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ARTICLE
Abiraterone acetate preferentially enriches for
the gut commensal Akkermansia muciniphila in
castrate-resistant prostate cancer patients
Brendan A. Daisley 1,2,3, Ryan M. Chanyi 1,2,3, Kamilah Abdur-Rashid1,2,3, Kait F. Al 1,2,3,
Shaeley Gibbons1,2,3, John A. Chmiel 1,2,3, Hannah Wilcox1,2,3, Gregor Reid 1,2,3, Amanda Anderson4,
Malcolm Dewar4, Shiva M. Nair 4, Joseph Chin4& Jeremy P. Burton 1,2,3,4 ✉
Abiraterone acetate (AA) is an inhibitor of androgen biosynthesis, though this cannot fully
explain its efficacy against androgen-independent prostate cancer. Here, we demonstrate
that androgen deprivation therapy depletes androgen-utilizing Corynebacterium spp. in pros-
tate cancer patients and that oral AA further enriches for the health-associated commensal,
Akkermansia muciniphila. Functional inferencing elucidates a coinciding increase in bacterial
biosynthesis of vitamin K2 (an inhibitor of androgen dependent and independent tumor
growth). These results are highly reproducible in a host-free gut model, excluding the pos-
sibility of immune involvement. Further investigation reveals that AA is metabolized by
bacteria in vitro and that breakdown components selectively impact growth. We conclude
that A.muciniphila is a key regulator of AA-mediated restructuring of microbial communities,
and that this species may affect treatment response in castrate-resistant cohorts. Ongoing
initiatives aimed at modulating the colonic microbiota of cancer patients may consider tar-
geted delivery of poorly absorbed selective bacterial growth agents.
https://doi.org/10.1038/s41467-020-18649-5 OPEN
1Department of Microbiology and Immunology, The University of Western Ontario, London, ON N6A 5C1, Canada. 2Canadian Centre for Human
Microbiome and Probiotics Research, London, ON N6C 2R5, Canada. 3Lawson Health Research Institute, St. Joseph’s Health Care London, London, ON N6A
4V2, Canada. 4Department of Surgery, Division of Urology, Schulich School of Medicine, London, ON N6A 5C1, Canada. ✉email: jeremy.
burton@lawsonresearch.com
NATURE COMMUNICATIONS | (2020)11:4822 | https://doi.org/10.1038/s41467- 020-18649-5 | www.nature.com/naturecommunications 1
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Several recent reports have demonstrated that the human
microbiome plays a critical role in cancer development and
progression, as well as treatment efficacy1–3. One bacterial
species, Akkermansia muciniphila, appears to be particularly
important for positive response to anti-PD-1-based immu-
notherapies. This species has also been inversely correlated with
inflammation, obesity, and a variety of metabolic disorders in
mice and humans4,5. However, there remains limited knowledge
on how the gastrointestinal (GI) microbiota affects distant
malignancies, such as prostate cancer (PC). This disease affects
approximately one in seven men, has high morbidity rates, and is
a tremendous financial burden on healthcare systems. The pro-
gression of PC relies on androgen receptor activation, primarily
by testosterone and dihydrotestosterone. In PC patients with
metastatic or high-risk localized disease, androgen deprivation
therapy (ADT) via medical castration is a key part of therapy
administered in the form of systemic (intramuscular or sub-
cutaneous) gonadotropin-releasing hormone agonists, such as
goserelin, leuprorelin, or triptorelin. Disease progression despite
castrate levels of androgens is termed castrate-resistant PC and is
commonly treated with oral androgen receptor axis-targeted
therapies, such as abiraterone acetate (AA). Notably, AA is poorly
absorbed, and it is estimated that ~55% of the unaltered parent
compound and 22% of abiraterone from an administered dose are
excreted in the feces6—demonstrating that the patient GI
microbiota is exposed to high concentrations of AA and that
partial metabolism of AA may occur in the gut.
Most studies to date have not considered the direct influence of
PC treatments on the GI microbiota or vice versa. Based on the
known association between serum sex steroid hormone levels and
GI microbiota composition7, we suspected that androgen deple-
tion in PC patients receiving ADT might have unintentional
consequences on microbial homeostasis and thereby impact
clinical outcome. We also theorized that xenobiotic metabolism
of AA by the GI microbiota of PC patients could be an unin-
tended consequence of oral delivery, but a likely scenario based
on previous studies showing microbiota-mediated bio-
transformation of irinotecan, non-steroidal anti-inflammatory
drugs, digoxin, metformin, and a myriad of other pharmaceutical
drugs8.
In this study, we evaluate how systemic ADT and oral AA
treatments differentially impact the GI microbiota of PC patients.
The findings demonstrate that systemic depletion of androgens via
ADT also depletes androgen-utilizing and pro-inflammatory
Corynebacterium spp. Alternatively, oral AA distinctively remo-
dels the GI microbiota characterized most notably by promotion of
the health-associated and anti-inflammatory gut commensal, A.
muciniphila. Functional inferencing suggests that these changes are
also associated with a shift towards increased bacterial biosynthesis
of certain host vitamins relevant to PC. Through further investi-
gation and modeling of this unique microbiota–xenobiotic inter-
action in a host-free environment, we identify that A. muciniphila
is a major determinant influencing the outcome of AA exposure
on human-derived bacterial communities—a clinically relevant
finding suggests that A. muciniphila may improve oral AA treat-
ment response in PC patients.
Results
PC treatment correlates with patient microbiota. To investigate
potential interactions between PC treatment drugs and the GI
microbiota, we performed 16S rRNA gene sequencing on fecal
samples collected from 68 PC patients following routine digital
rectal examination (patient characteristics outlined in Supple-
mentary Table 1). The rectal microbiota composition of those
receiving either ADT or ADT combined with oral AA (ADT +
AA) was significantly different based on several measures of
comparison relative to control patients not receiving any form of
therapy (Fig. 1). Summarizing the dataset, a principal component
analysis (PCA) exploring patient microbiota differences (based on
Aitchison distances between samples) demonstrated that the
microbiota composition of ADT and ADT +AA patients shifted
slightly from that of the control group along the PC1 axis and
each other along the PC2 axis—accounting for 20.4% and 15.7%
of interpersonal microbiota variability, respectively (Fig. 1a). The
two largest influencers driving these directional shifts, based on
genus-level ordination, were found to be Akkermansia and Cor-
ynebacterium (Fig. 1b, c). Additional overview analyses were
performed on the basis of previously reported differences in GI
microbiota alpha diversity between PC patients and healthy
controls9. Shannon’s diversity (a metric of bacterial community
alpha diversity accounting for species abundance and evenness)
was found to be significantly lower in ADT +AA samples
compared to control samples (Supplementary Fig. 1A–C).
Given that a variety of pharmaceutical drugs and various
disease states are known to influence the GI microbiota, we
examined a broad range of patient variables during evaluation of
potential confounders in the clinical metadata (Supplementary
Table 1). Out of the 17 total factors evaluated, only AA (p=
0.001), ADT (p=0.043), and corticosteroid treatment (p=0.049)
had a significant impact on overall microbiota composition
(Supplementary Table 2). We concluded from this that no patient
variables better explained the variance in microbiota composition
than the PC treatments themselves and that identification of
corticosteroids as having a significant impact on microbiota
composition was likely due to the disproportionally high degree
of overlap with prescribed regimens including ADT.
Androgen deprivation reduces androgen-utilizing gut bacteria.
In comparing differentially abundant taxa between patient groups,
ADT and ADT +AA samples both demonstrated a decreased
relative abundance in Corynebacterium (95% confidence intervals
[CIs], 0.001–0.064% and 0.001–0.099%, relative abundance,
respectively) compared to the control group (95% CI,
0.097–0.509% relative abundance). Corynebacterium spp. belong to
the phylum Actinobacteria and are well-known testosterone
metabolizers (some of which possess 5α-reductase activity10)and
have been shown to colonize androgen-rich anatomical sites of
males, including the axilla11 and urogenital tract12. Exploratory
inquiry demonstrated a general trend towards a decreased pro-
portion of several other steroid-metabolizing genera within Acti-
nobacteria in either ADT, ADT +AA, or both treatment groups
(Fig. 1d, e and Supplementary Data 1). These results suggest that
depletion of circulating androgen levels via systemic ADT may
indirectly reduce host colonization by testosterone-metabolizing
species. This substantiates previous findings on the interplay
between serum sex steroid hormone levels and the GI microbiota7.
In addition, we demonstrate that AA can directly inhibit
several testosterone-metabolizing Actinobacteria in vitro, includ-
ing multiple isolates of human-derived Corynebacterium spp.
(Supplementary Fig. 2E). We confirmed that this inhibition was
due to the abiraterone portion of AA and that acetate alone was
unable to inhibit any of the strains tested (Supplementary
Fig. 2F–H). Alongside the structural similarities and interking-
dom receptor activation of various steroid hormones and
bacterial autoinducers13, our findings suggest that the active
portion of AA (abiraterone; a steroidal progesterone derivative)
may interfere with bacterial growth dynamics. One potential
mechanism could be cross-reactivity of AA with actinobacterial
steroid hydroxylases that, like mammalian CYP17A, are capable
of catalyzing carbon–carbon bond cleavages14. While AA is
ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-18649-5
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highly selective towards human enzymes, its potential effect on
microbial enzymes in the gut warrants further investigation.
Akkermansia muciniphila modulates AA effects on patient
microbiota. In contrast to the depletion of Actinobacteria, a
considerable enrichment of A. muciniphila was observed in ADT
+AA patient samples (95% CI =0.094–2.436%) compared to
both ADT (95% CI =0.031–0.304%) and no treatment control
(95% CI =0.035–0.565%) group samples (Fig. 1e). A recent
smaller size study (n=30) also demonstrated A. muciniphila to
be increased in patients receiving oral androgen receptor axis-
targeted therapies such as AA, bicalutamide, and enzalutamide9.
However, the authors did not differentiate between the three oral
treatments in their statistical analyses, making it difficult to
ascertain whether the observed effects were due to host-mediated
hormone depletion, direct drug interactions with the gut micro-
biota, or both.
To delineate between host hormone–microbe interactions and
potential immune-based or cytochrome P450 inhibition-
mediated alterations to the gut microbiota, we investigated the
direct effects of AA on fecal microbiota dynamics in vitro. A
standardized amount of fecal inoculum was prepared from eight
donors not receiving any form of PC treatment and then grown
anaerobically in brain heart infusion (BHI) media supplemented
with either AA or vehicle. Following 24 h of growth, we found a
substantial decrease in beta diversity (i.e., AA-exposed samples
had more similar microbiota compositions than did non-exposed
samples) when A. muciniphila was present at a relative
abundance of 0.1% or higher, whereas no change was observed
in samples with low A. muciniphila levels (<0.1% relative
abundance; Fig. 2a–c). Similarly, alpha diversity of AA-exposed
samples also demonstrated a similar trend showing that the
degree to which AA altered microbial diversity indices was
dependent on background levels of A. muciniphila in patient
0.001
0.01
0.1
1
10
100
Relative abundance (%)
CTRL
0.001
0.01
0.1
1
10
100
Relative abundance (%)
****
Akkermansia
Alistipes
Bacteroides
Bifidobacterium
Blautia
Campylobacter
Corynebacterium
Escherichia
Faecalibacterium
Finegoldia
Fusobacterium
Klebsiella
Lachnospiraceae_incertae_sedis
Parabacteroides
Porphyromonas
Prevotella
Roseburia
Ruminococcaceae_incertae_sedis
Ruminococcus
–4
0
4
–4 048
PC1: 20.4%
PC2: 15.7%
Treatment
CTRL
ADT
ADT + AA
Lactobacillus
ADT+AA
–4 –2 0 2
ADT
–4 –2 0 2
Median absolute log2 difference compared to CTRL
ab
c
d
Akkermansia
Corynebacterium Corynebacterium
Lawsonella
n.s.
**
Akkermansia
Corynebacterium
***
e
Actinobacteria
Bacteroides
Cyanobacteria
Epsilonbacteraeota
Euryarchaeota
Firmicutes
Fusobacteria
Lentisphaerae
Patescibacteria
Proteobacteria
Synergistetes
Tenericutes
Verrucomicrobia
ADT ADT + AA
CTRL ADT ADT + AA
Fig. 1 Rectal swab microbiota from prostate cancer patients receiving no treatment CTRL, ADT, or ADT +AA. a Principal component analysis (PCA)
plot of the microbiota from patient samples. Sequence variants were collapsed at genus-level identification, with clr-transformed Aitchison distances used
as input values for PCA analysis. The distance between points represent differences in microbiota composition. Strength of association for taxa are
depicted by the length of red arrows shown. Ellipses indicate 95% confidence intervals for each group. b,cPercent relative abundance of the two largest
influencers of microbiota separation based on treatment. Data represent the median (line in box), IQR (box), and minimum/maximum (whiskers) for CTRL
(n=33), ADT (n=21), and ADT +AA (n=14) patient samples. Statistics shown are derived from multivariate analysis performed using MaAsLin2
additive generalized linear models with log
10
clr-transformed input values. d,eALDEx2 strip charts showing differential abundances of taxa between
different patient groups. Positive values indicate increased relative abundance, while negative values indicate decreased relative abundance in the specified
treatment groups (ADT or ADT +AA) relative to the CTRL samples. Statistical analysis performed with ALDEx2 and MaAsLin2 software. Features are
colored red if ALDEx2 effect size differences (>1) and MaAsLin2 pvalue (<0.05) thresholds are exceeded, and blue if effect size difference (>2) and pvalue
(<0.05) thresholds are exceeded. **P=0.0030, ***p=0.0007, ****p< 0.0001, and n.s. =not significant.
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Content courtesy of Springer Nature, terms of use apply. Rights reserved
samples. These findings are clinically relevant and show that A.
muciniphila is capable of modulating a specific shift in bacterial
community dynamics in response to AA. Given that previous
literature has positively correlated A. muciniphila levels in the gut
with cancer treatment success1–3, we chose to investigate this
species further in hopes of elucidating potential ways to enhance
the therapeutic efficacy of AA.
Effects of AA are reproducible in a host-free gut model. Next,
to deconvolute the complexity of deciphering microbe–drug
interactions over time in the human body, we utilized a simulated
model of the human distal gut microbiota to simulate AA
exposure in the context of a stabilized polymicrobial community
within a host-free environment (Fig. 3). Following community
stabilization, we exposed the simulated gut model to AA (250 mg/
day) for six consecutive days. Initial AA exposure led to a stark
increase in A. muciniphila levels (>130-fold increase in relative
abundance within 24 h post AA exposure) that persisted over the
exposure period (peaking at >1000-fold increase in relative
abundance) and gradually decreased following AA withdrawal
(Fig. 3b). We performed a PCA on time-course collected samples
from the gut model (at genus-level ordination) and identified that
samples from the AA exposure period separated from the non-
exposure period on PC1 axis (22.6% explained variance), while all
samples indiscriminatory of AA exposure were spread out along
the PC2 axis (56.4% explained variance) in what appeared to be a
temporally driven drift in composition (Fig. 3c).
After adjusting for time as a confounder, gut model samples
from AA exposure days were found to be enriched with
Akkermansia,Klebsiella,Bilophila,Hungatella, and Oscillibacter
(Supplementary Table 3). Alternatively, AA exposure led to a
depletion in Faecalibacterium,Coprococcus, and unclassified
Lachnospiraceae group 3007. Corroborating these findings, a
co-occurrence network analysis of the gut model samples also
showed that AA exposure was positively correlated with
Akkermansia (which itself was connected to several important
hub genera) and negatively correlated very strongly with
Faecalibacterium,Coprococcus, and unclassified Lachnospiraceae
group ND3007 (Fig. 3f). Faecalibacterium and Coprococcus
specifically showed a near opposite response to AA compared
to A. muciniphila (Supplementary Fig. 3), which is interesting
given that they are both well-known butyrate producers15 and
that F. prausnitzii has been shown to positively interact with A.
muciniphila on multiple occassion16,17; future studies would
benefit from investigating the mechanism by which AA negatively
impacts butyrate-producing bacteria. These findings overlap
considerably with the ADT +AA patient microbiota dataset
and solidify the idea that AA can alter human microbial
communities independent of host factors (Supplementary Data 1,
Supplementary Table 3, Fig. 3a–d, and Supplementary Fig. 4).
Moreover, preferential growth status of A. muciniphila during
early AA exposure in the host-free gut model further supported
its chief regulatory role in the facilitation of AA-mediated
modulation of microbial communities.
AA uniquely promotes A. muciniphila growth in pure culture.
Next, we assessed bacterial metabolism of AA by growing A.
muciniphila and 17 other bacterial strains of interest (mostly
Enterobacteriaceae isolates showing a capacity to utilize AA as a
sole carbon sole; Supplementary Fig. 2E, Supplementary
Fig. 5A–E, and Supplementary Table 4) in media supplemented
with AA and then measured breakdown in culture supernatants
after 24 h growth. High-performance liquid chromatography
(HPLC) analysis confirmed that all strains tested were able to
breakdown ~70% of the AA present relative to the uninoculated
media control (Fig. 3e). However, subsequent growth assays
demonstrated that only A. muciniphila could derive a direct
growth advantage from AA in pure culture alongside other car-
bon sources, while all other strains showed either no effect or a
decreased growth potential (Fig. 3g, Supplementary Fig. 2, and
Supplementary Fig. 5). Supplementation of growth media with
sodium acetate at equimolar concentration elicited a near-
identical growth response in A. muciniphila, suggesting that the
acetate portion of AA was the responsible factor mediating
enhanced growth (Fig. 3h, i).
AA exposure correlates with predicted bacterial vitamin bio-
synthesis. Based on the evidence so far suggesting that A. muci-
niphila is privileged in receiving a growth benefit from AA, we
performed functional inferencing on 16S rRNA gene sequencing
datasets from PC patients and the simulated gut model to
determine how these interactions might impact overall bacterial
metagenome potential (Fig. 4). We found that out of 306 total
MetaCyc pathways predicted, glyoxylate cycle-related pathways
(which permit the bypassing of two decarboxylation steps in the
citric acid cycle and allow bacteria to grow on acetate via the
BHI + vehicle
BHI + vehicle
BHI + vehicle
BHI + vehicle
2
3
4
Shannon’s H index
40
50
60
70
Aitchison distance
Low Akkermansia (<0.1% relative abundance)
BHI + AA (100 µg/mL)
BHI + AA (100 µg/mL)
BHI + AA (100 µg/mL)
BHI + AA (100 µg/mL)
High Akkermansia ( 0.1% relative abundance)
ab
–2
–1
0
1
2
3
Akkermansia abundance
(normalized clr values)
Vehicle AA Vehicle AA
c
n.s.
n.s.
n.s.
p = 0.067 ***
*
Fig. 2 In vitro incubation of patient fecal samples with abiraterone acetate (AA). Freshly collected fecal samples from donors not receiving any form of
prostate cancer treatment (n=8) were transferred to BHI media supplemented with 100 μg/mL of abiraterone acetate (AA) or vehicle (EtOH). Samples
were incubated anaerobically at 37 °C for 48 h prior to 16S rRNA gene sequencing. aAlpha diversity was measured via Shannon’sHindex and bbeta
diversity was measured via Aitchison distance between samples within the same group. Data represent the median (line in box), IQR (box), and minimum/
maximum (whiskers) of n=4 low Akkermansia and n=4 high Akkermansia samples. Statistical comparisons shown for separate Wilcoxon’s matched-pairs
tests with multiple comparisons corrected using the Benjamini–Hochberg FDR method. cDifferences in Akkermansia abundances following incubation with
AA compared to vehicle. Data shown represent log
10
clr-transformed relative abundances normalized to the vehicle group for each sample. Statistics
shown are derived from differential abundance analysis using ALDEx2 software in R. ***P=0.0005, *p=0.0358, and n.s. =not significant.
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direct conversion to succinate18) and nearly all menaquinone
(MKn, where n=sidechain prenyl subunit length; collectively
referred to vitamin K2) biosynthesis-related pathways were con-
sistently increased in both AA-exposed gut model samples and
ADT +AA patient samples compared to relative controls
(Fig. 4a–i, Supplementary Fig. 6A, B, Supplementary Fig. 7,
Supplementary Table 5, and Supplementary Data 2–6). In addi-
tion, four pathways related to mycolic acid biosynthesis were
nearly undetectable in both ADT and ADT +AA patient samples
compared to controls (Supplementary Fig. 8).
Quinones (which can act as lipid-soluble electron carriers) have
recently been shown to facilitate syntrophic growth via
Media only
A. muciniphila
PCp1
PCp2
PCp3
PCp4
PCp5
PCp6
PCp7
PCp8
PCp9
PCp10
PCp11
0
50
100
AA remaining (%)
51015
0.0
0.2
0.4
0.000
0.002
0.004
Bacteria RA
Pathway RA
51015
0.0
0.1
0.2
0.3
0.000
0.001
0.002
0.003
Bacteria RA
Pathway RA
Vehicle
Acetate
AA
0.4
0.6
0.8
1.0
Vehicle
Acetate
AA
1.4
1.5
1.6
Vehicle
Acetate
AA
1.50
1.65
1.80
Vehicle
Acetate
AA
1.55
1.60
1.65
Klebsiella spp. Positive interaction
Negative interaction
Akkermansia
Klebsiella
A. muciniphila
DMK-8
MK-8
MK-11/12/13
Agathobacter
Akkermansia
Alistipes
Bacteroides
Bilophila
Faecalibacterium
Fusicatenibacter
Intestinimonas
ND3007
Negativicoccus
Parabacteroides
Ruminiclostridium
UCG-005
Ruminococcus
UBA1819
–4
0
4
8
–5 0 5 10PC1: 56.4%
PC2: 22.6%
Escherichia/Shigella
Blautia
Hungatella
Oscillibacter
Klebsiella
Coprococcus
–AA +AA
D12
D9
D15
D8
D1
D3
D4
D2
D7
D6
D5
AA exposure
qPCR validation
Culture-based
analysis
16S rRNA sequencing
Media
Effluent
Stabilization period (14 days) Experimental period (15 days)
Bioreactor
Donor
sample
ca
Remainder
ND3007 group
Fusicatenibacter
UCG-005 group
Coprococcus
Alistipes
Intestinimonas
Negativicoccus
R7 group
Clostridium SS1
Agathobacter
Ruminococcus
UBA1819
Oscillibacter
Ruminiclostridium
Unclassified
Bilophila
Stenotrophomonas
Subdoligranulum
Lachnoclostridium
Paraclostridium
Hungatella
Dialister
Escherichia/Shigella
Citrobacter
Parabacteroides
Blautia
Faecalibacterium
Klebsiella
Akkermansia
Bacteroides
0.0
0.2
0.4
0.6
0.8
1.0
16S rRNA fraction
Day: 1 2 3 4 5 6 7 89 12 15
51015
0
2
4
6
8
Log10 copies
of 16S rRNA
Total bacteria
A. muciniphila
Enterobacteriaceae
C. fruendii E. coli
0244872
0.0
0.5
1.0
0.0
0.5
1.0
A. muciniphila
DSM 22959
0.0
1.0
2.0
012240122401224
0 24 48 72 0 24 480122401224
0.0
1.0
2.0
0.0
1.0
2.0
0.0
1.0
2.0
K. michiganensis
PCp9
C. freundii
PCp1
E. coli
PCp4
0.0
1.0
2.0
0.0
1.0
2.0
Enterobacteriaceae
MK-6/9
DMK-6/9
b
de
f
g
j
k
hh hh
OD600
OD600
Acetate Vehicle
Day
AA
Vehicle
AA
h
Bacilli
Bacteroidia
Clostridia
Deltaproteobacteria
Negativicutes
Verrucomicrobiae
Gammaproteobacteria
Day
n.s.
i
k
Day
Faecalibacterium
Bacteroides
Lachnospiraceae ND3007
Oscillibacter
Parabacteroides
Paraclostridium
Coprococcus
Clostridium SS1
n.s.
n.s.
****
****
****
250 mg
30
AA
Washout
AA exposure
AA exposure in a host-free gut microbiota model
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interspecies electron transfer19 and encourage growth of
important symbionts in a homolog-specific manner20. Supporting
this, a near-perfect overlap was observed between MK7, MK8,
MK11, MK12, and MK13 biosynthesis pathways and relative
abundance of A. muciniphila over time in the simulated gut
model (Fig. 3j). In contrast, relative abundance of Enterobacter-
iaceae was better able to explain changes in MK6, MK9, and
MK10 biosynthesis pathways over time (Fig. 3k and Supplemen-
tary Data 6). A stratified breakdown of MKn pathway contribu-
tion showed that Bacteroidales was the top contributor in PC
patients irrespective of treatment group (Fig. 4j and Supplemen-
tary Data 7), which is consistent with recent reports from other
human cohorts and mammalian models21,22. However, Enter-
obacteriales and Verrucomicrobiales (solely represented by A.
muciniphila in this study) disproportionately contributed to the
increased pathway abundance of MK6-MK13 in ADT +AA
patients in the same homolog-specific pattern that was observed
in the simulated gut model during AA exposure (Figs. 3j and 4j
and Supplementary Data 8). Further supporting an AA-mediated
shift in metagenomic potential that preferentially supports the
growth of A. muciniphila, biosynthesis- and catabolism-related
pathways for L-threonine and UDP-N-acetyl-glucosamine (two
essential growth-promoting factors for A. muciniphila23) were
also favorably altered during AA exposure in the gut model
(Supplementary Fig. 7 and Supplementary Data 6).
Discussion
This study demonstrates that the oral PC treatment drug AA can
reproducibly modulate patient-associated GI microbial commu-
nities through promoting the growth of A. muciniphila. Notably,
this study: (i) provides insight into this microbiota–xenobiotic
interaction and the potential mechanism of cross-feeding between
various bacterial species that are clinically relevant to PC treat-
ments, and (ii) elucidates that bacterially mediated enrichment of
host vitamin K2 status might be an auxiliary mechanism by
which AA elicits its pro-survival effects in castrate-resistant PC
cohorts.
The unusually high levels of contact between AA and the
patient GI microbiota (due to poor absorption of the drug6)
provides rich insight on the effect of localized delivery of selective
bacterial growth agents. Specifically, our findings suggest that
colonic delivery of the conjugated acetate portion of AA is the
likely factor responsible for enrichment of A. muciniphila in PC
patients (Figs. 1c and 3g–i). The importance of site-specificity in
regard to exogenously delivered acetate is emphasized by past
work showing that distal, but not proximal, colonic acetate
infusions can improve metabolic disorders in overweight men24.
Supporting the observed linkage between acetate and A. muci-
niphila, short-chain fatty acid metabolism is thought to influence
A. muciniphila growth in the GI microbiota25. Serum acetate and
A. muciniphila abundance in the gut have also been shown to be
positively correlated5, although general consensus has long sug-
gested that A. muciniphila is responsible for this increase though
producing acetate itself. Nonetheless, acetate-producing Bifido-
bacterium animalis LMG P-28149 orally supplemented to mice
can increase fecal A. muciniphila abundance by over a 100-fold26
and engraftment of A. muciniphila in a simulated intestinal
mucosa environment is sustained alongside cointroduction with
mucin-colonizing and acetate-producing Bacteroides,Rumino-
coccus, and Coprococcus strains27. Thus, it may be that A. muci-
niphila capitalizes on the multifunctional glyoxylate pathway
(predicted to be higher in ADT +AA patients; Fig. 4a, b), which
is both required for mucin degradation28 and enables bacterial
growth on acetate via bypassing of the two decarboxylation steps
of the citric acid cycle18. In addition, A. muciniphila also pos-
sesses a functional cytochrome bd, which when experimentally
cloned into a cytochrome-deficient Escherichia coli mutant strain
shifted metabolism in favor of acetate and ultimately doubled the
maximum growth yield under oxygen limiting conditions29—
similar to what would be expected at the intestinal
lumen–epithelium interface30. Altogether with our findings, it
appears that high acetate concentrations can accelerate A. muci-
niphila growth by acting as a direct energy source as well as
triggering mucin degradation-related gene expression. While this
phenomenon remains uncharacterized in A. muciniphila, analo-
gous observations in Pseudomonas aeruginosa have demonstrated
the simultaneous liberation and consumption of acetate during
mucin degradation in the lung28.
The fact that various Enterobacteriaceae members did not
receive a growth benefit from AA or acetate in pure culture but
increased in the simulated gut model during AA exposure
(exhibiting an ~48 h lag in growth behind A. muciniphila) sug-
gests a potential for mutualistic cross-feeding—which in light of
differential menaquinone biosynthesis capabilities, supports pre-
vious findings of A. muciniphila requiring the exogenous co-
occurrence of complementary quinones (i.e., ubiquinone, mena-
quinones) due to an incomplete biosynthetic pathway31. More-
over, cleavage of host-derived mucin by various commensals
(including A. muciniphila) liberates fucose, which has been
shown to stimulate the growth of certain Enterobacteriaceae via
activation of their two-component sensing system, FusKR32.
Uniquely among mucin degraders though, A. muciniphila can
largely outcompete other bacteria for fucose in the gut and yield
1,2-propanediol from its metabolism33. Supporting this, A.
muciniphila contributed substantially to the predicted increase in
relative abundance of fucose degradation pathways in AA-
exposed gut model samples (Supplementary Fig. 7D, and Sup-
plementary Data 6 and 8). These findings suggest a scenario
whereby A. muciniphila supports the maintenance of Enter-
obacteriaceae growth and in return benefits from gaining access
Fig. 3 AA exposure promotes A. muciniphila in a simulated model of the human distal gut microbiota. a Simplified schematic providing a detailed
overview of experimental methodology. Servier Medical Art images were used and modified under the Creative Commons Attribution 3.0 Unported
License. bBar plot representing the microbiota compositions of gut model samples from before, during, and after AA exposure as determined by
sequencing of the V4 region of the bacterial 16S rRNA gene. cPCA plot of time-course collected gut model samples matching the days shown. Aitchison
distance of genus-level microbiota compositions were used as input values and strength of association for taxa are depicted by the length of arrows shown.
dqPCR-based quantification of total bacteria, Enterobacteriaceae, and A. muciniphila in the gut model over time. eRelative amount of AA remaining in
bacterial culture supernatants following 24 h incubation in 100 ppm. AA-supplemented media. Data shown represent the mean ± standard deviation (one-
way ANOVA with Sidak’s multiple comparisons) for n=3 biological replicates performed in technical triplicate for each bacterial strain. fCo-occurrence
network visually illustrating the significant interactions between taxa in the gut model. g,hRepresentative growth curves and icarrying capacity (k)of
bacteria in 0.25 mM AA and acetate-supplemented media. Data represent mean ± standard deviation (one-way ANOVA with Sidak’s multiple
comparisons) of n=3 biological replicates performed in technical triplicate for each bacterial strain. j,kTemporal overlay graphs showing A. muciniphila
and Enterobacteriaceae abundances in the gut model over time alongside predicted menaquinone (vitamin K2) biosynthesis-related pathway abundances.
RA =relative abundance. ****P< 0.0001 and n.s. =not significant.
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to a wider repertoire of quinones that cannot otherwise be syn-
thesized, but that are required for optimal growth20. This might
also attest to why A. muciniphila grows so poorly in pure culture
(Fig. 3g–i).
The outer membrane protein Amuc_1100 can blunt tumor-
igenesis via modulation of CD8+cytotoxic T lymphocytes4and is
likely the most well-supported component of A. muciniphila for
improving anti-PD1 immunotherapies. Here, we identify that A.
muciniphila-mediated orchestration of communal menaquinone
(i.e., vitamin K2) pools in the gut may represent an alternative
mechanism by which this keystone species impacts cancer
treatment outcomes. From the perspective of human biology,
51015
–
9
–
5
–
1
–4
–2
0
2
Negatively associated with AA treatment in PC patients
1
2
3
2
3
4
51015
1.2
1.6
2.0
2.4
51015
0.5
1.0
1.5
2.0
–2 –1
012
LDA effect size (log10)
L-methionine biosynthesis
L-cysteine biosynthesis
Menaquinol 8 biosynthesis I
Superpathway of ubiquinol 8 biosynthesis
Glyoxylate bypass
Assimilatory sulfate reduction I
2-carboxy-1,4-naphthoquinol biosynthesis
Menaquinol 7 biosynthesis
Phylloquinol biosynthesis
Menaquinol 11 biosynthesis
Menaquinol 12 biosynthesis
Menaquinol 13 biosynthesis
Ubiquinol 10 biosynthesis
Ubiquinol 7 biosynthesis
Ubiquinol 8 biosynthesis
Ubiquinol 9 biosynthesis
Heme b biosynthesis from glycine
Methylphosphonate degradation I
Demethylmenaquinol 8 biosynthesis I
Menaquinol 9 biosynthesis
Chorismate metabolism
Glycol metabolism and degradation
5-aminoimidazole biosynthesis I
5-aminoimidazole biosynthesis II
5-aminoimidazole biosynthesis superpathway
pyrimidine nucleobases salvage
UMP biosynthesis I
aminoacyl-tRNA charging
coenzyme A biosynthesis I
adenosine nucleotides biosynthesis II
inosine-5-phosphate biosynthesis I
adenosine nucleotides biosynthesis I
UDP N-acetylmuramoyl biosynthesis I
peptidoglycan biosynthesis I
peptidoglycan biosynthesis III
UDP N-acetylmuramoyl biosynthesis II
adenine and adenosine salvage III
S-adenosyl L-methionine cycle I
Calvin–Benson–Bassham cycle
pyruvate fermentation to acetate and lactate II
Positively associated with AA treatment in PC patients
Prostate cancer patients Simulated gut model
b
Glyoxylate bypass
(log
10
clr-transformed)
Ubiquinol biosynthesis
(log
10
clr-transformed)
S-adenosyl
L
-methionine
cyc l (log
10
clr-transformed)
Calvin–Benson–Bassham
(log
10
clr-transformed)
a f AA exposure
cg
dh
ei
Day
AA exposure
AA exposure
AA exposure
n.s.
51015
–2.5
–1.5
–0.5
n.s.
MK7 MK12 MK13
MK11MK10MK9MK8
CTRL
ADT
ADT + AA
CTRL
ADT
ADT + AA
CTRL
ADT
ADT + AA
CTRL
ADT
ADT + AA
CTRL
ADT
ADT + AA
CTRL
ADT
ADT + AA
CTRL
ADT
ADT + AA
CTRL
ADT
ADT + AA
0
4 × 10
–4
8 × 10
–4
1.2 × 10
–3
MK6
Relative pathway contribution
PWY-5899PWY-5898PWY-5896 PWY-5897PWY-5845PWY-5838PWY-5840PWY-5850
Bacteroidales Enterobacteriales Verrucomicrobiales Other
j
PWY-6121
PWY-6122
PWY-6277
PWY-7208
PWY-5686
TRNA-CHARGING-PWY
COA-PWY
PWY-6126
PWY-6123
PWY-7229
PWY-6387
PEPTIDOGLYCANSYN-PWY
PWY-6385
PWY-6386
PWY-6609
PWY-6151
CALVIN-PWY
PWY-5100
PWY-5345
SULFATE-CYS-PWY
PWY-5838
UBISYN-PWY
GLYOXYLATE-BYPASS
SO4ASSIM-PWY
PWY-5837
PWY-5840
PWY-5863
PWY-5897
PWY-5898
PWY-5899
PWY-5857
PWY-5855
PWY-6708
PWY-5856
PWY-5920
PWY0-1533
PWY-5861
PWY-5845
ALL-CHORISMATE-PWY
GLYCOL-GLYOXDEG-PWY
CTRL ADT ADT + AA
CTRL ADT ADT + AA
CTRL ADT ADT + AA
CTRL ADT A DT + AA
–
4
–
2
0
2
4
p = 0.0271
n.s.
n.s.
p = 0.0277
n.s. p = 0.0266
n.s. p = 0.0251
p = 0.0029
p = 0.0191
Fig. 4 Abiraterone acetate (AA) exerts a reproducible effect on the human gut microbiota in vivo and in vitro. a Differentially abundant pathways in
patients receiving AA compared to those who were not. Multiclass analysis with the LEfSe algorithm was used to discriminate between AA effects
overlapping systemic ADT exposure (LDA score >2 and p< 0.05 for all pathways shown). b–eRelative abundance of predicted pathways that were
statistically increased in AA-treated patients. Data represent the median (line in box), IQR (box), and minimum/maximum (whiskers) of CTRL (n=33),
ADT (n=21), and ADT +AA (n=14) patient samples. Statistics shown for Kruskal–Wallis tests with multiple comparisons corrected using the
Benjamini–Hochberg FDR method. f–iRelative abundance of predicted pathways in AA-exposed gut model samples. jRelevant bacterial contribution to
each menaquinone biosynthesis pathway. Predicted pathways were inferenced using an exact amplicon sequence variant approach with the
PICRUSt2 software and annotated using the MetaCyc metabolic pathway database. n.s. =not significant.
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vitamin K2 (dietary-derived or absorbed via microbial biosynth-
esis in the gut) acts as a ligand of the nuclear pregnane X receptor
(also known as the steroid and xenobiotic sensing nuclear
receptor, SXR, or NR1I2) and performs critical roles in blood
coagulation, bone metabolism, and regulation of blood calcium
levels and soft tissue calcification34. Interestingly, vitamin K2 is
also a prospective anti-cancer agent that can target castrate-
resistant PC in vitro35, inhibit both androgen-dependent and
androgen-independent tumor growth in mice36, and is inversely
associated with PC based on dietary intake results from a large
European prospective study37. In-depth discussion on the phy-
siological role of vitamin K2 is outside the scope of this study,
although soft tissue calcification (a well-known consequence of
vitamin K2 insufficiency) is frequently observed in a variety of
diseased prostate specimens and is strongly correlated with PC38.
Notably, Corynebacterium spp. (shown to be higher in control
patients not receiving treatment; Fig. 1b) are expert colonizers of
these calcified surfaces39, more abundant in the urogenital tract of
PC patients40, and found at higher concentrations in prostate
tumor and peri-tumor samples compared to non-tumor prostate
tissue41. Coryneform bacteria are classically defined by the pre-
sence of unique α-branched β-hydroxy fatty acids called mycolic
acids42 (predicted biosynthesis pathways shown to be nearly
abolished in both ADT and ADT +AA patients; Supplementary
Fig. 8 and Supplementary Data 2 and 3) in their cell wall, which
can evoke interleukin-23 signaling43—a key cytokine thought to
contribute to tumorigenesis and progression to metastatic disease
in the context of inflammation-related cancers44. While no clear
etiological relationship has been established between Cor-
ynebacterium spp. and PC, it is interesting to speculate how
androgen-inhibiting treatments as well as A. muciniphila-medi-
ated modulation of quinone signaling and host vitamin K2 status
may influence colonization of these common but potentially
problematic bacterial species.
The seminal findings that steroidal progesterone derivate AA
could inhibit CYP17A and thereby improve PC patient outcome
in castrate-resistant cohorts has led to the widely accepted con-
sensus that castrate-resistant PC remains a hormonally driven
process45. Our results suggest that the efficacy of AA may be
imparted through its ability to increase microbially synthesized
vitamin K2 in PC patients via specific interactions with the key
symbiont, A. muciniphila. Altogether, these findings expand on
previous work from several other large studies that have
demonstrated A. muciniphila to invoke a multiplicity of health
benefits in their hosts and improve the efficacy of checkpoint
inhibitor cancer immunotherapies1–3. We note that the predic-
tion of underlying metabolic pathways was inferenced using an
exact amplicon sequence variant (ASV) approach and that our
findings should be further evaluated in a longitudinal interven-
tion study design—preferably in conjunction with A. muciniphila
and vitamin K2 treatment groups. Moreover, the direct inhibitory
effects that abiraterone has on several human-derived steroid-
metabolizing bacteria, as was identified in vitro in this study, also
warrants further investigation.
Methods
Study design. Patients were recruited as a sub-study of a Canadian Observational
Study in Metastatic Cancer of the Prostate: a study of ZYTIGA use in the com-
munity urology setting (COSMiC; NCT02364531). The three PC patient groups
examined included (1) patients not receiving any active treatment, (2) patients
receiving ADT alone, and (3) patients receiving both ADT and treatment with
orally administered AA. Additional information about the patients was collected,
including age, body mass index, antibiotic use within 3 months, bicalutamide use,
metformin use, oral steroid use, PSA level, radiation therapy exposure to pelvis,
and whether or not they had metastatic disease (Supplementary Table 1). This
study was approved by the Western University Research Ethics Board (IRB
00000940) and all participants (i.e., control PC patients, PC patients receiving
ADT, PC patients receiving ADT +AA, and the healthy donor which provided a
sample for chemostat inoculation) were informed about the purpose of the study
and signed a consent form prior to collection. Clinical samples were collected at
Victoria Hospital (London, Ontario, Canada) between 2017 and 2018. Gloves used
for digital rectal examinations of PC patients were collected and stored at 4 °C
immediately afterwards for 1–2 days (a convenient storage period and temperature
expected to be comparable to immediate freezing46). Subsequently, fecal matter was
aseptically transferred from each glove using a single sterile polyester tipped swab
(BD, Franklin Lakes, NJ) and stored in a 1.5 mL RNase/DNase-free micro-
centrifuge tube at −80 °C until DNA extraction.
In vitro incubation of PC patient samples with AA. A standardized inoculum of
freshly collected fecal matter from male donors not receiving any form of PC
treatment were homogenized in BHI media (catalog number: B11059, BD Difco)
with the addition of 0.25 mM AA or vehicle (EtOH). After being incubated
anaerobically at 37 °C for 24h, debris was removed, and bacteria was harvested by
centrifugation at 5000 × gfor 10 min and then stored at −80 °C until DNA
extraction.
Simulated model of the human gut microbiota. The effect of AA exposure on
human gut-associated bacterial communities was evaluated in a simulated human
gut microbiota model using a Bioflo 110 Bioreactor (New Brunswick Scientific, NJ).
Briefly, the bioreactor unit (4 L) was inoculated with freshly collected fecal matter
from a healthy male donor and was allowed to stabilize over a period of 14 days
prior to experimentation. An anaerobic state was maintained under continuous
flow of nitrogen gas, a pH of 6.8 was maintained through daily titration with 0.5 M
NaOH, and a constant input of established growth media relevant to the human
intestinal environment was provided based on an established protocol for che-
mostat models of the human distal gut47. Following stabilization, one tablet of
ZYTIGA (containing 250 mg AA) was dissolved in 5 mL ethanol and administered
daily for several days, followed by a washout period (Fig. 3a). Effluent from the
bioreactor was collected before, during, and after AA exposure and stored at −80 °
C until DNA extraction.
DNA extraction and 16S rRNA gene library preparation. Patient rectal swabs,
bioreactor effluent from the gut model, and in vitro incubated rectal samples were
placed directly into the wells of a 96-well DNeasy PowerSoil HTP 96 kit (Qiagen)
and the DNA extraction protocol was followed as per the manufacturer’s
instructions. Extracted DNA template was then transferred to a 96-well PCR plate.
Targeted amplification of the 16S rRNA V4 region was performed using the
established GOLAY-barcoded primers (5′–3′) ACACTCTTTCCCTACACGACG
CTCTTCCGATCTNNNNxxxxxxxxxxxxGTGCCAGCMGCCGCGGTAA and
(5′–3′) CGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCTNNNNxxxxxx
xxxxxxGGACTACHVGGGTWTCTAAT, wherein “xxxxxxxxxxxx”represents the
sample-specific 12-mer barcode following the Illumina adaptor sequence used for
downstream library construction48. Utilizing a BioMek Automated Workstation
(Beckman Coulter), 2 µL of sample DNA (5 ng/µL) was added to a 96-well 0.2-mL
PCR plate containing 10 µL of each primer per well (3.2 pmol/µL), followed by the
addition of 20 µL of GoTaq 2X Colorless Master Mix (Promega). Final plates were
then sealed using PCR-grade adhesive aluminum foil and placed in a Prime
Thermal Cycler (Technie). PCR reaction conditions were as follows: 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 subsequently stored at −20 °C until further processing.
16S rRNA sequencing and data analysis. Processing of amplicon libraries was
conducted at the London Regional Genomics Centre (Robarts Research Institute,
London, Canada) in which amplicons were quantified using PicoGreen (Quant-It;
Life Technologies, Burlington, ON), 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 sequence variants from amplicon
data49. Patient rectal swab sample sequence reads were filtered (reads truncated
after a quality score of ≤2 and forward/reverse reads truncated after 183/174 bases,
respectively), de-replicated, de-noised, and merged using DADA2 default para-
meters with read recovery rates ranging from 72.3 to 93.8%. For the gut model and
in vitro patient incubation samples, 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 with read recovery rates
ranging from 88.3% to 91.8% for gut model samples and from 49.7% to 93.1% for
incubated patient samples. Taxonomy was assigned to sequence variants using the
SILVA non-redundant v132 training set. Contaminating taxa were identified and
removed using frequency and prevalence methods in the decontam R package
(v1.1.2) with PCR and environmental blanks as control samples. Outliers were
identified within groups using the “codaSeq.outlier”function in the CoDaSeq R
package (v0.99.4).
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Determination of diversity and differentially abundant taxa. Pre-processing of
sequence variant count matrices consisted of the following: zero imputation using
the “cmultRepl”function (method =“CZM”) in the zCompositions R package
(v1.3.2-1), applying a per sample minimum abundance cutoff of 1%, and center
log-ratio (clr) transformation of values using the “clr”method in the compositions
R package. PCA was performed on genus-collapsed datasets with sample Aitchison
distances (clr-transformed Euclidian distance) as input values using the “prcomp”
function in R. Alpha and beta diversities for each sample were determined by
calculating Shannon’sHindex and Aitchison’s distance (within and between
samples), respectively, using the QIIME2 software50. Statistical differences in alpha
and beta diversities were determined by Kruskal–Wallis tests with Dunn’s multiple
comparisons. Differential abundance of genera were compared with the ALDEx2
tool51, which uses clr-transformed posterior distribution of data generated from
128 Dirichlet Monte-Carlo instances to determine significantly different features
within a compositional dataset52. Wilcoxon’s rank-sum tests were used, followed
by Benjamini–Hochberg false discovery rate (FDR) correction to identify genera
that were significantly differed in relative abundance between groups using the
“aldex.ttest”function of ALDEx2. In addition, effect size of differentially abundant
taxa was calculated with the “aldex.effect”function of ALDEx2 since effect size
measures have been shown to be more reproducible than pvalues53.
Correlations between metadata and microbiota variation.Influencers of patient
microbiota variation were identified by calculating the association between clinical
metadata variables and genus-level community ordination (PCA based on clr-
tranformed Euclidian distance between samples) using the envfitfunction in the
vegan R package (v2.5-6). Multivariate analysis of variance and linear correlations
were performed on categorical and continuous variables, respectively, using the
envfitfunction (999 permutation, significance set at FDR < 5%). Multivariate
association testing was then performed to identify unique genus–covariate asso-
ciations while de-confounding the effect of all other covariates. Patient features
identified as significant covariates of microbiota variation (ADT, AA, and gluco-
corticoid usage) were used as predictor variables in boosted additive generalized
linear models using the MaAsLin2 (v0.99.1) R package. Genus-level abundances
were clr-transformed, a minimum abundance cutoff of 1% was applied during pre-
processing steps, and multiple comparisons were corrected with Benjamini and
Hochberg FDR method.
Co-occurrence network analysis of AA-exposed samples. Genus-level correla-
tions between taxa and AA exposure in the gut model were assessed by con-
structing a co-occurrence network using the CoNet software54. Briefly, ensemble
inferencing with Pearson’s and Spearman’s correlations, Bray–Curtis dissimilarity,
Kullback–Leibler divergence, and mutual information was used to create an initial
association network. Subsequently, the edgeScores randomization function was
used to perform 100 row-wise permutations with the 1000 highest and lowest
scoring edges retained. The ReBoot renormalization function was then applied to
address compositional bias and a merged final network was then constructed based
on the score distribution of 100 bootstrap iterations. Significance was calculated
using the Brown’s method and multiple comparisons were corrected with the
Benjamini–Hochberg FDR method. Edges that exceeded an adjusted FDR
threshold of 1% were discarded. The network was visualized using an organic
layout in Cytoscape (v3.7.2).
Inferencing of microbial metagenomes. Functional potential of microbial com-
munities was determined by inferencing gene content from taxonomic abundances
using an exact sequence variant approach with updated PICRUSt2 software.
DADA2 processed ASVs were placed into a reference multiple-sequence alignment
followed by sequence placement with the reference phylogeny database using the
“q2-fragment-insertion plugin”in QIIME2. Subsequently, hidden-state predic-
tions55 were performed using the recommended maximum parsimony approach to
predict gene family abundances and nearest-sequenced taxon index (NSTI) values
were calculated. NSTI values are a metric used to identify ASVs that are highly
distant from those that are available in the reference sequence database; high NSTI
values are generally indicative of uncharacterized phyla or off-target sequences and
are less informative. All ASVs from both patient and gut model samples were below
the NSTI < 2.0 cutoff recommended for a high level of confidence in predictions.
Unstratified metagenome predictions for EC numbers were normalized to 16S
rRNA copies, regrouped to MetaCyc RXNs using the default mapping file, and then
metabolic pathway abundances were inferenced with the MetaCyc pathway data-
base56. The LEfSe algorithm was used with subclass analysis to discriminate
between differentially abundant pathway associations with AA treatment and
overlapping effects of concurrent ADT (combined LDA scores >2 and alpha values
of <0.05 for factorial Kruskal–Wallis and pairwise Wilcoxon’s tests were considered
significant). In addition, effect size comparisons of differentially abundant path-
ways between individual patient groups were calculated using the
ADLEx2 software51.
Quantitative PCR (qPCR)-based verification of bacterial loads. Bacterial
abundances of interest were confirmed by performing qPCR on DNA extracted
from effluent of the gut model and patient fecal samples using established genus-
and species-specific primer sets (Supplementary Table 6). Each 10 µl reaction was
performed in technical triplicate using 2× SYBR Green PCR Master Mix (Applied
BioSystems) following the manufacturer’s instructions. Thermocycling conditions
were as follows: 50 °C for 2 min, a cycle of 95 °C for 10 min, 40 cycles of 95 °C for
15 s, and 60 °C for 1 min. Subsequently , a dissociation melt-curve analysis was
performed to assess amplification specificity: 95 °C for 15 s, 60 °C for 15 s, 0.075 °C/
s increment increases in temperature until 95 °C. All qPCR reactions were per-
formed in 384-well microplates on a QuantStudio 5 Real-Time PCR System
(Applied Biosystems) and data were analyzed using the QuantStudio Design &
Analysis Software v1.4 (Thermo Fisher Scientific).
Bacterial growth assays.Akkermansia muciniphila-type strain Muc (DSM 22959)
was routinely cultured anaerobically (GasPak EZ container system; BD) at 37 °C
using BHI (catalog number: B11059, BD Difco) supplemented with 5 μg/mL hemin
(catalog number A11165; Alfa Aesar), 0. 1 μg/mL vitamin K (catalog number 460-
027-G005; Enzo), 0.125% [w/v] mucin (catalog number M2378; Sigma), and 0.5%
[w/v] yeast extract (catalog number 212750; BD Difco, BHIS-YE). Escherichia coli
O6:H1 CFT073 (ATCC 700928), E. coli 6757,E. coli O18:K1:H7 UTI8958, and the
bacterial isolates derived from rectal swabs or the gut model (Supplementary
Table 4) were routinely cultured at 37 °C under microaerophilic conditions. To
determine the effect of AA on bacterial growth, freshly grown single colonies of
bacteria were transferred to BHI-YE broth supplemented with either 0.25 mM AA,
0.25 mM sodium acetate, or vehicle (EtOH). For each strain, a minimum of three
separate colonies were used with each colony subsequently being transferred in
technical triplicate to a 96-well microplate (catalog no. 351177, Falcon) and
incubated at 37 °C for 48–72 h. Growth was monitored intermittently by measuring
optical density (OD) at a wavelength of 600 nm using a BioTek Microplate Spec-
trophotometer (Agilent). Summarization of growth characteristics and statistical
analyses were performed using the growthcurver package (v0.3.0) in R59.
Determination of acetate utilization. Bacterial strains isolated from the gut model
(Supplementary Table 4) were qualitatively categorized based on their ability to utilize
AA or sodium acetate as a sole carbon source (RCA +designation) or not (RCA −
designation). Following an established protocol60, bacterial isolates were streaked onto
colorimetric selective media agar containing only sodium acetate as a sole carbon. A
modified version of this assay, in which sodium acetate was replaced by an equimolar
amount of AA, was also utilized to determine if the bacterial isolates could liberate
bioavailable acetate from AA. Inoculated agar plates were then incubated under
microaerophilic conditions at 37 °C and color change was evaluated at 24 and 48 h.
Acetate utilization was also quantitively assessed for several strains of interest using
M9 minimal media61 containing 90.2 μMNa
2
HPO
4
, 22. 0 μMKH
2
PO
4
,8.6μMNaCl,
9.3 μMNH
4
Cl, 2 mM MgSO
4
,and0.1mMCaCl
2
. Overnight bacterial cultures grown
aerobically in Luria-Bertani (catalog number: DF0446173, BD Difco) at 37 °C were
washed thrice with 0.01M phosphate-buffered saline, resuspended in M9 media
supplemented with 0.25 mM AA orvehicle (EtOH), and then sub-cultured (1:40) into
96-well microplates (catalog no. 35177, Falcon). Plates were then incubated at 37 °C
for 24 h in a BioTek Microplate Spectrophotometer (Agilent) with OD measurements
at 600 nm taken every 30 min. Summarization of growth characteristics and statistical
analyses were performed using the growthcurver package in R and GraphPad Prism
(v8.3).
Bacterial degradation of AA. HPLC was used to quantify breakdown of AA in
bacterial cultures. Briefly, bacterial strains of interest were grown overnight in
media supplemented with 100 p.p.m. AA and then centrifuged (5,000 × g)for10
min to obtain cell-free supernatant. Protein crash was performed by adding one
volume of supernatant to one volume of HPLC-grade acetonitrile (catalog no.
A996-4, Fisher), incubated at 4 °C for 15 min, and then centrifuged for 10 min
(16,000 × g) at 4 °C. All samples were filtered using a 0.45 μmfilter prior to
HPLC analysis. AA (catalog no. SML1527, Sigma) standards were made in
acetonitrile. All samples were analyzed using an Agilent 1100 HPLC instrument
equipped with a degasser (G1379A), quaternary pump (G1311A), autosampler
(G1313A), and diode array detector (G1315B). Samples were run on an Agilent
Poroshell 120 EC-C18 ( 4.6 × 150 mm2,4μm particle size) column with a
Poroshell 120 EC-C18 (4.6 mm, 4 μm particle size) guard column kept at
ambient temperature. The mobile phase consisted of an isocratic mixture of
acetonitrile and HPLC-grade water (catalog no. W5-4, Fisher) (80:20 [v/v]) at a
flowrateof1.2mL/min.Thesampleinjectionwas10μL, and detection was
performed at 254 nm. Run times were 10 min, with AA having a retention time
of ~8.0 min. Data were analyzed using ChemStation A.10.02. The peak area of
the samples was compared with the peak area of the external calibration curve to
quantify AA.
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
Raw sequence reads were uploaded to the NCBI Sequence Read Archive and are
accessible under BioProject ID PRJNA609050. All other remaining relevant data are
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NATURE COMMUNICATIONS | (2020)11:4822 | https://doi.org/10.1038/s41467- 020-18649-5 | www.nature.com/naturecommunications 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved
provided in the article, Supplementary information, or available from the corresponding
author upon reasonable request.
Received: 7 April 2020; Accepted: 2 September 2020;
Published online: 24 September 2020
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Acknowledgements
This study was in part funded by the W. Garfield Weston Foundation and by a Canadian
Urologic Oncology Group (CUOG) grant. Abiraterone acetate was kindly provided by
Janssen Pharmaceuticals.
Author contributions
J.P.B., J.C., S.M.N., M.D., A.A., and G.R. conceived the study design. K.A.-R., A.A., M.D.,
S.M.N., and J.C. contributed to patient sample collection. B.A.D., R.M.C., K.A.-R., K.F.A.,
S.G., J.A.C., and H.W. performed laboratory experiments. B.A.D. and K.A.-R. drafted the
manuscript and performed bioinformatic analyses. B.A.D., K.F.A., and J.P.B. contributed
to figure preparation and interpretation of the data. All authors read, revised, and
approved the final manuscript.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41467-
020-18649-5.
Correspondence and requests for materials should be addressed to J.P.B.
Peer review information Nature Communications thanks the anonymous reviewers for
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