natural
field conditions in honey bee hives experiencing low-grade
chronic infection with P. larvae (Fig. 1a). Using a qPCR-based
approach to enumerate pathogen load, P. larvae abundance was
found to be significantly lower in honey bee larvae (primary
target of AFB) at week 1 and week 2 of OTC treatment
(Kruskal–Wallis with Dunn’s multiple comparisons, P=0.0071
and P=0.0005, respectively) compared to baseline levels at day 0
(Fig. 1b). In contrast, no observable differences in P. larvae
abundance were found in adult honey bees (active vector of AFB)
at any time point during this treatment (Kruskal–Wallis with
Dunn’s multiple comparisons, P=0.9999, P=0.6367, respec-
tively; Fig. 1c).
A robust body of scientific evidence shows that supplementa-
tion with probiotic Lactobacillus spp. can augment the effects of
certain antibiotics and attenuate antibiotic-induced dysbiosis in
humans and other animals26–29. Testing this in honey bees, it was
found that larval samples from LX3-treated hives exhibited
significantly lower levels of P. larvae at week 2 and week 4
compared to both no treatment control (NTC; P< 0.0001 for
both) and vehicle-treated (P=0.0011 and P=0.0014) hives,
respectively (two-way analysis of variance [ANOVA] with Sidak’s
multiple comparisons; Fig. 1d). The NTC group also demon-
strated a trend towards higher P. larvae loads in larval samples
compared to the vehicle group at week 4 (two-way ANOVA with
Sidak’s multiple comparisons, P=0.0568). Similar results were
observed in adults with samples from LX3-supplemented hives
demonstrating a significantly lower P. larvae load compared to
both the NTC group (P=0.0002 and P=0.0003) and vehicle-
treated group (P< 0.0001 for both) at 2 and 4 weeks, respectively
(two-way ANOVA with Sidak’s multiple comparisons; Fig. 1e).
To compare the effects of OTC and LX3 treatments on overall
hive health, the coverage of capped brood on hive frames (an
established metric for assessing colony strength and reproduc-
tion30) was measured weekly during experimentation. No
observable differences were found in capped brood counts
following 1 week of OTC treatment, whereas a significant
reduction was found after 2 weeks (one-way ANOVA with
Sidak’s multiple comparisons, P< 0.9999 and P=0.0041, respec-
tively) compared to pretreatment baseline measurements (Fig. 1f).
In contrast, capped brood counts were significantly higher in LX3-
treated hives at week 3 and 4 of the supplementation period (two-
way ANOVA with Sidak’s multiple comparisons, P=0.0071 and
P=0.0055, respectively), while no differences were found in NTC
and vehicle-treated hives at comparable time points (Fig. 1g).
Antibiotics reduce key immune regulators in the adult gut
microbiota. Given the broad-spectrum activity of tetracyclines, we
evaluated how OTC exposure might influence the symbiotic bac-
terial communities associated with honey bees. Total bacterial
loads, as determined by qPCR-based molecular quantification, were
significantly reduced in adult bees following 1 week of OTC
treatment (P< 0.0001) and in larvae (P=0.0421) after 2 weeks of
treatment (Kruskal–Wallis tests with Dunn’s multiple comparisons;
Supplementary Fig. 2). The nurse-aged adult bees sampled at the
experimental start point (pre-OTC exposure) and on the final day
of OTC treatment (post-OTC exposure) were chosen for 16S rRNA
gene sequencing-based microbiota analysis due to their close
physical proximity with brood, passive carriage of P. larvae,and
well-balanced representation of overall hive microbial diversity31.
Bar plots shown in Fig. 2a, b visually represent the relative
proportion and absolute abundance (adjusted according to
qPCR-based quantification of total bacteria) of taxa in samples
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from pre- and post-OTC exposure, respectively. Adult gut
samples collected post-OTC exposure revealed a significant
reduction in a single amplicon sequence variant (SV) of Frischella
perrera (SV19; Wilcoxon test with Benjamini–Hochberg [BH]
multiple comparisons, P=0.0043) and three unique SVs of
Lactobacillus Firm-5 (SV01, SV08, and SV10; Wilcoxon tests with
BH multiple comparisons, P=0.0151, P=0.0295, and P=
0.04217, respectively) compared to samples collected pre-OTC
exposure (absolute effect >0.5; Fig. 2c). SV74 (Lactobacillus Firm-
4), SV63 (Bartonella apis), and SV54 (Lactobacillus Firm-4)
showed a trend towards decreased relative abundance following
OTC exposure (absolute effect <0.5; Fig. 2c).
A.0 A.1 A.2
0
100
200
300
400
OTC treatment timepoint
Relati ve capped
brood / frame (%)
Relati ve capped
brood / frame (%)
A.0 A.1 A.2
2
4
6
OTC treatment timepoint
2 weeks anbiocs 4 weeks supplementaon
P. larvae load in larvae
(Log10 16S copy number)
4.S2.S0.S
2
3
4
5
Supplementation timepoint
4.S2.S0.S
2
4
6
Supplementation timepoint
NTC VEH LX3
B)
Pollen pay + Vehicle (VEH)
Pollen pay + LX3 (LX3)
D)
A)
Standard hive treatment
with oxytetracycline (OTC)
No treatment control (NTC)
p=0.057
ns ns
ns ns **
**** **
****
ns
ns ns
ns ns ****
*** ****
***
** ns
***
ns
ns
ns
ns
**
ns
E)C)
G)F)
**
ns
4.S2.S0.S2.A1.A0.A
**
ns
Fig. 1 LX3 enhances larval pathogen eradication by antibiotics. Experimental hives were subjected to standard antibiotic treatment with oxytetracycline
(OTC) for 2 weeks and then supplemented for 4 weeks with either pollen patties containing LX3 (LX3) or pollen patties containing vehicle (VEH). No
treatment control (NTC) hives received no further treatment after OTC. aSchematic diagram outlining the experimental design. b,cMolecular-based
quantification of P. larvae in honey bee larvae (whole body) and adults (dissected abdomen) collected just prior to the start of OTC exposure (A.0), and
then after 1 (A.1) and 2 (A.2) weeks of exposure. Data are depicted as median ± 95% confidence intervals (Kruskal–Wallis with Dunn’s multiple
comparisons) at different time points. Each data point represents either one individual (adults) or three pooled individuals (larvae) sampled equally from a
total of n=6 hives. d,eMolecular-based quantification of P. larvae in larvae (whole body) and adults (dissected whole abdomens) at the start of the
supplementation period (S.0; corresponding to 3 days post A.2 time point), and then after 2 (S.2) and 4 (S.4) weeks. Data are depicted as mean ± standard
deviation (two-way ANOVA with Sidak’s multiple comparisons) at different time points with each data point representing either one individual (adults) or
three pooled individuals (larvae) sampled equally from n=4 hives per treatment group. f,gCapped brood counts during OTC treatment (n=6 hives) and
subsequent supplementation period (n=4 hives per treatment group). Data represents the median (line in box), IQR (box), and minimum/maximum
(whiskers) of relative change in brood counts normalized by hive. Statistics shown for one-way and two-way ANOVA, respectively, with Sidak’s multiple
comparisons for both. **P< 0.01, ***P< 0.001, ****P< 0.0001, ns not significant.
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The compositional 16S rRNA gene sequencing dataset also
demonstrated differences in alpha diversity (intraindividual) and
beta diversity (interindividual) metrics of the adult gut microbiota
following OTC exposure (Fig. 2d, e). Specifically, Shannon’sH
index (balanced alpha diversity metric taking species abundance
and evenness into account) was determined to be significantly
lower following 2 weeks of OTC treatment (Wilcoxon test, P=
0.0273; Fig. 2d). These findings also corresponded with a decrease
in microbiota stability as demonstrated by a significant increase
in Aitchison distance (i.e. Euclidean distance after center log-ratio
transforming SV read counts) between adults within the same
treatment group (Wilcoxon test, P< 0.0001; Fig. 2e).
Next, to determine how the standard practice of treating hives
with OTC may influence accumulation of antibiotic-resistance
genes in the honey bee gut microbiota, we screened seven
tetracycline-resistance loci that have been repeatedly detected in
honey bee guts6. These loci included five tetracycline efflux pump
genes (tetB,tetC,tetD,tetH,andtetY)andtworibosomal
protection protein-encoding genes (tetM and tetW). The abun-
dance of tetB was significantly higher (two-tailed ttest,P=0.0092)
Pre-OTC
Post-OTC
2
4
6
8
tetM
ns
Pre-OTC
Post-OTC
15
25
35
45
Aitchison Distance
****
Pre-OTC
Post-OTC
2
3
4
5
Shannon'sHInd
ex
*
Pre-OTC
Post-OTC
7
8
9
tetB
log
10
genec
opies/adult
**
Beta diversityAlpha diversity
-0.5 0.0 0.5
Frischella perrera
Median Effect Size (log2)
Lactobacillus Firm-5
A) B)
C)
F)
0.0
0.2
0.4
0.6
0.8
1.0
16S rRNA fraction
0.0
2.0x108
4.0x108
6.0x108
8.0x108
1.0x108
1.2x109
16S rRNA gene copies
Lactobacillus kunkeei
Pseudomonas spp.
Lactococcus spp.
Rosenbergiella spp.
Pediococus spp.
Remainder
Weissella spp.
Bartonella apis
Franconibacter spp.
Bacteroides spp.
Escherichia spp.
Lactobacillus spp.
Enterococcus spp.
Bifidobacterium spp.
Klebsiella spp.
Streptococcus spp.
Commensalibacter spp.
Lactobacillus Firm-4
Frischella perrara
Lactobacillus Firm-5
Snodgrassella aliva
Gilliamella apicola
Relave abundance Absolute abundance
Pre-OTC
Post-OTC
3
5
7
9
tetC
ns
Pre-OTC
Post-OTC
2
4
6
8
tetD
ns
Pre-OTC
Post-OTC
4
5
6
7
tetW
ns
p=0.09
Pre-OTC
Post-OTC
2
4
6
8
tetY
Pre-OTC
Post-OTC
2
4
6
8
tetH
ns
D) E)
Decreased post-OTC |Increased post-OTC
Acnobacteria
Alphaproteobacteria
Bacilli
Bacteroidia
Campylobacteria
Clostridia
Fusobacteriia
Gammaproteobacteria
Negavicutes
Verrucomicrobiae
Post-OTC Pre-OTC Post-OTC Pre-OTC
112233445566
ABABABABABAB
112233445566
ABABABABABAB
112233445566
ABABABABABAB
112233445566
ABABABABABAB
Hive:
Rep:
Hive:
Rep:
Tetracycline efflux pump genes Ribosomal protecon genes
SV01 SV08 SV10
SV19
Fig. 2 Antibiotics reduce key immune regulators in the adult gut microbiota. The gut microbiota of adult honey bees was analyzed before (Pre-OTC) and
after (Post-OTC) hive administration with oxytetracycline. a,bBar plots illustrating the relative and absolute abundance of bacterial species in the gut
microbiota of adult honey bee samples as determined by V4 region 16S rRNA gene sequencing. Each bar represents a pooled sample of three adult guts
collected from n=6 hives, with two replicates performed for each hive. Absolute abundance of bacterial taxa was estimated by quantifying total 16S rRNA
gene copy number via qPCR. cStrip chart showing differentially abundant taxa in the gut microbiota of OTC-exposed adult honey bees. Positive values
indicate an increased relative abundance in response to OTC and negative values indicate a decreased relative abundance. Statistical inference was
performed on centered log-ratio transformed read counts of sequence variants using ALDEx2 software in R. Features exceeding absolute effect size (>0.5)
and Pvalue (<0.05) thresholds are shown as red. d,eAlpha diversity (measured via Shannon’s H Index) and Beta diversity (measured via Aitchison’s
distance between samples at different time points) of adult gut microbiota samples. Data represents the median (line in box), IQR (box), and minimum/
maximum (whiskers) of respective microbiota diversity metrics with statistical comparisons shown for separate Wilcoxon tests. fAbundance of seven
tetracycline-resistance loci in adult gut samples relative to the total number of 16S rRNA gene copies present. **P< 0.01 and ****P< 0.0001.
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whereas tetY showed a trend towards increased abundance (two-
tailed Mann–Whitney test, P=0.0887) in post-OTC adult gut
samples (two-tailed Mann–Whitney tests; Fig. 2f). No observable
change in abundance were found for any of the remaining five
tetracycline-resistance genes examined in this study (Fig. 2f). A
positive association was identified between total Gammaproteo-
bacteria (Pearson correlation, r=0.888, P=9.25 × 10−17)and
Gilliamella apicola (Pearson correlation, r=0.719, P=1.23 ×
10−08), but not Frischella perrera (Pearson correlation, r=−0.228,
P=0.124), and the presence of tetB (Supplementary Fig. 3).
LX3 improves adult microbiota recovery post-OTC exposure.
In humans, probiotic therapy helps to encourage healthy remo-
deling of the microbiota and can improve recovery following
antibiotics32. Here, we tested the ability of LX3 supplementation
in honey bees to reduce P. larvae levels and restore microbiota
homeostasis in adults and larvae following OTC exposure. As
expected, principal component analysis showed clear separation
between the microbiota composition of adult and larval samples
(Fig. 3a). The largest influencers of separation that were positively
associated with adult samples included core microbiota members
such as G. apicola,Snodgrassella alvi,F. perrara,Commensali-
bacter,Lactobacillus Firm-4, and Lactobacillus Firm-5. In con-
trast, larval samples showed a positive association with mostly
opportunistic bacteria including Escherichia/Shigella,Staphylo-
coccus,Enterococcus,Pseudomonas, and P. larvae (Fig. 3a).
Using ALDEx233 software, Bland–Altmann-like effect plots
were generated to investigate the relationship between differences
in relative abundance for each SV between treatment groups and
the within-group variance of each SV. While no discernible
differences were observed within larval samples with an under-
developed microbiota composition, a tightly clustered group of 13
SVs with low variance were identified in adult samples (Fig. 3b,
c). Notably, 12 out of 13 of these SVs trended towards increased
abundance in the microbiota of LX3-supplemented adults
(Fig. 3b) and consisted of several well-characterized honey bee
core microbiota members including five SVs of Lactobacillus
Firm-5, two SVs of S. alvi, two SVs of G. apicola, one SV of
Lactobacillus Firm-4, one SV of F. perrara, and one SV of
Bifidobacterium (see Supplementary Data 1 for full list of SVs).
Corroborating these results, LX3 supplementation demonstrated
a rescuing effect on microbiota stability as demonstrated by a
significant decrease in compositional differences between adult
microbiota samples (measured via Aitchison distance; P=
0.0012), whereas no changes were observed in vehicle-treated
adults (Kruskal–Wallis test with BH multiple comparisons, P=
0.7570; Fig. 3e).
No differences in overall alpha diversity, as determined by
Shannon’s H Index (accounting for species abundance and
evenness), were detectable in either vehicle or LX3 treatment
groups (Kruskal–Wallis test with BH multiple comparisons, P=
0.2985 and P=0.2348, respectively; Fig. 3d). However, further
investigation demonstrated that species richness alone (deter-
mined using the abundance-based coverage estimator [ACE]
algorithm in QIIME2) was significantly lower (P=0.0428) and
that compositional dominance (or unevenness, as determined by
Strong’s Dw Index) trended towards being higher (P=0.0535) in
LX3-treated adults compared to vehicle-treated adults
(Kruskal–Wallis test with BH multiple comparisons; Fig. 3f, g).
Since these data did not provide conclusive evidence as to
whether core SVs (dominant microbiota members) or noncore
SVs (rare species and transient opportunists) were responsible for
the observed differences in diversity indices, a nested composi-
tional analysis was performed on total relative abundance for
each group. LX3-treated adults demonstrated a significant
enrichment in core SVs (effect size =1.3631) and a reduction
in noncore SVs (effect size =−1.3860; Kruskal–Wallis test with
BH multiple comparisons, P=0.0225; Fig. 3h). In contrast, no
change was seen in the total relative abundance of core SVs (effect
size =0.1579) or noncore SVs (effect size =−0.1654) in the
vehicle-treated controls (Kruskal–Wallis test with BH multiple
comparisons, P=0.2046; Fig. 3i).
LX3 strains are detectable in-hive members post-supplementation.
Since 16S rRNA gene sequencing is unable to resolve bacterial
taxonomy at the species level, we performed qPCR-based quan-
tification of Lp39, GR-1, and BR-1 using established primer sets25
to confirm that LX3 strains were being effectively dispersed
throughout the hive as intended. LX3-supplemented adults and
larvae were found to contain significantly higher levels of L.
plantarum (multiple ttests, P=0.0087 and P=0.0035, respec-
tively) and L. rhamnosus (multiple ttests, P=0.0002 for both)
compared to vehicle-treated control groups (Fig. 4a). In addition,
evaluation of bacterial compositions in honey bee larval samples
showed that abundance of P. larvae correlated inversely with L.
plantarum (r=−0.442, P=0.006) and L. rhamnosus (r=
−0.456, P=0.006), but not with L. kunkeei (r=0.060, P=0.724,
Pearson correlations; Fig. 4b).
LX3 upregulates both head and gut immunity in adult bees.
Repeated exposure to antibiotics can weaken immune defenses in
honey bees—a phenomenon thought to be facilitated through the
reduction of bacterial species important to immunoregulation34.
Using an established zone-of-inhibition (ZOI) assay as a crude
measure of immune function35, we assessed the inhibitory
potential of honey bee-derived hemolymph against Arthrobacter
globiformis. The antimicrobial capacity of adult hemolymph was
found to be reduced by 31.27% (95% CI =8.19–54.34%, one-way
ANOVA with Sidak’s multiple comparisons, P=0.0150) follow-
ing 2 weeks of OTC treatment (Fig. 5a). In contrast, the anti-
microbial capacity of hemolymph from LX3-supplemented adults
was significantly increased by 121.30% (95% CI =22.34–220.30%,
two-way ANOVA with Sidak’s multiple comparisons, P=0.0443)
after 2 weeks in comparison to vehicle supplemented controls
(Fig. 5b).
Previous work has shown that ex situ supplementation of
LX3 to honey bee larvae can increase innate immune gene
expression of several antimicrobial peptides (AMPs) that control
susceptibility to P. larvae infection25. Here, we tested how
LX3 supplemented directly to the hive impacted adult bee
immunity. Expression of nine well-characterized innate immune-
and antioxidant-related genes (defensin-1,defensin-2,hymenop-
taecin,apismin,apidaecin,VgMC,catalase, and lysozyme-1) were
measured in adult head and gut tissue as their respective
anatomical sites are known to play a major role in social and
individual immunity36. Exploratory analysis showed that basal
gene expression levels in pre-supplemented adult bees demon-
strated a vast enrichment of defensin-1 (900.0 ± 163.6-fold higher)
and apismin (297,292 ± 63,498-fold higher) in adults heads
relative to gut expression, as compared to the other AMP genes
examined (one-way ANOVA with Sidak’s multiple comparisons,
P< 0.0001 for both; Fig. 5c)—suggesting that changes to the
expression of these genes in the head, as opposed to the gut,
might produce a more potent immune response and protective
benefit at the colony level.
During the supplementation period, head expression of
defensin-1,apismin, and apidaecin showed a significant increase
over time in only the LX3-supplemented group (two-way
ANOVAs with Sidak’s multiple comparisons, P=0.0406, P<
0.0001, and P=0.0004, respectively) whereas expression of
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Pre Post Pre Post
1
2
3
4
5
Shannon'sHI
ndex
ns ns
Pre Post Pre Post
20
25
30
35
40
45
Aitchisondistance
ns **
Pre Post Pre Post
0.5
0.6
0.7
0.8
ns
p=0.054
Bacteroides spp.
Apibacter spp.
Snodgrassella alvi
Gilliamella apicola
Bartonella apis
Staphylococcus spp.
Weissella
Lactobacillus Firm-4
Lactococcus
Bombella intestini
Paenibacillus larvae
Bifidobacterium spp.
Commensalibacter spp.
Enterococcus
Escherichia/Shigella Franconibacter
Fusobacterium
Klebsiella
Lactobacillus spp.
Pantoea
Pseudomonas
Rosenbergiella
Streptococcus
Veillonella
Pediococcus Frischella perrara
Campylobacter
-5
0
5
10
-5 0510
PC1: 23.3%
PC2: 17.5%
Lactobacillus kunkeei
Lactobacillus Firm-5
2468
0
4
8
8
4
2468
0
4
8
-8
-4
Pre Post Pre Post
-2
-1
0
1
Relative abundance
(clr-transformed)
*
ns
Pre Post Pre Post
0
1
2
Relative abundance
(clr-transformed)
*
ns
Pre Post Pre Post
15
20
25
30
35
40
ACE metric
ns
*
Fig. 3 LX3 improves adult microbiota recovery post-OTC exposure. a Principle component analysis (PCA) plot of the honey bee microbiota from adult
and larval samples before (Pre-supp) and after (Post-supp) the supplementation period. Sequence variants were collapsed at species-level identification,
with clr-transformed Aitchison distances used as input values for PCA analysis. Distance between individual samples (points) represent the difference in
microbiota composition between samples, with 40.8% of variance explained by the first two principle components shown. Strength of association for taxa
are depicted by length of corresponding arrows. b,cALDEx2 effect plots comparing differences in relative abundance of SVs between groups (ΔA) plotted
against the variance, or within-group difference, in relative abundance for each SV (Δw). Low variance SVs that cluster tightly together in adult microbiota
samples largely represent well-established core microbiota members (see Supplementary Data 1 for list of corresponding SVs). dAlpha diversity
determined by Shannon’s H Index (accounting for abundance and evenness), eBeta diversity measured via Aitchison’s distance (representing within-group
microbiota differences), fspecies dominance (or unevenness) measured via Strong’s Dw Index, and gspecies richness as determined using the abundance-
based coverage estimator (ACE) metric in QIIME2. h,iDifferential abundance analysis on adult gut samples between the relative abundance of all core
cluster SVs grouped together compared to all noncore SVs grouped together. Data represents median (line in box), IQR(box), and minimum/maximum
(whiskers) of clr-transformed relative abundances with statistical comparisons performed by Kruskal–Wallis test with Benjamini–Hochberg multiple
comparisons. *P< 0.05, **P< 0.01, ns not significant.
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hymenoptaecin increased in both LX3 and vehicle supplemented
groups (two-way ANOVAs with Sidak’s multiple comparisons,
P=0.0240 and P=0.0365, respectively; Supplementary Fig. 4A,
C–E). All experimental groups showed an increase in head
expression level of catalase (two-way ANOVA with Sidak’s
multiple comparisons, P=0.0050, P< 0.0001, and P< 0.0001,
respectively) over time with no changes in expression of defensin-
2or lysozyme-1 (Supplementary Fig. 4B, H, I). Gut expression of
defensin-1,hymenoptaecin,apidaecin, and VgMC were exclusively
increased by LX3 supplementation (two-way ANOVAs with
Sidak’s multiple comparisons, P=0.0004, P=0.0201, P< 0.0001,
and P< 0.0001, respectively), whereas lysozyme-1 and catalase
expression increased in both vehicle (P=0.0013 and P< 0.0001,
respectively) and LX3 (P< 0.0001 for both) treatment groups
(two-way ANOVA with Sidak’s multiple comparisons; Supple-
mentary Fig. 4J, M, N, P–R and Supplementary Data 2). Relative
expression of immune- and antioxidant genes in head and gut
samples are visually summarized by the PCA plots and heatmaps
in Fig. 5d–g.
Correlations between host gene expression and bacterial loads.
It has been known for decades now that probiotic bacteria can
induce an immune response in honey bees37. In addition, recent
evidence shows that host bacterial communities are selectively
regulated by the innate immune system of honey bees, and that
core microbiota members demonstrate a higher level of resistance
to host AMPs than opportunistic bacterial pathogens38. Here, we
examine the simultaneous relationship between bacterial abun-
dances and immune- and antioxidant-related gene expression by
using a dual extraction-based method to derive RNA and DNA
from adult heads and guts. Experimental end point measure-
ments at week 4 of the supplemental period demonstrated a
negative Pearson correlation between total abundance of P. larvae
and expression of apidaecin (r=−0.589, P=0.004), apismin
(r=−0.483, P=0.023), hymenoptaecin (r=−0.460, P=0.036),
defensin-1 (r=−0.599, P=0.004), and catalase (r=−0.650, P=
0.001) in head samples, and with apidaecin (r=−0.559, P=
0.007), hymenoptaecin (r=−0.467, P=0.029), and catalase (r=
−0.589, P=0.004) in gut samples (Fig. 6).
Assessment between the supplemented strains of lactobacilli
and immune or antioxidant gene expression showed a positive
relationship (Pearson correlations) between L. plantarum abun-
dance and head expression of apidaecin (r=0.653, P=0.001),
defensin-1 (r=0.526, P=0.014), and catalase (r=0.435, P=
0.043), as well as gut expression of defensin-2 (r=0.401, P=
0.008) catalase (r=0.591, P=0.004), and VgMC (r=0.468, P=
0.028; Fig. 6). For L. rhamnosus, bacterial abundance was
associated with increased head expression of apidaecin (r=
0.454,P=0.039) and a trend towards increased gut expression of
defensin-2 (r=0.401, P=0.072). No correlations were observed
between gene expression and abundance of L. kunkeei. Core
microbiota members demonstrated varying relationships with
head and gut expression of immune- and antioxidant-related
genes in adult honey bees. Total abundance of Alphaproteobac-
teria, Betaproteobacteria, Gammaproteobacteria, Bifidobacterium,
and F. perrara clustered together (based on Euclidean distance of
Pearson correlation coefficients) and were found to be mostly
associated with increased AMP gene expression (Fig. 6).
Oppositely, total abundance of Bacteroidetes, Firmicutes, and G.
apicola clustered together and were mostly associated with an
overall decrease in immune- and antioxidant-related gene
expression irrespective of body site. A notable exception to these
trends was the gut expression of apismin and defensin-1 as well as
the head expression of defensin-2 and lysozyme-1, which clustered
together (Euclidean distance matrix) based on similarities in gene
expression patterns relative to bacterial abundances (Fig. 6).
Pearson correlation coefficients (and associated statistics) for all
relationships between bacterial abundances and host gene
expression are provided in Supplementary Data 3.
Discussion
This study demonstrated that in-hive supplementation with LX3
can augment P. larvae clearance rates during chronic low-grade
infection and improve honey bee immunity following standard
antibiotic treatment. Notably, the combination of OTC treatment
followed by LX3 supplementation suppressed P. larvae more
effectively than OTC treatment alone (to nearly undetectable
levels) in honey bee brood. This combined treatment approach,
but not antibiotics alone, also demonstrated the capacity to lower
P. larvae loads in the gut microbiota of adult honey bees. Fur-
thermore, LX3 treatment helped to partially restore antibiotic-
induced deficits in-hive productivity, beta diversity of the gut
microbiota in adult nurse bees, and immune responsiveness of
adult-derived hemolymph. These findings expand on previous
work in which Lp39, GR-1, and BR-1 strains of lactobacilli were
2.0 2.5 3.0 3.5
2
4
6
P. larvae
Lactobacillus spp.
L. kunkeei
r=0.060,P= 0.724
L. plantarum
r= -0.442, P=0.006
L. rhamnosu
s
r= -0.456, P=0.006
L. plantarum
L.rh
amnosus
L. kunkeei
L. plantarum
L. rhamnosus
L. kunkeei
2
4
6
8
Log
10
copies of16SrRNA
**
ns
ns
***
ns ns
ns
** ns **
ns
ns
Fig. 4 LX3 strains are detectable in-hive members post-supplementation.
aQuantification of LX3 lactobacilli strains in honey bee adults and larvae
before and after supplementation period. Data are depicted as mean ±
standard deviation (two-tailed ttests) of bacterial abundances (determined
via qPCR with species-specific primers) at different time points with each
data point representing a single individual (n=18 adult guts per treatment
group at each time point) or a pooled sample of three larvae (n=12 pooled
samples for each treatment group at each time point). bPearson
correlation analysis between P. larvae and LX3 lactobacilli strains
(quantified via qPCR) in honey bee larval samples. VEH =Pollen patty
supplementation with vehicle, LX3 =Pollen patty supplementation with
LX3. *P< 0.05, **P< 0.01 and ****P< 0.0001.
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shown to improve survival of laboratory-reared honey bee larvae
in an acute infection model of P. larvae, as well as mitigate disease
severity during an active AFB outbreak25.
Tetracycline-derived antibiotics have been shown to reduce the
abundance39 and genetic diversity7of core bacterial species in the
gut microbiota of caged honey bee workers. Consistent with these
reports, OTC directly administered to the hive altered the
gut microbiota diversity of adult bees with significant decreases
in relative abundances of F. perrara and Lactobacillus Firm-5
post-treatment (Fig. 2). Interestingly, metagenomic sequencing
has demonstrated the genomes of Gammaproteobacteria (e.g.
F. perrara) and Firmicutes (e.g. Lactobacillus Firm-5) to be
1234
0
100
200
300
Hemolymph killing capacity(%)
012
0
100
200
300
Weeks OTC exposure
Hemolymphkilling capacity(
%)
NTC
VEH
LX3
NTC
VEH
LX3
Def-1
Def-2
Apis
Hym
Apid
Abac
VgMC
Cat
Lyso
-4
0
4
NTC
VEH
LX3
NTC
VEH
LX3
Def-1
Def-2
Apis
Hym
Apid
Abac
VgMC
Cat
Lyso
-4
-1
2
G)
Apid
Hym
Apis
Abaecein
VgMC
-6
-3
0
3
6
-4 04
PC1: 38.6%
PC2: 23%
Head
Cat
Def-2
Lyso
Def-1
Cat
Apid
Hymen
Apis
Def-1
Abaecein
VgmC
Lyso
-5
0
5
-4 04
PC1: 43.4%
PC2: 21.9%
Gut
Def-2
Pre-supp Post-supp
A) B) C)
D) E)
F)
Pre-supp Post-supp
log2
fold change
log2
fold change
NTC
VEH
LX3
NTC
VEH
LX3
Apid
Hym
Def-2
Abac
VgMC
Cat
Lyso
Def-1
Apis
10
-2
10
0
10
2
10
4
10
6
10
8
Head:Gut geneexp
ression
*****
ns
ns
*
Weeks OTC exposure Weeks post-OTC treatment
ns ns
Fig. 5 LX3 upregulates both head and gut immunity in adult bees. Adult hemolymph killing capacity against A. globiformis during aantibiotic treatment
and bsupplementation periods. Data represents the median (line in box), IQR (box), and minimum/maximum (whiskers) of hemolymph killing capacity for
n=6 hives (during antibiotic treatment) and n=4 hives per treatment group (during supplementation period), respectively. Representative measurements
for each hive at each time point were derived from a total of five randomly sampled adult nurse bees. Statistical analysis shown for one-way and two-way
ANOVAs, respectively, with Sidak’s multiple comparisons. cIntraindividual head-to-gut gene expression ratios of nine innate immune- or antioxidant-
related genes in adult honey bees. Gene expression was quantified by RT-qPCR with gut gene expression shown as relative to head gene expression. Data
shown represents the mean ± standard deviation (one-way ANOVA with Sidak’s multiple comparisons) for n=24 adults. PCA plots and heatmaps
demonstrating innate immune- or antioxidant-related gene expression in d,ehead and f,ggut samples of adult honey bees before (Pre-supp) and after
(Post-supp) the supplementation period. Log2-transformed relative gene expression estimates (determined via qPCR) were used as input values for PCA
analyses. Distance between individual samples (points) represent the difference in gene expression profiles for the nine immune or antioxidant genes
shown, with 61.6% (heads) and 65.3% (guts) of variance explained by the first two principle components. Strengths of association for each gene are
depicted by the length of corresponding arrows. Ellipses indicate 95% confidence intervals for each treatment group. NTC =no treatment control, VEH =
pollen patty supplementation with vehicle, LX3 =pollen patty supplementation with LX3. *P< 0.05, ****P< 0.0001, ns not significant. Capped brood counts
during OTC treatment (n=6 hives) and subsequent supplementation period (n=4 hives per treatment group). Data represents the median (line in box),
IQR (box), and minimum/maximum (whiskers) of relative change in brood counts normalized by hive. Statistics shown for one-way and two-way ANOVA,
respectively, with Sidak’s multiple comparisons for both. **P< 0.01, ***P< 0.001, ****P< 0.0001, ns not significant.
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specifically enriched with carbohydrate metabolism enzymes that
can degrade mannose40—a prevalent plant-derived sugar known
to be toxic to honey bees. In addition to the direct toxicity
that antibiotics exert on honey bees41, our results suggest that
antibiotic exposure may also indirectly increase mortality via
depletion of symbionts crucial for the breakdown of toxic food
components. The evaluation of LX3 supplementation in hives
following cessation of OTC treatment elucidated a partial
restorative effect on the adult gut microbiota (Fig. 3b). This effect
was structurally characterized by a relative enrichment of core
microbiota members and a reduction of transient opportunists
(Fig. 3h, i). Together, these findings validate the negative and
long-lasting implications of OTC exposure in honey bees while
also highlighting a potential role for probiotics in safeguarding
against the blooming effect of low-level pathobionts and transient
opportunists following antibiotic exposure42.
Although widespread tetracycline resistance has been identified
in honey bees6, there is lacking knowledge on how the adult gut
metagenome may facilitate pathogen persistence during in-hive
antibiotic treatment. This study demonstrated that OTC could
effectively lower P. larvae loads in honey bee brood (unstable
microbiota profile characterized by low abundance and diver-
sity43), but not adult nurses (well-characterized microbiota profile
with high abundance and diversity44) after 2 weeks of in-hive
treatment (Fig. 1b, c). Similarly to the reduced diversity of
tetracycline-resistance genes observed in OTC-exposed wax
moths (Galleria mellonella)45, the relative abundance of five out
of seven resistance genes (tetC,tetD,tetH,tetM, and tetW)in
adult nurse bees remained unchanged or was diminished by OTC,
while the relative abundance of tetB (and to a lesser extent, tetY)
was increased (Fig. 2f). These findings suggest that the adult
honey bee gut microbiota may provide P. larvae safe harborage
against antibiotic exposure—potentially through polymicrobial
synergy of multiple distinct resistance factors46 or via physical
exclusion47 within the thick stratified biofilm known to be pro-
duced by S. alvi (existing in the bottom layer directly associated
with host epithelium) and G. apicola (predominantly in the top
layer) in the hindgut of bees48. Supporting this postulation,
relative abundance of G. apicola was unaffected by OTC treat-
ment (Fig. 2a–c) yet showed a strong positive association with
tetB—an efflux pump-encoding gene which itself was shown to
increase during OTC exposure (Fig. 2f). Presence of the tetB gene
was confirmed in select G. apicola strains wkB1 and PEB01626,
though only ~18% of wild isolates are expected to possess
tetracycline-resistance genes. Considering that G. apicola strains
vary in their ability to beneficially regulate honey bee
nutrition49,50, metabolism40,51, and microbiota composition52,53,
the consequences of antibiotic selective pressures on intraindivi-
dual strain diversity merits further study.
An auxiliary repercussion of antibiotic-induced dysbiosis is
perturbation of innate immunity. Adult bees exhibited a reduc-
tion in hemolymph antimicrobial capacity over time during the
OTC treatment period (Fig. 5a), suggesting a disturbance in their
microbial regulation. These findings support past observations of
antibiotic-induced susceptibility to opportunistic infection by
Serratia marcescens39—a Gram-negative bacterial pathobiont
frequently associated with honey bees. Our current findings show
that LX3 supplementation broadly upregulated expression of
several AMPs (crucial immune effectors involved in pathogen
defense) in the heads and guts of adult honey bees (Fig. 5d–g).
Defensin-1 is critical in controlling Gram-positive bacteria54
(including P. larvae55) whereas apidaecin and hymenoptaecin are
most active against Gram-negative bacteria (including many
opportunistic pathogens)56,57. Notably, expression of apidaecin
increased by over fivefold in heads (primarily role in social
immunity) and 50-fold in guts (primary role in individual
immunity) after 4 weeks of LX3 supplementation, while no dis-
cernible changes were observed in vehicle and NTC groups.
In a recent report, Kwong et al. showed expression of apidaecin
to be uniquely elevated in healthy adults in comparison to
microbiota-depleted counterparts38. Moreover, three core phy-
lotype strains of G. apicola,S. alvi, and Lactobacillus Firm-5 were
found to upregulate apidaecin (and to a lesser extent hyme-
noptaecin) expression in gut tissue yet were largely resistant to
both of these purified AMPs in vitro38. This suggests that host
AMPs may play a role in shaping the microbiota through
pathogen exclusion and selective refinement of evolutionarily
adapted bacterial species. Supporting this theory, we found that
intraindividual expression of apidaecin and several other key
immune genes covaried in their expression patterns based on
Apis(G)
Def-1(G)
Def-2(H)
Lyso(H)
Abac(H)
Abac(G)
Def-2(G)
Def-1(H)
Apid(H)
Cat(G)
Hym(H)
Hym(G)
Apis(H)
Lyso(G)
Apid(G)
Cat(H)
VgMC(H)
VgMC(G)
L. rhamnosus
L. plantarum
ß-proteobacteria
a-proteobacteria
γ-proteobacteria
Bifidobacterium
F. perrara
L. kunkeei
G. apicola
Firmicutes
Bacteroidetes
P. larvae
-1.0
-0.5
0
0.5
1.0
r -value
Fig. 6 Correlations between host gene expression and bacterial loads. At the end of the 6-week experimental period, bacterial abundances in the guts of
adult honey bees were compared with head and gut expression of nine immune- or antioxidant-related genes. Scale shown represents Pearson correlation
coefficient, r, for n=20–24 individual adults for each comparison. (G) =Gut gene expression, (H) =Head gene expression. The horizontal dendrogram
acts to group host genes that covary in their expression patterns while the vertical dendrogram groups bacteria based on their co-occurring abundance
relative to host gene expression. Both dendrograms were calculated using Euclidean distance and the complete ‘hclust’function in R.
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tissue type as well as the abundance of LX3 strains and core
phylotype members in the gut (Fig. 6). Whereas for P. larvae,
abundance in the adult gut was negatively associated with the
expression of most AMPs tested, which suggests a robust host
immune response is critical to suppressing this pathogen in the
hive. The findings also suggest that the increase in core SVs and
decrease in noncore SVs observed in LX3-treated adults (Fig. 3b)
were likely a result of upregulated host AMPs (Fig. 5d–g). This
evidence cumulatively supports that timely use of probiotics may
help in the re-establishment of a healthy microbiota following
OTC exposure and concurs with the proposition that immu-
nostimulatory Lactobacillus spp. can ‘reset’dysbiotic microbiota
phenotypes58. Thus, while it has been long known that probiotics
can induce an immune response in honey bees37, the function-
ality of probiotics in modulating AMPs with microbiota-shaping
properties is only now becoming apparent. It is important to note,
however, that LX3 supplementation was unable to fully restore
microbiota composition to pre-OTC baseline levels in terms of
overall diversity and structure during the time frame of this
study. Though, a limitation to our findings is that low-level
residues of antibiotics can persist in the hive for up to 3 months
post-treatment39, which may have functionally impeded the
corrective potential of treatment. Nevertheless, it remains plau-
sible that targeted modulation of immune components may
facilitate host-guided microbiota restoration following exposure
to noxious chemicals distributed across apicultural and agri-
cultural landscapes.
Resolving the mechanisms by which supplemental lactobacilli
modulate honey bee immunity under normal field conditions,
and the subsequent impact this has on their microbiota, is chal-
lenging due to the highly complex social interaction networks of
adult workers and convoluted microbial dynamics in the hive.
Though, innate immunity of insects is highly conserved59 with
peptidoglycan recognition proteins (PGRPs) thought to play an
important role in bacterial sensing via cell wall component
recognition. Interestingly, some but not all Lactobacillus spp.
produce diaminopimelic acid (DAP)-containing peptidoglycan60
which is known to activate Imd pathway signaling (leading to
downstream production of AMPs) in insects via select PGRP
binding61. Corroborating this, past work has shown that Lp39
(one of the LX3 strains used in this study) produces DAP-type
peptidoglycan that can potently upregulate host AMP gene
expression via Imd pathway signaling in D. melanogaster19.
Alternatively, LGR-1 can modulate other divisions of insect
innate immunity (e.g. Duox pathway signaling) and ameliorate
neonicotinoid-induced suppression of host-generated micro-
bicidal reactive oxygen species21. Its noteworthy to mention that
honey bee-derived core lactobacilli members including L. kunkeei,
L. apinorum,L. mellifer,L. mellis,L. melliventris,L. kimbladii,L.
helsingborgensis, and L. kullabergensis all lack the ability to pro-
duce DAP-type peptidoglycan60,62. Accordingly, the absence of
this crucial cell wall immune modulator may potentially explain
the multiple recent failures at using honey bee-specific lactobacilli
to reduce AFB disease severity63,64. Whether L. kunkeei BR-1
affected the overall immune-boosting performance of LX3 is
undetermined, though it would be an interesting avenue for
future studies given the opposing immune response to various
lactobacilli-derived peptidoglycans observed in mouse models65.
In summary, this study has: (i) recapitulated the deleterious
effects of antibiotics on the gut microbiota, immunity, and pro-
ductivity of honey bees, (ii) substantiated the anti-P. larvae
properties of LX3 supplemented to subclinically infected hives,
and (iii) characterized the transcriptional response of several
important immune- and antioxidant-related genes in response to
hive supplementation with LX3. These results are not intended to
refute the value of antibiotics or discourage their usage under
appropriate circumstances. Instead, we suggest that exploiting the
multipronged and evolutionarily adapted immune repertoire of
honey bees via microbial-based therapeutics could augment
current management strategies and offer a more effective method
of controlling hive pathogens.
Methods
Culture conditions of bacterial strains. The three strains of lactobacilli used in
this study were L. plantarum Lp39 (American Type Culture Collect [ATCC]
14917), L. rhamnosus GR-1 (ATCC 55826), and L. kunkeei BR-1 (honey bee-
derived isolate from a healthy hive)25. Unless otherwise stated, routine culturing of
these strains was performed under anaerobic conditions at 37 °C using de Man,
Rogosa, and Sharpe (catalog number: 288130, BD Difco) broth or agar supple-
mented with 10 g/L D-fructose (catalog number: F-3510, Sigma-Aldrich; MRS-F).
A. globiformis (ATCC 8010) utilized in hemolymph ZOI assays was routinely
cultured aerobically at 37 °C using Luria-Bertani (catalog number: DF0446173, BD
Difco; LB) broth or agar.
Apiary conditions and experimental design. Two separate experiments were
performed in two distinct apiaries maintained within a single geographic region
near Western University (London, ON, Canada). These experimental apiaries were
selected based on their geographic inclusion (<1 km away; a distance shown to be
at risk of contracting high spore loads66) within a boundary assessed to have a
recent increase of AFB incidence denoted through provincial apiary inspection by
the Ontario Ministry of Agriculture and Food and Ministry of Rural Affairs
(OMAFRA). Apiary A (N=8 Carniolan hives) and apiary B (N=8 Buckfast hives)
were subjected to similar experimental designs between early summer (June–July)
and late summer (August–September), respectively (Supplementary Fig. 1). In each
case, all hives received standard dusting treatment with OTC hydrochloride (cat-
alog number: 0223111, MEDIVET Pharmaceuticals) for 2 weeks according to
manufacturer instructions. Two hives in each apiary were then left to freely interact
with experimental hives in an effort to emulate realistic buffering conditions of
undisturbed local neighboring colonies. The remaining six hives in each apiary
were longitudinally monitored for an additional 4 weeks after being randomly
assorted into the following experimental groups: (i) a NTC group that received no
supplementation following administration of OTC, (ii) a vehicle control group
(VEH) that received a standard 250 g pollen substitute patty (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) with the addition of 4 mL phosphate-
buffered saline (0.01 M) once per week, or (iii) a probiotic supplementation group
(LX3) that received a 250 g pollen substitute patty infused with Lp39, GR-1, and
BR-1 strains (each at a final concentration of 1 × 109colony forming units [CFU]/
g) once per week. All larval samples in this study represent third-to-fifth instar,
whereas adult samples represent nurse-aged workers found in close association
with the brood. Sampling was performed in a manner to minimize the number of
bees removed from the hive during experimentation. Hive tools were flame ster-
ilized prior to use between each of the hives and sterile latex gloves were employed
to prevent cross-contamination of LX3 strains and potential pathogens.
Hive sampling procedures. For molecular quantification of P. larvae during OTC
exposure, adults and larvae were sampled from n=6 hives in apiary A with several
replicates per hive. For adults, four samples from each hive were collected per time
point (0, 1, and 2 weeks) with each replicate consisting of an individual dissected
abdomen (n=24 samples for each time point collected equally across the hives).
For larvae, five samples from each hive were collected per time point (day 0, day 7,
day 14) with each replicate consisting of a pooled sample of three whole larvae
(n=30 samples for each time point collected equally across the hives). Similarly,
for quantification of P. larvae during the post-OTC supplementation period, adults
and larvae were sampled from a total of n=12 hives (six hives from apiary A [n=
2 NTC, n=2 for vehicle treatment, n=2 for probiotic treatment] and six hives
from apiary B [n=2 NTC, n=2 for vehicle treatment, n=2 for probiotic treat-
ment]). For both adults and larvae, four samples from each hive were collected per
time point (0, 2, and 4 weeks) with each replicate consisting of either an individual
dissected abdomen or a pooled sample of three whole larvae, respectively (n=48
adult samples and n=48 larval samples collected equally across 12 hives at each
time point).
For experiments evaluating the adult microbiota in response to OTC exposure,
n=6 hives from apiary A were sampled in biological duplicate (i.e. two samples
from each hive with each replicate consisting of three nurse-aged adult guts pooled
together) at two separate time points (pre-OTC and post-OTC), which represented
a total of 72 individual bees sampled randomly and equally across the hives. The
same sampling methodology was also implemented in experiments evaluating the
adult and larval gut microbiota during the post-OTC supplementation period, for
which a total of n=8 hives were used (four hives from apiary A [n=2 for vehicle
treatment, n=2 for probiotic treatment] and four hives from apiary B [n=2 for
vehicle treatment, n=2 for probiotic treatment]).
For experiments characterizing the relationship between intraindividual
bacterial abundances and host innate immune or antioxidant gene expression,
ARTICLE COMMUNICATIONS BIOLOGY | https://doi.org/10.1038/s42003-020-01259-8
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Content courtesy of Springer Nature, terms of use apply. Rights reserved

adults were sampled at the final time point (week 4 of the supplementation period)
from a total of n=12 hives (six hives from apiary A [n=2 NTC, n=2 for vehicle
treatment, n=2 for probiotic treatment] and six hives from apiary B [n=2 NTC,
n=2 for vehicle treatment, n=2 for probiotic treatment]). Each hive was sampled
in duplicate (i.e. two samples from each hive with each replicate consisting of a
single individual nurse-aged adult bee) with each adult sample then subcategorized
into head and gut groupings (n=20–24 total paired head and gut samples used as
not all variables measured were detectable in all samples).
Molecular-based quantification of P. larvae. Larval (whole body) samples were
evaluated as they represent the target developmental stage for P. larvae infection,
whereas adults (dissected abdomen) are passive carriers of P. larvae and provide a
good estimate of overall microbial diversity31. Respective samples were surface
sterilized using 0.25% sodium hypochlorite and then rinsed in ddH
2
O for 30 s.
DNA was then extracted from samples using the previously described CTAB
method53. Bacterial loads were determined by qPCR using the Power SYBR Green
PCR Master Mix kit (Applied Biosystems) following manufacturer’s instructions.
Universal 16S rRNA gene and species-specific primer sets used in this study are
listed in Supplementary Table 1. All qPCR reactions were performed in DNase-
and RNase-free 384-well microplates on a QuantStudio 5 Real-Time PCR System
(Applied Biosystems) and analyzed with associated QuantStudio Design and
Analysis software. Copy numbers of target 16S rRNA genes were calculated as
previously described using established primer efficiencies and limits of detection25.
Enumeration of capped brood. To evaluate the efficacy of OTC and
LX3 supplementation on hive health dynamics, the coverage of capped brood on
hive frames (an established metric for assessing colony strength and reproduc-
tion30) was measured weekly during experimentation. Briefly, both sides of every
frame in all experimental hives were photographed at the specified sampling time
points and then semiautomatically counted using image analysis software as pre-
viously described24.
Determination of in vitro antimicrobial capacity of adult hemolymph. Anti-
microbial activity of adult hemolymph was determined using an established ZOI
assay35 Prior to experimentation, the bacterial indicator stock of A. globiformis was
grown aerobically in LB broth at 30 °C for 48 h to a final concentration of 1.6 × 109
CFU/mL. Subsequently, 1 mL of broth culture was used to create a lawn of A.
globiformis on LB agar plates. Next, flash frozen adult samples were thawed to 4 °C
and hemolymph was extracted via centrifugation (1200 × gfor 1 min) following the
aseptic removal of antennae. Further, antimicrobial capacity was determined by
plating 0.75 μL of hemolymph in preconstructed wells on agar plates that had been
freshly seeded with a lawn of A. globiformis. The bottom of each plate was marked
with a grid consisting of 12 squares, each 2 × 2 cm. Streptomycin (0.1 g/mL in 80%
glycerol) was used at 1:200 and 1:300 dilutions as a positive control to account for
batch variation among agar plates35. The agar plates were then incubated aero-
bically at 30 °C for 48 h prior to measurement of ZOI diameters for hemolymph or
antibiotic control wells. ZOI was normalized by hive, and data are shown relative to
experimental start points for OTC and LX3 treatment periods.
Preparation of the 16S rRNA gene library. Targeted amplification of the V4
region of the bacteria 16S rRNA gene was achieved using established GOLAY-
barcoded primers (5′–3′) ACACTCTTTCCCTACACGACGCTCTTCCGA
TCTNNNNxxxxxxxxxxxxGTGCCAGCMGCCGCGGTAA and (5′–3′)
CGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCTNN
NNxxxxxxxxxxxxGGACTACHVGGGTWTCTAAT wherein ‘xxxxxxxxxxxx’
represents the sample-specific 12-mer barcode following the Illumina adapter
sequence used for downstream library construction67. A BioMek Automated
Workstation (Beckman Coulter) was then used to transfer 2 µL of sample DNA
(5 ng/µL) to a 96-well 0.2-mL PCR plate containing 10 µL of each primer per well
(3.2 pmol/µL). Next, 20 µL of GoTaq 2X Colorless Master Mix (Promega) was
added to each well and plates were sealed using PCR-grade adhesive aluminum foil.
PCR was then performed using a Prime Thermal Cycler (Technie) with the fol-
lowing reaction conditions: an initial activation step at 95 °C, followed by 25 cycles
of 95 °C for 1 min, 52 °C for 1 min, and 72 °C for 1 min. After completion, the
thermocycler was held at 4 °C, and amplicons were stored at −20 °C until further
processing.
Sequencing and analysis of the 16S rRNA gene dataset. 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 paired-end Illumina platform
adapted for 2 × 250 bp paired-end chemistry. Sequence reads were then processed,
aligned, and categorized using the DADA2 (v1.8) pipeline to infer exact amplicon
SVs from the data68. Briefly, sequence reads were filtered (reads truncated after a
quality score of ≤2 and forward/reverse reads truncated after 155/110 bases,
respectively) and trimmed (10 bases off 5′end of reverse reads) using optimized
parameter settings as recommended. Next, sequence reads were de-replicated, de-
noised, and merged using DADA2 default parameters. After omitting PCR blank
control samples, the dataset used to assess the effects of OTC on the adult
microbiota consisted of 24 nurse bee-derived gut samples and a total of 1,109,022
reads with an average count of 46,209 reads per sample. Following quality assur-
ance measures described in the DADA2 pipeline69, an average of 13.94% reads
were removed from each sample, leaving a total of 955,475 filtered reads. The
dataset which evaluated the impact of probiotic treatment following antibiotic
exposure consisted of 16 nurse bee-derived gut samples (372,198 total reads with
an average of 23,262 reads per sample) and 16 larval samples (59,335 total reads
with an average of 3708 reads per sample). Following quality control, an average of
11.63% and 31.29% reads were removed from adult and larval datasets, respec-
tively. SV read counts were left in their unadulterated state for analysis (i.e. copy
number was not corrected) to prevent additional noise in the dataset70. Taxonomic
designations were assigned to SVs using the SILVA nonredundant training set
(V132) configured for DADA2. Phylotype identity was determined by cross
referencing with a previously established honey bee-specific seed alignment of 276
unique representatives71. Raw sequence reads are uploaded to the NCBI Sequence
Read Archive and accessible under BioProject ID PRJNA610196.
Simultaneous extraction of RNA and DNA from individual honey bees. Dis-
sected head and abdomen samples from adult honey bees were surface sterilized
using 0.25% sodium hypochlorite and then rinsed in ddH
2
O for 30 s. Subsequently,
samples were homogenized in TRIzol (Invitrogen) by beat beating and RNA
extracted following manufacturer’s instructions. Using the same samples, DNA was
also extracted from the TRIzol homogenates using a back-extraction buffer con-
sisting of 4 M guanidine thiocyanate, 50 mM sodium citrate dihydrate, and 1 M
Tris base as previously described72. Quality of RNA and DNA was assessed using a
microvolume spectrophotometer (DS-11 Spectrophotometer; DeNovix) and only
samples determined to have A260/280 absorbance ratios between 1.9 and 2.2 were
considered for further analyses.
Measurement of host gene expression and molecular quantification of bac-
terial communities. To determine host gene expression, a total of 1500 ng of the
extracted RNA from adult head and gut samples was reverse transcribed to cDNA
using a High-Capacity cDNA Reverse Transcription Kit following manufacturer’s
instructions (Applied Biosystems, catalog number: 4368813). RT-qPCR reactions
were performed with tenfold-diluted cDNA using the Power SYBR Green kit
(Applied Biosystems) following manufacturer’s instructions. Oligonucleotide pri-
mers used to evaluate host expression of innate immune and antioxidant-related
genes are listed in Supplementary Table 2. Honey bee alpha-tubulin was deter-
mined to be the most stably expressed endogenous control gene (compared to
ribosomal protein S5,microsomal glutathione-S-transferase, and UDP-
glucuronyltransferase) under experimental conditions in this study, and thus was
chosen as the internal standard for normalization as per MIQE guidelines73.To
measure bacterial abundance in samples, qPCR reactions were performed with
tenfold-diluted DNA (from back extraction) using the Power SYBR Green kit
(Applied Biosystems) following manufacturer’s instructions. The universal 16S
rRNA gene, phylotype-specific, and species-specific primer sets that were used for
molecule quantification of bacteria are listed in Supplementary Table 1. All qPCR
reactions 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 gene expression was
calculated using the 2−ΔΔ Ct method74 whereas bacterial abundance (measured via
copy number of target 16S rRNA genes) was calculated using previously established
primer efficiencies and limits of detection44,52,53.
Statistics and reproducibility. All compositional analyses on 16S rRNA gene
sequencing datasets were performed using QIIME2 (v2020.2) or in R (v3.6.0) using
ALDEx2 software (v1.18.0). Statistics for all other datasets were performed using
GraphPad Prism (v8.3.0). Datasets with unique values were tested for normality
using the Shapiro–Wilk test, whereas datasets with ties (two or more identical
values) were tested for normality using the D’Agostino–Pearson test. Normally
distributed data were statistically compared with two-tailed ttests, Pearson cor-
relations, one-way ANOVAs, or two-way ANOVAs as indicated. ANOVA tests
were complemented with Sidak’s multiple comparisons when appropriate. Non-
parametric datasets were statistically compared using Wilcoxon, Mann–Whitney,
or Kruskal–Wallis tests and complemented with Dunn’s multiple comparisons
(quantitative datasets) or BH false discovery rate method (compositional datasets)
when appropriate. Sample sizes and replicate details are described in the relevant
‘Methods’sections.
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
Raw 16S rRNA gene sequencing reads were uploaded to the NCBI Sequence Read
Archive and are available under BioProject accession: PRJNA610196. Other source data
underlying applicable figures are available in Supplementary Datasets 1–3.
COMMUNICATIONS BIOLOGY | https://doi.org/10.1038/s42003-020-01259-8 ARTICLE
COMMUNICATIONS BIOLOGY | (2020) 3:534 | https://doi.org /10.1038/s42003-020-01259-8 | www.nature.com/commsbio 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Received: 24 March 2020; Accepted: 24 August 2020;
References
1. Otterstatter, M. C. & Thomson, J. D. Does pathogen spillover from
commercially reared bumble bees threaten wild pollinators? PLoS One 3,
e2771 (2008).
2. Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by
combined stress from parasites, pesticides, and lack of flowers. Science 347,
1255957 (2015).
3. Smith, K. M. et al. Pathogens, pests, and economics: drivers of honey bee
colony declines and losses. EcoHealth 10, 434–445 (2013).
4. Fürst, M. A., McMahon, D. P., Osborne, J. L., Paxton, R. J. & Brown, M. J. F.
Disease associations between honeybees and bumblebees as a threat to wild
pollinators. Nature 506, 364–366 (2014).
5. Piva, S. et al. Could honey bees signal the spread of antimicrobial resistance in
the environment? Lett. Appl. Microbiol. 70, 349–355 (2020).
6. Tian, B., Fadhil, N. H., Powell, J. E., Kwong, W. K. & Moran, N. A. Long-term
exposure to antibiotics has caused accumulation of resistance determinants in
the gut microbiota of honeybees. mBio.3, e00377–12 (2012).
7. Raymann, K., Bobay, L.-M. & Moran, N. A. Antibiotics reduce genetic
diversity of core species in the honeybee gut microbiome. Mol. Ecol. 27,
2057–2066 (2018).
8. Raymann, K. & Moran, N. A. The role of the gut microbiome in health and
disease of adult honey bee workers. Curr. Opin. Insect Sci. 26,97–104 (2018).
9. Arbia, A. & Babbay, B. Management strategies of honey bee diseases. J.
Entomol. 8,1–15 (2011).
10. Murray, K. D. & Aronstein, K. A. Oxytetracycline-resistance in the honey bee
pathogen Paenibacillus larvae is encoded on novel plasmid pMA67. J. Apic.
Res. 45, 207–214 (2006).
11. Evans, J. D. Diverse origins of tetracycline resistance in the honey bee bacterial
pathogen Paenibacillus larvae.J. Invertebr. Pathol. 83,46–50 (2003).
12. Spivak, M. & Reuter, G. S. Resistance to American foulbrood disease by honey
bee colonies Apis mellifera bred for hygienic behavior. Apidologie 32, 555–565
(2001).
13. González, M. J. & Marioli, J. M. Antibacterial activity of water extracts and
essential oils of various aromatic plants against Paenibacillus larvae, the
causative agent of American Foulbrood. J. Invertebr. Pathol. 104, 209–213
(2010).
14. Beims, H. et al. Paenibacillus larvae-directed bacteriophage HB10c2 and its
application in American Foulbrood-affected honey bee larvae. Appl Env.
Microbiol.81, 5411–5419 (2015).
15. Alvarado, I. et al. Inhibitory effect of indole analogs against Paenibacillus
larvae, the causal agent of American foulbrood disease. J. Insect Sci. 17,
104–112 (2017).
16. Arredondo, D. et al. Lactobacillus kunkeei strains decreased the infection by
honey bee pathogens Paenibacillus larvae and Nosema ceranae.Benef.
Microbes.9, 279–290 (2017).
17. Chmiel, J. A., Daisley, B. A., Pitek, A. P., Thompson, G. J. & Reid, G.
Understanding the effects of sublethal pesticide exposure on honey bees: a role
for probiotics as mediators of environmental stress. Front. Ecol. Evol. 8,
e00022 (2020).
18. Mudroňová, D. et al. Lactobacillus sp. as a potential probiotic for the
prevention of Paenibacillus larvae infection in honey bees. J. Apic. Res. 50,
323–324 (2011).
19. Daisley, B. A. et al. Neonicotinoid-induced pathogen susceptibility is mitigated
by Lactobacillus plantarum immune stimulation in a Drosophila melanogaster
model. Sci. Rep. 7, 2703 (2017).
20. Trinder, M. et al. Probiotic Lactobacillus rhamnosus reduces organophosphate
pesticide absorption and toxicity to Drosophila melanogaster.Appl. Environ.
Microbiol. 82, 6204–6213 (2016).
21. Chmiel, J. A., Daisley, B. A., Burton, J. P. & Reid, G. Deleterious effects of
neonicotinoid pesticides on Drosophila melanogaster immune pathways. mBio
10, e01395–19 (2019).
22. Kaznowski, A. et al. The effects of probiotic supplementation on the content of
intestinal microflora and chemical composition of worker honey bees (Apis
mellifera). J. Apic. Res. 44,10–14 (2005).
23. Alberoni, D. et al. Impact of beneficial bacteria supplementation on the gut
microbiota, colony development and productivity of Apis mellifera L. Benef.
Microbes.9, 269–278 (2018).
24. Audisio, M. & Benítez-Ahrendts, M. Lactobacillus johnsonii CRL1647, isolated
from Apis mellifera L. bee-gut, exhibited a beneficial effect on honeybee
colonies. Benef. Microbes.2,29–34 (2011).
25. Daisley, B. A. et al. Novel probiotic approach to counter Paenibacillus larvae
infection in honey bees. ISME J. 14, 476–491 (2019).
26. Reid, G. Probiotics to prevent the need for, and augment the use of,
antibiotics. Can. J. Infect. Dis. Med. Microbiol. 17, 291–295 (2006).
27. Du, Y.-Q. et al. Adjuvant probiotics improve the eradication effect of triple
therapy for Helicobacter pylori infection. World J. Gastroenterol. WJG 18,
6302–6307 (2012).
28. Verdú, E. F. et al. Specific probiotic therapy attenuates antibiotic induced
visceral hypersensitivity in mice. Gut 55, 182–190 (2006).
29. Tarasova, E. et al. The influence of probiotic Enterococcus faecium strain L5 on
the microbiota and cytokines expression in rats with dysbiosis induced by
antibiotics. Benef. Microbes.1, 265–270 (2010).
30. Delaplane, K. S., Steen, Jvander & Guzman-Novoa, E. Standard methods for
estimating strength parameters of Apis mellifera colonies. J. Apic. Res. 52,1–12
(2013).
31. Erban, T. et al. Honeybee (Apis mellifera)-associated bacterial community
affected by American foulbrood: detection of Paenibacillus larvae via
microbiome analysis. Sci. Rep. 7, 5084 (2017).
32. Reid, G. et al. Microbiota restoration: natural and supplemented recovery of
human microbial communities. Nat. Rev. Microbiol. 9,27–38 (2011).
33. Fernandes, A. D., Macklaim, J. M., Linn, T. G., Reid, G. & Gloor, G. B.
ANOVA-like differential expression (ALDEx) analysis for mixed population
RNA-seq. PLoS One 8, e67019 (2013).
34. Li, J. H. et al. New evidence showing that the destruction of gut bacteria by
antibiotic treatment could increase the honey bee’s vulnerability to Nosema
infection. PLoS One 12, e0187505 (2017).
35. Harpur, B. A. et al. No genetic tradeoffs between hygienic behaviour and
individual innate immunity in the honey bee, Apis mellifera.PLoS One 9,
e104214 (2014).
36. López-Uribe, M. M., Fitzgerald, A. & Simone-Finstrom, M. Inducible versus
constitutive social immunity: examining effects of colony infection on glucose
oxidase and defensin-1 production in honeybees. R. Soc. Open Sci. 4, 170224
(2017).
37. Evans, J. D. & Lopez, D. L. Bacterial probiotics induce an immune response
in the honey bee (Hymenoptera: Apidae). J. Econ. Entomol. 97, 752–756
(2004).
38. Kwong, W. K., Mancenido Amanda, A. L. & Moran, N. A. Immune system
stimulation by the native gut microbiota of honey bees. R. Soc. Open Sci. 4,
170003 (2017).
39. Raymann, K., Shaffer, Z. & Moran, N. A. Antibiotic exposure perturbs the gut
microbiota and elevates mortality in honeybees. PLoS Biol. 15, e2001861
(2017).
40. Engel, P., Martinson, V. G. & Moran, N. A. Functional diversity within the
simple gut microbiota of the honey bee. Proc. Natl Acad. Sci. USA 109,
11002–11007 (2012).
41. Peng, Y.-S. C., Mussen, E., Fong, A., Montague, M. A. & Tyler, T. Effects of
chlortetracycline of honey bee worker larvae reared in vitro. J. Invertebr.
Pathol. 60, 127–133 (1992).
42. Stecher, B., Maier, L. & Hardt, W.-D. ‘Blooming’in the gut: how dysbiosis
might contribute to pathogen evolution. Nat. Rev. Microbiol. 11, 277–284
(2013).
43. Mohr, K. I. & Tebbe, C. C. Diversity and phylotype consistency of bacteria in
the guts of three bee species (Apoidea) at an oilseed rape field. Environ.
Microbiol. 8, 258–272 (2006).
44. Martinson, V. G., Moy, J. & Moran, N. A. Establishment of characteristic gut
bacteria during development of the honeybee worker. Appl. Environ.
Microbiol. 78, 2830–2840 (2012).
45. Ignasiak, K. & Maxwell, A. Oxytetracycline reduces the diversity of
tetracycline-resistance genes in the Galleria mellonella gut microbiome. BMC
Microbiol. 18, 228 (2018).
46. Burmølle, M. et al. Enhanced biofilm formation and increased resistance to
antimicrobial agents and bacterial invasion are caused by synergistic
interactions in multispecies biofilms. Appl. Environ. Microbiol. 72, 3916–3923
(2006).
47. Davies, D. Understanding biofilm resistance to antibacterial agents. Nat. Rev.
Drug Discov. 2, 114–122 (2003).
48. Koch, H. & Schmid-Hempel, P. Socially transmitted gut microbiota protect
bumble bees against an intestinal parasite. Proc. Natl Acad. Sci. 108,
19288–19292 (2011).
49. Zheng, H. et al. Metabolism of toxic sugars by strains of the bee gut symbiont
Gilliamella apicola.mBio.7, e01326–16 (2016).
50. Engel, P. & Moran, N. A. Functional and evolutionary insights into the simple
yet specific gut microbiota of the honey bee from metagenomic analysis. Gut
Microbes 4,60–65 (2013).
51. Zheng, H., Powell, J. E., Steele, M. I., Dietrich, C. & Moran, N. A. Honeybee
gut microbiota promotes host weight gain via bacterial metabolism and
hormonal signaling. Proc. Natl Acad. Sci. 114, 4775–4780 (2017).
52. Kešnerová, L. et al. Disentangling metabolic functions of bacteria in the honey
bee gut. PLoS Biol. 15, e2003467 (2017).
ARTICLE COMMUNICATIONS BIOLOGY | https://doi.org/10.1038/s42003-020-01259-8
12 COMMUNICATIONS BIOLOGY | (2020) 3:534 | https://doi.org /10.1038/s42003-020-01259-8 | www.nature.com/commsbio
Content courtesy of Springer Nature, terms of use apply. Rights reserved

53. Powell, J. E., Martinson, V. G., Urban-Mead, K. & Moran, N. A. Routes of
acquisition of the gut microbiota of the honey bee Apis mellifera.Appl.
Environ. Microbiol. 80, 7378–7387 (2014).
54. Ilyasov, R., Gaifullina, L., Saltykova, E., Poskryakov, A. & Nikolenko, A.
Review of the expression of antimicrobial peptide defensin in honey bees Apis
Mellifera L. J. Apic. Sci. 56, 115–124 (2012).
55. Khilnani, J. C. & Wing, H. J. Protocols to test the activity of antimicrobial
peptides against the honey bee pathogen Paenibacillus larvae.J. Microbiol.
Methods 117,54–56 (2015).
56. Li, W.-F., Ma, G.-X. & Zhou, X.-X. Apidaecin-type peptides: biodiversity,
structure–function relationships and mode of action. Peptides 27, 2350–2359
(2006).
57. Casteels, P., Ampe, C., Jacobs, F. & Tempst, P. Functional and chemical
characterization of Hymenoptaecin, an antibacterial polypeptide that is
infection-inducible in the honeybee (Apis mellifera). J. Biol. Chem. 268,
7044–7054 (1993).
58. Daisley, B. A., Chmiel, J. A., Pitek, A. P., Thompson, G. J. & Reid, G. Missing
microbes in bees: how systematic depletion of key symbionts erodes
immunity. Trends Microbiol.https://doi.org/10.1016/j.tim.2020.06.006. (2020)
59. Wang, Q., Ren, M., Liu, X., Xia, H. & Chen, K. Peptidoglycan recognition
proteins in insect immunity. Mol. Immunol. 106,69–76 (2019).
60. Felis, G. E. & Dellaglio, F. Taxonomy of lactobacilli and bifidobacteria. Curr.
Issues Intest. Microbiol. 8,44–61 (2007).
61. Lesperance, D. N. & Broderick, N. A. Microbiomes as modulators of
Drosophila melanogaster homeostasis and disease. Curr. Opin. Insect Sci. 39,
84–90 (2020).
62. Olofsson, T. C., Alsterfjord, M., Nilson, B., Butler, È. & Vásquez, A.
Lactobacillus apinorum sp. nov., Lactobacillus mellifer sp. nov., Lactobacillus
mellis sp. nov., Lactobacillus melliventris sp. nov., Lactobacillus kimbladii sp.
nov., Lactobacillus helsingborgensis sp. nov. and Lactobacillus kullabergensis
sp. nov., isolated from the honey stomach of the honeybee Apis mellifera.Int.
J. Syst. Evol. Microbiol. 64, 3109–3119 (2014).
63. Stephan, J. G. et al. Honeybee-specific lactic acid bacterium supplements have
no effect on American Foulbrood-infected honeybee colonies. Appl. Environ.
Microbiol. 85, e00606–e00619 (2019).
64. Lamei, S. et al. Feeding honeybee colonies with honeybee-specific lactic acid
bacteria (Hbs-LAB) does not affect colony-level Hbs-LAB composition or
Paenibacillus larvae spore levels, although American Foulbrood affected
colonies harbor a more diverse Hbs-LAB community. Microb. Ecol. 79,
743–755 (2020).
65. Shida, K., Kiyoshima-Shibata, J., Kaji, R., Nagaoka, M. & Nanno, M.
Peptidoglycan from lactobacilli inhibits interleukin-12 production by
macrophages induced by Lactobacillus casei through Toll-like receptor 2-
dependent and independent mechanisms. Immunology 128,e858–e869 (2009).
66. Lindström, A., Korpela, S. & Fries, I. Horizontal transmission of Paenibacillus
larvae spores between honey bee (Apis mellifera) colonies through robbing.
Apidologie 39, 515–522 (2008).
67. Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on
the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).
68. Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants
should replace operational taxonomic units in marker-gene data analysis.
ISME J. 11, 2639–2643 (2017).
69. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina
amplicon data. Nat. Methods 13, 581–583 (2016).
70. Louca, S., Doebeli, M. & Parfrey, L. W. Correcting for 16S rRNA gene copy
numbers in microbiome surveys remains an unsolved problem. Microbiome 6,
41 (2018).
71. Newton, I. L. & Roeselers, G. The effect of training set on the classification of
honey bee gut microbiota using the Naive Bayesian Classifier. BMC Microbiol.<