Morphine induces changes in the gut microbiome and metabolome in a morphine dependence model
Scientific REPORTS |
Morphine induces changes in the gut microbiome and metabolome in a morphine dependence model
OPEN Opioid analgesics are frequently prescribed in the United States and worldwide. However, serious comorbidities, such as dependence, tolerance, immunosuppression and gastrointestinal disorders limit their long-term use. In the current study, a morphine-murine model was used to investigate the role of the gut microbiome and metabolome as a potential mechanism contributing to the negative consequences associated with opioid use. Results reveal a significant shift in the gut microbiome and metabolome within one day following morphine treatment compared to that observed after placebo. Morphine-induced gut microbial dysbiosis exhibited distinct characteristic signatures, including significant increase in communities associated with pathogenic function, decrease in communities associated with stress tolerance and significant impairment in bile acids and morphine-3-glucuronide/ morphine biotransformation in the gut. Moreover, expansion of Enterococcus faecalis was strongly correlated with gut dysbiosis following morphine treatment, and alterations in deoxycholic acid (DCA) and phosphatidylethanolamines (PEs) were associated with opioid-induced metabolomic changes. Collectively, these results indicate that morphine induced distinct alterations in the gut microbiome and metabolome, contributing to negative consequences associated with opioid use. Therapeutics directed at maintaining microbiome homeostasis during opioid use may reduce the comorbidities associated with opioid use for pain management.
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Morphine is the gold standard for pain management. Opioid analgesics are frequently prescribed in the United
States and worldwide1. However, serious side effects, such as addiction, analgesic tolerance, immunosuppression
and gastrointestinal (GI) symptoms limit their use1?3. Peripheral consequences of opioid use, such as
constipation, nausea, vomiting, bloating and gut barrier dysfunction have been well-documented4. More recently,
alteration in the gut microbiome has been implicated in opioid-induced bowel dysfunction and gut barrier disruption5.
However, the mechanisms underlying morphine-induced dysbiosis are still unknown. The recent rapid progress
in metagenomics has provided powerful tools to determine if perturbation of the human microbiome
contributes towards disease6. Changes in the composition or density of the microbiota are associated with increased
susceptibility to a variety of pathogens and abnormal mucosal immune responses7,8. Taxonomic and functional
diversity of gut microbiota is crucial in conferring resilience in gut homeostasis9. Low microbial diversity
correlates with obesity10,11, inflammatory bowel disease (IBD)12, and recurrent Clostridium difficile-associated
diarrhea (CDAD)13. It is still unclear if there is a direct cause-consequence relationship between microbial diversity
and resilience. Consistent with other reports, we have demonstrated that morphine disrupts intestinal barrier
function and induces bacterial translocation in mice14,15. Use of opioids is associated with an increased risk of
C. difficile infection16. We recently demonstrated that morphine inhibition of endotoxin tolerance, leading to
sustained sepsis, is mediated by modulation of miR-146a17, and opioid exacerbation of gram-positive sepsis is
rescued by IL-17A neutralization15. Our recent study demonstrated that opioid-induced gut microbial disruption
and bile acid dysregulation leads to gut barrier compromise and sustained systemic inflammation18. It is not yet
clear whether morphine treatment perturbs gut microbial homeostasis, resulting in increased growth of potential
pathogenic bacteria in the gut. Several studies are cross-sectional and do not have the ability to examine changes
over time with repeated biosample measurements. To date, there are no reports examining the association of
opioid treatment with dynamic changes in the microbiome and metabolome in a short-term study.
There is evidence suggesting that metabolites in the gut play a significant role in the crosstalk between gut
microbes and host biological functions, such as maturation of the host immune system19 and protection against
pathogens20. Disruption in bile acid metabolism is associated with increased susceptibility to C. difficile
infection21. Gastrointestinal barrier function may be regulated by intestinal symbiotic bacterial metabolites through
xenobiotic sensor PXR-dependent TLR4 signaling22. Healthy microbiota produce short chain fatty acids (SCFAs)
as carbon sources for the host, synthesize vitamins and essential amino acids, transform bile acids, produce
neurotransmitters and modify xenobiotics23. Metabolomic analysis allow for the determination and identification of
small molecular metabolites within the gut lumen, thus profiling the functional status of the gut microbiome24.
We have confirmed that morphine-induced intestinal barrier dysfunction contributes to bacterial translocation14.
However, it is still unknown how morphine treatment modulates the composition and abundance of gut
metabolites. The current study, therefore, is focused on identification of distinctness in the morphine-modulated gut
microbiome and its functional consequences through metabolomic analysis.
The host-microbiome metabolic interaction affects xenobiotic metabolism significantly25. Clayton,
et al., demonstrated that the host-gut microbial metabolic interaction leads to modifications of major
xenobiotic-metabolizing cytochrome enzymes and alteration of bile acid metabolites25,26. Morphine is
metabolized primarily through glucuronidation, biotransforming to morphine 3-glucuronide (M3G) and morphine
6-glucuronide (M6G) in the liver27. Though M3G exhibits no analgesic effect, M6G is more potent than
morphine28. M6G and M3G are hydrolyzed by ?-glucuronidase, synthesized by both intestinal mucosal cells and gut
bacteria, and subsequently reabsorbed as morphine29?31. Our recent study demonstrated that glucocorticoids can
significantly augment morphine-withdrawal-induced immunomodulation32. However, the role of the gut
microbiome in morphine metabolism and elimination is relatively unknown.
The aim of the present study is to evaluate the effect of morphine use or abuse on the temporal shift in the gut
microbiome and its consequence on the metabolome in a short-term study. The composition of the gut microbiome
and its metabolomic functions were determined at day 0, day 1, day 2, day 3, day 4, day 5 and day 6 following
morphine treatment. Our results demonstrate that morphine treatment induces a decrease in the diversity of the
microbial community and leads to distinct clustering and profiling of the gut microbiome and metabolome when
compared to the observations in placebo-treated mice. We hypothesized that expansion of Enterococcus faecalis
is a distinct feature associated with opioid-induced gut microbiome alteration and alterations in deoxycholic
acid (DCA) and phosphatidylethanolamines (PE) are associated with opioid-induced metabolomic changes. We
demonstrated that E. faecalis augmented tolerance of morphine analgesic effect in mice. Moreover, we determined
an increase in the morphine-3-glucuronide/morphine (M3G/MS) ratio in the gut, suggesting reduced conversion
of M3G by deconjugating microbes to morphine. These results suggest that morphine-induced changes in the
gut microbiome have implications in the enterohepatic recirculation of morphine and, thereby, its efficacy as an
analgesic agent. In addition, we demonstrate a cross-correlation and an association between reduced intestinal
bacterial communities and bile acid metabolism. Collectively, these results demonstrate that morphine induces
a distinct alteration in the gut microbiome and metabolome contributing to opioid-induced pathogenesis and
disrupted morphine pharmacokinetics.
Morphine treatment leads to temporal modulation of the gut microbiome. Morphine treatment
has been demonstrated, in our laboratory and also in human patients, to induce bacterial translocation on day
2 post-treatment2,14,33. It has been demonstrated that microbial dysbiosis contributes to bowel dysfunction and
susceptibility to infectious diseases5,34. To determine the effect of morphine treatment on the gut microbial
profile, we analyzed the gut microbiome based on Illumina sequencing of intestinal microbial 16S-rRNA genes.
Interestingly, opioid-induced adverse effects, such as constipation, are observed in patients within 1 day
following morphine administration35. At day 1 post-treatment, mice receiving morphine revealed bacterial
translocation into the mesenteric lymph node (MLN) and liver14. To determine time-dependent changes in the microbial
composition following subcutaneous morphine pellet implantation, fecal samples were collected from the same
animal at day 0, day 1, day 2 and day 3 following morphine treatment. Control animals were implanted
subcutaneously with a placebo pellet. Our time course study revealed distinct modulation of the gut microbiome by
morphine (Fig.?1). Principal coordinate analysis of fecal samples from day 0, day 1, day 2, and day 3 post-morphine
treatment demonstrated that the microbial profiles at day 0 in all treatment groups were similar and there was
no distinct clustering. However, as early as day 1, the microbiome from the morphine treatment group clustered
distinctly from all other groups and the clustering was more pronounced, with all samples clustering more tightly,
at day 3 post-morphine treatment. We expanded the study to 6 days post-treatment (Fig.?2). Data from day 4, day
5 and day 6 revealed the same trend as that of the 3-day experiment.
To determine the role of opioid receptors in morphine-induced effects, naltrexone?an opioid receptor
antagonist?was implanted subcutaneously in mice to determine whether naltrexone antagonizes morphine-induced
shifts in the gut microbiome. Results demonstrated that naltrexone antagonized morphine-induced alterations of
the gut microbiome (Figs?1 and 2). Interestingly, animals treated with naltrexone alone clustered distinctly from
the placebo group at day 3 following implantation, suggesting that endogenous opioids may set a basal tone on
the host microbial profile (Fig.?1D).
To compare microbial patterns, principal coordinate analysis (PCoA) was used. Principal coordinate analysis
(PCoA = multidimensional scaling, MDS) is a method to explore and to visualize inter-object
similarity/dissimilarity in a low-dimensional, Euclidean space. PCoA of unweighted UniFrac phylogenetic distances between
microbial communities were carried out using this program with observation ID level. A 3D plot, based on
unweighted UniFrac distance matrices obtained from the sequences at OTU level with 97% similarity,
demonstrated a distinct clustering of the community composition between the morphine- and placebo-treated groups.
By using the chao1 index to evaluate alpha diversity (diversity within a group) and using unweighted UniFrac
distance to evaluate beta diversity (diversity between groups, comparing microbial community based on
compositional structures), we observed that morphine treatment resulted in a significant decrease in alpha
diversity (Fig.?3A,B) and shift in fecal microbiome at day 3 post-treatment compared to that after placebo treatment
(Fig.?3C,D). Taxonomical analysis demonstrated that morphine treatment resulted in a significant increase in the
potential pathogenic bacterial community. Multiple hypothesis test with the given threshold of an estimate of false
discovery rate (q-value < 0.05) demonstrated that the relative abundance of potential pathogenic bacteria (genus
level) increased significantly at day 3 post-treatment with morphine compared to that after placebo treatment
(Fig.?3E and Supplementary Table?S1). Representatives of potential pathogenic bacteria with an increased
abundance post-morphine treatment at the genus level, include Flavobacterium, Enterococcus, Fusobacterium,
Identification of association between gut microbial dysbiosis and morphine treatment at the
species level. To identify association between microbiome alteration and morphine treatment at the species
level, we conducted expression profiling of selected species-specific 16SrRNA genes in the gut microbiota using
quantitative real-time PCR. Our results demonstrate a significant expansion in the Enterococcus faecalis species
in the morphine-treated group (Fig.?4A) with E. faecalis 16S-rRNA gene amplification 100 times greater in the
morphine-treated group compared to the levels in the placebo-treated group on day 3 post-treatment (Fig.?4B).
The effect of morphine on E. faecalis expansion was observed as early as day 1 in morphine-treated animals and
sustained through all days tested post-treatment (Fig.?4C). The effect of morphine on E. faecalis abundance in the
gut microbiome was antagonized by naltrexone treatment (Fig.?4B,C).
We next determined if infection with E. faecalis accelerates morphine analgesic tolerance in a tail flick test.
Withdrawal latencies of the tail from a radiant heat source were measured by tail flick. We demonstrate that
infection with E. faecalis augmented morphine analgesic tolerance compared to mice treated with morphine
alone (Fig.?5A,B). Mice, who received repeated dose of morphine, presented 50% MPE% between day 5 and day
6 in the tail flick test. However, tolerance to morphine induced analgesia was observed in mice infected with E.
faecalis in the presence of repeated morphine injections at day 2 compared to non-infected mice which developed
tolerance at day 4 post morphine injection. The 50% MPE% occurred between day 4 and day 5 in mice infected
with E. faecalis, who achieved 50% MPE% one day earlier than mice given PBS. Interestingly, the baseline
withdrawal latency in mice infected with E. faecalis was lower than that of mice treated with PBS indicating that mice
infected with E. faecalis were more sensitive to pain than uninfected mice. Based on these results, we propose
that expansion of E. faecalis can be used as a potential biomarker of gut dysbiosis following morphine treatment.
Morphine changes gut metabolomic profile gradually and shifts metabolites differentially.
Microbial dysbiosis leading to disruption in host-microbe homeostasis does not only result in alteration of the
microbial composition, but, more importantly, also disrupts the functional configuration of the microbiota36.
Intestinal microbiota metabolizes input substrates from the host, including diet and xenobiotics, into metabolites
that may affect the host37. Although gut microbiome may vary in different individuals, the core functional
metabolic interaction with gut microbiota is essential for the host and alteration of the core functional microbiome is
associated with different physiological states38. Thus, the identification of gut metabolomic profile is fundamental
to identifying the functional changes in the gut microbiome. To explore changes in the metabolomic profile
induced by morphine treatment, a time course study was carried out, as described earlier. Fecal samples were
collected from the same animal at day 0, day 1, day 2 and day 3 post-treatment and were analyzed using liquid
chromatography-mass spectrometry (LC-MS). Gut metabolomic analysis was performed on the same fecal
samples as those that were used for microbial analysis. Scores scatter plots of the partial least square discriminant
analysis (PLS-DA) model of fecal samples were constructed for morphine- or placebo-treated samples (Fig.?6A).
The results demonstrated that morphine treatment induced a distinct gut metabolomic profile compared to that
observed in the placebo-treated group.
To identify metabolites contributing to differences in fecal samples from mice following morphine or placebo
treatments, we constructed a loading plot of the principal components in which each dot represents a single
molecular metabolite (Fig.?6B). The metabolic profiles following morphine treatment were determined in a time
course study. Our results demonstrate that morphine treatment resulted in a gradual and differential shift in
metabolites in a time-dependent manner (Fig.?6C,D). To identify shifts in the gut metabolome following
morphine treatment, we constructed scored scatter plots of the partial least square discriminant analysis (PLS-DA)
model and characterized the metabolomics profiles of fecal samples from wild-type mice (C57B6/J) with those
of morphine- or placebo-treated mice at day 0, day 1, day 2, and day 3 following treatment (Fig.?6C). Morphine
treatment revealed a distinct clustering in the metabolomics profile compared to that observed after placebo
treatment at each time point tested.
To reveal the details of the metabolomic changes due to morphine treatment, we identified the chemical
increases and decreases following morphine or placebo treatment (Fig.?6D). The results demonstrate that
morphine treatment altered the gut metabolomic profile and shifted metabolites differentially in the fecal samples
taken from mice at day 1, day 2, and day 3 post-morphine treatment compared to that observed after placebo
treatment. Bile acids decreased, whereas phosphatidylethanolamines (PEs) and saturated fatty acids increased as
a consequence of morphine treatment (Fig.?6D).
Naltrexone antagonized the morphine-induced gut metabolomic shift and reversed effects of
morphine on bile acid metabolism. We next used an opioid receptor antagonist, naltrexone, to determine
the role of classical opioid receptors in the morphine-induced effects on the gut metabolomics profile. Naloxone
treatment completely antagonized the effects of morphine on gut metabolomics and reversed the effect of
morphine on bile acid metabolism (Fig.?6E,F). Particularly, we established that the abundance of the secondary bile
acid, deoxycholic acid, significantly decreased following morphine treatment and the morphine-induced decrease
was antagonized by naltrexone (Fig.?6E,F). The abundance of the phospholipid, PE?a major component of the cell
membrane?was increased by morphine treatment, indicating increased cell injury, and the morphine-induced
increase was reversed by naltrexone treatment (Fig.?6F). These results indicate that levels of deoxycholic acid
(DCA) and phosphatidylethanolamines (PE) may be used as biomarkers to indicate morphine-modulation of the
gut microbiome and metabolome.
Morphine-induced dysbiosis disrupts morphine metabolism and its enterohepatic recirculation.
Morphine is conjugated to morphine-3-glucuronide (M3G) and morphine-6-glucuronide (M6G) in the liver
and excreted into the gut through the biliary tract. M3G is totally inactive, whereas M6G appears to display a
stronger analgesic activity than morphine39. The polar nature of the glucuronides prevents their reabsorption
across the gut lumen. Intestinal de-conjugating bacteria transform M3G and M6G to morphine, which allows its
reabsorption back into systemic circulation40. The enterohepatic circulation plays an important role in morphine
elimination, which is characterized by a prolonged terminal elimination phase41. In mice and rats, M6G
formation is insignificant; therefore, morphine is eliminated significantly as M3G. We demonstrated that the ratio of
M3G/MS concentration in the intestinal lumen increased between day 1 and day 2 post-treatment (Fig.?7E) and
serum levels also increased between day 1 and day 6 post-morphine treatment, indicating decreased M3G
conversion to morphine by deconjugation in the gut (Fig.?7F).
To further clarify whether morphine-modulated gut microbiome alteration is associated with gut metabolomic
changes following morphine treatment, we performed cross-correlation analyses between the gut microbiome and the
metabolome (Supplementary Figure?S1). The cross-correlation demonstrated that cholic acid and octadecanedioic acid
were negatively associated with Enterococcus and Erysipelotrichaceae at the family level. Phosphatidylethanolamines
(PE) were negatively associated with Bacteroidales (order level) and positively associated with Erysipelotrichaceae.
The current study is a short-term study following opioid administration and analysis of fecal samples collected
from mice over 6 days. We demonstrated significant changes in the microbial community within one day
post-morphine administration and that morphine treatment results in distinct characteristic microbial and
metabolomic signatures that are associated with dysbiosis, featuring increased expansion of potential pathogenic
bacterial communities (Figs?1, 2 and 3). Previous studies in our laboratory and other research groups have demonstrated
that morphine exposure resulted in an increased risk of virulent bacterial infection, bacterial translocation and
lethal gut-derived sepsis14,33,42,43. Our current results indicate that the increased abundance of potential pathogenic
bacteria following morphine treatment may account for morphine-induced bacterial translocation and sepsis.
Richness of microbial diversity is an indicator of microbial homeostasis and reduced microbial diversity is
associated with microbial dysbiosis44. Phylogenetic analysis revealed that morphine treatment results in decreased
alpha diversity (Fig.?3A,B) and distinct clustering in a beta diversity plot compared to that following placebo
treatment (Fig.?3C,D). Morphine treatment is associated with a significant decrease in gut microbial alpha diversity,
indicating microbial dysbiosis and increased risk for intestinal infections.
In this study, we reveal that E. faecalis expansion is associated with morphine-induced gut dysbiosis.
This observation is in alignment with our previous studies where we demonstrate that the major serotype of
the bacterial species that translocated into mesenteric lymph nodes and liver following morphine treatment
belonged to the Enterococcus family15. Infection of WT morphine-treated animals with E. faecalis may result
in greater gut pathology and sustained inflammation (unpublished observation). Transfer of microbial content
from morphine-treated animals to antibiotic-treated animals recapitulated morphine-induced pathology18.
These observations are consistent with E. faecalis being associated with ulcerative colitis in humans and with
opioid-induced sepsis in a murine model15,45. Moreover, we demonstrated that E. faecalis augments tolerance of
morphine analgesic effect in mice (Fig.?5). These results indicate that expansion of E. faecalis may act as biomarker
for morphine-induced gut microbial dysbiosis.
This study revealed that morphine-induced changes in the gut metabolomic profile shifts gradually with
differential changes in individual metabolites. Differential changes in the gut metabolomics profile may reflect
the alteration of the gut microbiome and, therefore, contribute to host response following morphine treatment.
Cross-correlation between the gut microbiome and the metabolome indicates an association between
bacterial communities and functional metabolites (Supplementary Figure?S1). Cholic acid is negatively associated
with Enterococcus and Erysipelotrichaceae but positively associated with Bacteroidales. In contrast, PEs and
stearic acid are positively associated with Enterococcus and Erysipelotrichaceae but negatively associated with
Bacteroidales. Our previous study determined that morphine treatment leads to intestinal barrier
dysfunction. It is well-demonstrated that bacterial metabolites regulate GI barrier function by the xenobiotic sensor
PXR-dependent TLR4 signaling pathway, suggesting that the alteration of the gut microbiome and metabolome
may contribute to gut barrier dysfunction following morphine treatment. Furthermore, bile acids have been
demonstrated to mediate host resistance to C. difficile infection. Decrease in primary and secondary bile acids in
the gut following morphine treatment is associated with an increase in pathogenic gut bacteria, such as E. faecalis.
This is consistent with observations that demonstrate that switching to a high-fat diet in mice is characterized
with Erysipelotrichaceae increase and associated with increased risk of infectious diseases and inflammation46,47.
Gut metabolites are an important link between gut microbes and host biological functions. Morphine
treatment results in dramatic changes in the fecal metabolome, alters fatty acid and bile acid metabolism, and increases
PEs levels in the gut. PEs are the primary lipid components of the inner bacterial membrane. An increase in PE
levels is indicative of significant cell injury. PEs are also associated with bacterial stress responses48.
This is the first study examining morphine metabolites in both stool and serum samples in a time-dependent
manner. We clearly demonstrate that enterohepatic recirculation of morphine decreases over time, implicating,
for the first time, the role of the gut microbiome in modulating morphine pharmacokinetics. We further establish
that the bacterial communities that synthesize ?-glucuronidase, which is associated with bile metabolism, also
play a significant role in morphine metabolism. Key morphine metabolites, M6G and M3G, are hydrolyzed by
bacterial ?-glucuronidase and are subsequently reabsorbed as morphine. Collectively, these results reveal that
opioid-induced alterations of the gut microbiome and metabolome contribute to dysregulation in morphine
pharmacokinetics. Our results demonstrate, for the first time, that opioid-induced dysbiosis results in decreased
enterohepatic recirculation of morphine into systemic circulation resulting in lower bioavailability of morphine
over time, thereby gradually reducing the efficacy of morphine as an analgesic agent. Our studies are
consistent with recent studies from Matthew Redinbo?s laboratory at the University of North Carolina at Chapel Hill,
demonstrating a significant role of ?-glucuronidase enzymes, expressed by the GI microbiota, in mammalian
systems and inactivation of endobiotic and xenobiotic compounds49.
Morphine metabolism and elimination plays an important role in determining drug pharmacokinetics and
assessing drug efficacy and adverse effects in clinical terms. We demonstrated that the M3G/MS serum
concentration ratio increased between day 1 and day 6 post-treatment (Fig.?7F), and this ratio also increased in fecal
samples between day 1 and day 2 post-morphine treatment, indicating decreased M3G deconjugation within
the gut lumen (Fig.?7E). The major glucuronide deconjugating bacteria are strict anaerobes, such as bacteroides
and bifidobacteria, which express ?-glucuronidase activity50. In the present study, we demonstrate that morphine
treatment results in a decrease in Bacteroidales, suggesting that the observed decrease in M3G-deconjugation is
a consequence of a decrease in deconjugating bacteria (Supplementary Table?S1). Our cross-correlation
analysis results reveal that these Bacteroidales are positively associated with cholic acid and octadecenoic acids, but
negatively associated with PEs, glucosides, and stearic acid, which is consistent with what we observed in the
morphine-induced alteration of the metabolomic profile (Fig.?6D,F). The phosphatidylethanolamine-binding
protein (PEBP) (alternatively named Raf-1 kinase inhibitor protein or RKIP), initially observed to bind PEs, has
been demonstrated to be associated with morphine derivatives51. PEBP acts as a molecular shield and prevents
morphine-3-glucuronide from rapid clearance52. These results reveal that the loss of the deconjugating bacterial
population, decreases primary and secondary bile acids and modulates morphine metabolism, elimination and
enterohepatic recirculation, which consequently impacts the systemic bioavailability of opioids. Therapeutics
directed at these targets may prolong the efficacy of opioid use with fewer side effects.
To our knowledge, this is the first study demonstrating that morphine use/abuse results in the emergence of
distinct microbial and metabolomic signatures. Morphine-induced alterations in the gut microbiome and
metabolome are inhibited by naltrexone, an opiate receptor antagonist, indicating that morphine-induced changes are
opiate receptor-dependent. We further identified E. faecalis being strongly correlated with gut dysbiosis following
morphine treatment, indicating its potential application in therapeutics and non-invasive diagnostics.
Morphine undergoes rapid first pass metabolism and is metabolized in the liver to M3G and M6G and
recirculated back through enterohepatic recirculation (Fig.?8). We investigated the role of the intestinal microbiota on
the biotransformation of opioids and other small molecular metabolites. Morphine-induced dysbiosis disrupts
morphine metabolism and its enterohepatic recirculation (Fig.?8). Morphine metabolism and elimination plays
an important role in determining drug pharmacokinetics and in assessing drug efficacy and adverse effects in
clinical terms. By understanding and altering the intestinal microbiota one may be able to detect and minimize
the adverse effects of xenobiotics. Thus, intestinal microbiota represent a therapeutic target for prolonging the
efficacy of morphine in pain management.
In summary, we demonstrate for the first time that morphine administration results in distinct gut microbial and
metabolic changes which impacts morphine metabolism and its pharmacokinetic profile contributing to the
deleterious effects of short-term opioid use. Our study implicates that modulation of the gut microbiome may be a
plausible therapeutic target in minimizing the negative consequences and prolonging the analgesic function of morphine.
Experimental animals. Pathogen-free C57BL/6J mice were obtained from the Jackson Laboratory (Bar
Harbor, Maine, USA). All mice were female and 8?10 weeks old. A maximum of four mice were housed per cage.
Food and tap water were available ad libitum. The animal housing facilities were maintained on a 12-h light/dark
cycle, with constant temperature (72 ? 1 ?F) and 50% humidity. All animals were maintained in
specific-pathogen-free facilities and all procedures were approved by the University of Minnesota Institutional Animal Care and
Use Committee (IACUC). The IACUC protocol number was 1203A11091. All procedures were conducted in line
with the guidelines set forth by the National Institutes of Health Guide for the Care and Use of Laboratory Animals.
Animal treatment. Mice received morphine through the pellet implantation method, as previously
described53. Using this method, plasma levels of morphine were maintained in the 0.6?2.0 ? g/ml range (range
observed in opioid abusers and patients on opioids for moderate to severe pain). Furthermore, this model is
commonly used in the study of opiate dependence and addiction53. Briefly, placebo or 25 mg morphine or 30 mg
naltrexone pellets (National Institutes of Health [NIH]/National Institute on Drug Abuse [NIDA], Bethesda, MD)
were inserted in a small pocket created by a small skin incision on the dorsal side of animals; incisions were closed
using surgical wound clips (Stoelting, 9 mm Stainless Steel, Wooddale, IL).
Experiment of determining effects of E. faecalis on morphine tolerance. The mice were
administrated with 5 g/L of streptomycin sulphate in the drinking water for 2 days and switch to normal drinking water
for 24 hours before E. faecalis inoculation by oral gavage. The spectinomycin sulphate selective E. faecalis were
diluted up to the concentration of 2 ? 1010/mL in PBS. Each mouse was administered with 200 ul spectinomycin
solution by oral gavage daily. After 48 hours post gavage, the mice were treated with 250 mg/L spectinomycin
sulfate in the drinking water during the behavior study. To maintain the population of E. faecalis in the mouse
gut, mice were administered with the same dose of E. faecalis and the same dose of spectinomycin sulphate by oral
gavage daily during the behavior experiment for 8 days.
Analgesic effectiveness of morphine interpreted as MPE% (percentage of maximum possible effect), was
determined by tail flick analgesic test54. Mice were intraperitoneally injected with saline or 15 mg/kg morphine
twice daily for 8 days with 12 hours interval. Behavioral assessment was performed before and 30 min after saline
or morphine administration in the morning. Placebo + PBS group and Placebo + E. faecalis group served as
control groups. All groups had a minimum of 10 mice per group. New groups of mice in the Placebo + PBS and
Placebo + E. faecalis were administered with 15 mg/kg morphine at each time point to test analgesic reaction of
these naive mice to morphine treatment. Withdrawal latencies of the tail from a radiant heat source were
measured by tail flick. Voltage to the light source was adjusted to achieve baseline latency between 2?3 seconds. The
cut-off time is 10 seconds to avoid tissue damage. The mice were placed on the tail flick assay for 5min for
habituation everyday for two days before the behavior test. Everyday, the averages of each two measurements before and
after morphine injection were recorded as baseline and then response to morphine antinocipective effect were
recorded. The tests of significance were performed using a two-sided student?s two-sample t-test. P value of 0.05
or less was considered significant.
Fecal sample collection and DNA extraction. Stool samples were collected in 1.7 ml RNase/DNase-free
tubes (Catalog #: C-2170, Denville Scientific, Holliston, MA, USA) at various time points. The fecal samples were
immediately frozen on dry ice and then stored at ?80 ?C; DNA extractions from the fecal matter was carried out
using the PowerSoil DNA isolation kit (Catalog #: 12888?100, MO BIO Laboratories, Carlsbad, CA, USA). All
extracted DNA samples were stored at ?80 ?C until amplification.
Quantitative real-time PCR amplification for Illumina sequencing. The 16S sequencing procedure
was performed at the University of Minnesota Genomic Center. Method optimizations and the protocol have
been recently published55,56. Fecal DNA samples (25 ng) were used as templates for PCR amplification of the V4
region of the 16S rRNA gene. Degenerate primer sets were designed with Illumina index sequences on the 5?
end of the reverse primer, which were specific to each fecal DNA sample and allowed for multiplex sequencing.
Primers also contained Illumina PCR primer sequences (reverse primer) and Illumina TruSeq Universal Adapter
sequences (forward primers) for library creation. The primer sequences (16S-specific portion in bold) used were
Meta_V4_515F (TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGCCAGCMGCCGCGGTAA) and
The indexing primers are as follows: This step adds both the index and the flow cell adapters. [i5] and [i7] refer
to the index sequence codes used by Illumina. The p5 and p7 flow cell adapters are in bold. Forward indexing
primer: ATGATACGGCGACCACCGAGATCTACAC[i5]TCGTCGGCAGCGTC; Reverse indexing primer:
CAAGCAGAAGACGGCATACGAGAT[i7]GTCTCGTGGGCTCGG. PCR reactions were performed using
KAPA HiFidelity Hot Start Polymerase. PCR 1 (using the Meta_V4_515F/Meta_V4_806R primer pair): 95 ?C
5 minutes, 20 cycles (98 ?C 20 seconds, 55 ?C 15 seconds, 72 ?C 1 minute), followed by holding at 4 ?C. After the
first round of amplification, PCR 1 products were diluted 1:100 and 5? l of 1:100 PCR 1 was used in the second
PCR reaction. PCR 2 (using different combinations of forward and reverse indexing primers): 95?C 5 minutes, 10
cycles (98 ?C 20 seconds, 55 ?C 15 seconds, 72 ?C 1 minute), followed by holding at 4 ?C.
DNA sequencing. Genomic DNA sequencing was performed using Illumina MiSeq at the University of
Minnesota Genomic Center (UMGC). Pooled, size-selected samples were denatured with NaOH, diluted to 8
pM in Illumina?s HT1 buffer, spiked with 15% PhiX, and heat-denatured at 96 ?C for 2 minutes immediately prior
to loading. The MiSeq 600 cycle v3 kit was used to sequence the sample. Nextera adapter sequences for post-run
trimming were as follows:
Read 1: CTGTCTCTTATACACATCTCCGAGCCCACGAGACNNNNNNNNATCTCGTATGCCGTCTT
Read 2: CTGTCTCTTATACACATCTGACGCTGCCGACGANNNNNNNNGTGTAGATCTCGGTGGTC
The raw data files for 16s rDNA sequencing have been deposited with ArrayExpress with the accession
numbers E-MTAB-5546 and E-MTAB-5550.
Sequence processing and analysis. Microbial operational taxonomic units (OTUs) and their taxonomic
assignments were obtained using default settings in QIIME version 1.8.0 by reference-mapping at 97%
similarity against representative sequences of 97% OTU in Greengenes (release GG_13_8), following which chimeric
sequences were removed from subsequent analyses57. Sequences showing 97% or greater similarity were
clustered into operational taxonomic units (OTUs) using the USEARCH method and representative sequences were
assigned taxonomies using the RDP classifier.
Quantitative real-time PCR amplification for detection of specific species. Fecal DNA samples (25
ng) were used as templates for PCR amplification of the species-specific primers. Amplification of species-specific
primers was normalized to the 16S rRNA gene. 16S rRNA gene universal primers used were as follows: F: ACTCCTA
CGGGCAGCAG; R: ATTACCGCGGCTGCTGG. Enterococcus faecalis, F: ATCAAGTACAGTTAGTCTT,
R: ACGATTCAAAGCTAACTG; Enterococcus faecium, F: GCAAGGCTTCTTAGAGA, R: CATCGTGTA
AGCTAACTTC; Fusobacterium nucleatum, F: AGA GTT TGA TCC TGG CTC AG, R: GTC ATC GTG CAC ACA GAA
TTG CTG; fadA, F: CAC AAG CTG ACG CTG CTA GA, R: TTA CCA GCT CTT AAA GCT TG; Clostridium
difficile, F: TTG AGC GAT TTA CTT CGG TAA AGA, R: CCA TCC TGT ACT GGC TCA CCT; Clostridium scindens,
F: CGT AAC GCG CTC TTT CTT CG, R: CCT TCC TCC AGG TTC TCC CT; Coprobacillus catenaformis,
F: CGGACGCGATGCTTCT(A/G)GC, R: AACATATCTCCCATGCGGTTG; Staphylococcus aureus, F:
GCGATTGATGGTGATACGGTT, R: AGCCAAGCCTTGACGAACTAAAGC; the qPCR program was as
follows: (i) initial denaturation at 95 ?C (10 min) and (ii) 45 cycles of 95 ?C (15 sec), 60 ?C (45 sec followed by
fluorescence plate read).
LC-MS analysis of fecal extracts. The method for LC-MS analysis of fecal extracts was as described in a
previous study with slight modification58. Fecal samples were suspended in 1 ml of 50% acetonitrile (wt/vol) and
extracted by vortexing and sonication for 10 min. The suspension was extracted twice by collecting supernatant
after centrifuging at 18,000 ? g for 10 min. After passage of the supernatant through a 2-?m filter, the filtrate was
transferred to a UPLC vial and subjected to LC-MS analysis. A 5-?l aliquot prepared from the fecal extract was
injected into an Acquity ultra-performance liquid chromatography (UPLC) system (Waters, Milford, MA) and
separated in a BEH C18 column (Waters). The mobile phase used a gradient ranging from water to 95% aqueous
ACN containing 0.1% formic acid over a 10-min run. The LC eluent was introduced into a Xevo-G2-S
quadrupole time-of-flight mass spectrometer (QTOFMS, Waters) for accurate mass measurement and ion counting in
negative-mode. Capillary voltage and cone voltage for electrospray ionization was maintained at ?3 kV and ?35
V for negative-mode detection. Source temperature and desolvation temperature were set at 120 ?C and 350 ?C,
respectively. Nitrogen was used as both cone gas (50 L/h) and desolvation gas (600 L/h), and argon as collision
gas. For accurate mass measurement, the mass spectrometer was calibrated with sodium formate solution with
mass-to-charge ratio (m/z) of 50?1,000 and monitored by the intermittent injection of the lock mass leucine
enkephalin ([M-H]? = m/z 554.2615) in real time. Mass chromatograms and mass spectral data were acquired
and processed by the MassLynxTM software (Waters) in centroided format. Additional structural information was
obtained by tandem MS (MS/MS) fragmentation with collision energies ranging from 15 to 40 eV. The
concentrations of bile acids in fecal samples were determined by calculating the ratio between the peak area of individual
bile acids and the peak area of internal standard and then fitting with a standard curve using the QuanLynxTM
software (Waters). Morphine sulfate standard was purchased from National Institute of Drug Abuse (NIDA) and
M3G standard was purchased from Sigma.
Statistics. For microbiome analysis, QIIME 1.8 was used to calculate the ? diversity (alpha_rarefaction.py)
and to summarize taxa (summarize_taxa_through_plots.py). Principal coordinates analysis (PCoA, =
multidimensional scaling, MDS) is a method to explore and to visualize inter-object similarity/dissimilarity in a
low-dimensional, Euclidean space. Principal coordinate analysis (PCoA) of unweighted UniFrac phylogenetic
distances between microbial communities were carried out using this program with observation ID level. The input
for PCoA is the OTU table containing the number of sequences observed in each OTU (rows) for each sample
(columns). OTU tables were rarefied to the sample containing the lowest number of sequences in each analysis.
An estimate of the false discovery rate (q-value < 0.10) was calculated to take into account the multiple
comparisons that occur in microbiome analysis. P-value in UniFrac distance comparison was Bonferroni-corrected. The
tests of significance were performed using a two-sided student?s two-sample t-test.
For real-time PCR analysis, the heat map was generated using a log transformation of the real-time PCR data
presented as ?CT (CT_species ? CT_universal_16 SrRNA). Red color indicates increased levels of
amplification. The 16S-rRNA gene amplification fold change due to treatments were analyzed by the t-test or ANOVA
For metabolite analysis, experimental values for fecal metabolites are expressed as the means ? SEM. Scores
scatter plots of the partial least square discriminant analysis (PLS-DA) model were constructed. An estimate of
the false discovery rate (q-value < 0.10) was calculated to take into account the multiple comparisons that
normally occur in metabolomic-based studies. In this study, the microbiome and metabolic profiles of fecal samples
from 4 groups (placebo, morphine, morphine + naltrexone, naltrexone) were compared. For morphine and M3G
concentration analysis, statistical analysis was performed using t-test or ANOVA (GraphPad Prism).
Cross-correlation analysis between the gut microbiome and metabolome data was performed using the
Microbiome R package, following the instructions provided online59. Cross-correlation between phylogenotypes
of microbiome and metabolites was analyzed using Spearman correlation (Supplemental Fig.?1). Taxa relative
values were transformed as the base-10 logarithm of a number before being correlated with metabolomic relative
abundance values and FDR-adjusted p-value (q-value) was obtained. The URL of the Microbiome R package is:
Data availability. The microbiome datasets supporting the conclusions of this article are available in the
ArrayExpress repository. The accession numbers are E-MTAB-5546 and E-MTAB-5550. The hyperlink to datasets
This work was supported by the NIH grants R01 DA043252, R01 DA037843, R01 DA044582, to SR. The funding
agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the
S.R. conceptualized the project. F.W. and S.R. designed experiments. F.W., J.M., L.Z. performed experiments and
data analysis. T.J. contributed to the microbiome analysis. C.C. contributed to the metabolomic analysis. F.W. and
S.R. wrote the manuscript.
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-21915-8.
Competing Interests: The authors declare no competing interests. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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