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. Author manuscript; available in PMC: 2024 Jun 1.
Published in final edited form as: Br J Pharmacol. 2023 Jan 24;180(12):1582–1596. doi: 10.1111/bph.16025

Morphine use induces gastric microbial dysbiosis driving gastric inflammation through TLR2 signaling which is attenuated by proton pump inhibitor

Nillu Ghosh 1, Kousik Kesh 1, Praveen Kumar Singh 1, Umakant Sharma 1, Irina Chupikova 1, Sundaram Ramakrishnan 1,2, Sabita Roy 1,2,*
PMCID: PMC10175111  NIHMSID: NIHMS1861880  PMID: 36585367

Abstract

Background and Purpose:

Opioids are the gold standard drug for pain management, however, their effect on gastric dysfunction is relatively understudied. Opioid users have a higher incidence of gastric pathology leading to increased hospitalization. Herein, we investigated the consequence of morphine use on gastric pathology and the underlying mechanisms. We further investigated the therapeutic benefit of proton pump inhibition as a pharmacological target to overcome morphine-mediated gastric inflammation.

Experimental Approach:

Mice were implanted with 25 mg slow-releasing morphine and placebo pellets. Gastric microbiome analyses were performed. Gastric damage was assayed by histology and TUNEL. Gastric pH was measured. Germ-free and TLR2KO mice were used to investigate the plausible mechanisms. Gastroprotective study was performed with proton pump inhibitor (PPI) omeprazole.

Key Results:

Chronic morphine treatment alters gastric microbial composition and induces preferential expansion of pathogenic bacterial communities such as streptococcus, and pseudomonas. In addition, morphine causes disruption of the gastric mucosal layer, increased apoptosis, and elevated inflammatory cytokines. Moreover, morphine-mediated gastric pathology was significantly attenuated in germ-free mice and reconstitution of germ-free mice with gastric microbiome collected from morphine-treated mice recapitulated gastric inflammation. In addition, morphine-mediated gastric inflammation was attenuated in TLR2KO mice. Furthermore, morphine causes a decrease in gastric pH, which contributes to gastric dysbiosis and subsequently leads to gastric inflammation. Omeprazole treatment inhibits gastric acidity thereby rescuing morphine-induced gastric dysbiosis and preventing inflammation.

Conclusion and Implications:

This study attributes morphine-induced gastric acidity as a driver of gastric dysbiosis and pathology and proposes the therapeutic use of PPI as an inexpensive approach for the clinical management of morphine-associated pathophysiology.

Keywords: Opioid, Gastric inflammation, Gastric microbiome, Proton pump inhibition, Germ-free mice

Graphical Abstract

graphic file with name nihms-1861880-f0001.jpg

Morphine treatment increases gastric acid secretion which causes dysbiosis of gastric microbiome. The dysbiotic gastric microbiome induces gastric inflammation through TLR2-mediated signaling. While omeprazole prevents morphine-induced gastric damage by regulating gastric acid secretion and restoring normal gastric microbiota.

Introduction:

Management of pain is an important challenge worldwide. Opioids are the most effective analgesics for pain management in cancer and in postoperative pain and are increasingly used for non-cancer diseases as well. Despite being the predominant drug of choice to alleviate pain, morphine use also results in undesired adverse effects, such as nausea, vomiting, and reduced gastrointestinal (GI) movement known as opioid-induced bowel dysfunction (OBD) (Camilleri, 2011). Reduced GI transit leads to bloating and idiopathic constipation, which subsequently leads to gastroesophageal reflux disease. Even clinical dose of opioids for pain management causes undesirable gastrointestinal side effects.

The composition of the gut microbiome is considered crucial for the maintenance of gastrointestinal homeostasis. Dysbiosis of the gut microbiome has been associated with various disease conditions. Recently, we have reported that opioid use results in gut microbial dysbiosis, which leads to gut barrier disruption and systemic inflammation (Banerjee et al., 2016; Wang & Roy, 2017). The stomach was long thought to be a sterile compartment before the discovery of H. pylori. Next-generation sequencing technology has expanded our knowledge of the human gastric microbiome, which is now also known to play a critical role in the maintenance of GI homeostasis. Gastric microbiota in humans mainly belongs to five phyla: Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria, and Fusobacteria (Nardone & Compare, 2015). Firmicutes and Proteobacteria are the most abundant phyla in gastric mucosal samples. Studies have shown that dysbiosis of the gastric microbiome is associated with gastric diseases (Yang et al., 2021). Gastric microbiome alteration in individuals with gastric cancer or precancerous conditions has been reported by several researchers (Noto & Peek, 2017). Recently there is increasing evidence that bacteria other than H. pylori also contribute to gastric carcinogenesis (Yang et al., 2021). The association between the gastric microbiome and non-cancer gastric diseases has also been reported (Noto & Peek, 2017). However, the role of gastric microbes other than H. pylori. in gastric inflammation remains poorly studied. Although, it has been shown that patients with atrophic gastritis, compare to healthy subjects, had an increase of Streptococcus and a decrease of Prevotella (Nardone & Compare, 2015). However, the role of morphine in gastric microbial dysbiosis and its association with gastric pathology has never been investigated.

Toll-like receptors (TLRs) recognize a variety of microbial components and allow the innate immune system to sense and react to the altered microbiota, hence playing a central role in the interaction between host and microbiota. Epithelial cells of gastric mucosa represent the first line of innate immune defense against pathogens and respond to infection by initiating numerous cell signaling cascades. PRRs of the Toll-like receptor (TLR) family have been shown to mediate many of these cell signaling events. Both TLR2 and TLR4 play important roles in innate immune response in gastric mucosa against infection. Previously we have reported that morphine-induced gut dysbiosis causes inflammation in the gut through TLR2-mediated pathways (Meng et al., 2013). Gastric inflammation occurs through a sequential multifaceted process leading to extracellular matrix (ECM) degradation. Studies on the effects of morphine on gastric inflammation and the underlying mechanisms are currently inconclusive.

Proton pump inhibitors (PPIs) inhibit gastric acid secretion and shown to prevent gastric ulceration and reflux esophagitis (Wilde & McTavish, 1994). In combination with antibiotics, PPIs are also an integral part of the therapy for Helicobacter pylori-induced gastric ulceration primarily due to their anti-inflammatory effects (Kedika et al., 2009).

In the present study, we investigate the consequences of morphine use on gastric microbiome alteration and gastric damage and the underlying mechanism driving morphine-induced gastric inflammation. The goal of the study was also to identify novel strategies that could be exploited pharmacologically to overcome morphine-mediated gastric inflammation. We have demonstrated that morphine induces gastric microbiome alteration which causes gastric inflammation that is alleviated by proton pump inhibition.

Methods:

Ethics and experimental animals:

Animal studies are reported in compliance with the ARRIVE guidelines (Percie du Sert et al., 2020) and with the recommendations made by the British Journal of Pharmacology (Lilley et al., 2020).

All animal care and procedures were approved by the University of Miami Institutional Animal Care and Use Committee (IACUC) and conducted in line with the guidelines set forth by the National Institutes of Health Guide for the Care and Use of Laboratory Animals. C57BL/6J (RRID: IMSR_JAX:000664) mice were purchased from Jackson Laboratory (Bar Harbor, ME, USA). Typically, 8–10-week-old (both male and female equal numbers) animals were used for our studies. Mice were housed 3–5 per cage and maintained on a 12-h light/dark cycle, at a constant temperature (72 ± 1°F) and 50% humidity. Food and tap water were available ad libitum. Animals were randomized after receiving from Jackson laboratory and experiments were repeated to eliminate the cage effect.

Animal treatment:

Mice were implanted with a 25 mg slow-releasing morphine or placebo pellet as described follows (Bryant et al., 1988). 25 mg morphine pellet maintained plasma level of morphine (0.2 – 2) µg/ml for 5–6 days in mice (Banerjee et al., 2016; McLane et al., 2017). Briefly, placebo and morphine 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 animal’s dorsal side; incisions were closed using surgical wound clips (Stoelting, 9 mm Stainless Steel, Wooddale, IL). For microbiome studies, mice were implanted with a 25 mg slow-releasing morphine or placebo pellet for 24 and 48 hours with or without naloxone and omeprazole. Omeprazole was gavaged (Sigma-Aldrich, 40mg/kg daily). Naloxone is used as a pellet (30mg). In another set of experiments, Wildtype (WT), Germ-free (GF), and TLR2KO mice were pelleted with either a morphine or placebo pellet. All the procedures were conducted under isoflurane and aseptic condition.

Histology:

Mice stomachs were sectioned for histological studies. Briefly, gastric tissue samples were embedded in paraffin, sectioned at 5 µm and stained with hematoxylin and eosin for histologic evaluation. Slides were imaged using a microscope (Leica microsystems, Germany) at original magnification, 10 × 10, and processed in Adobe Photoshop (Swarnakar et al., 2005).

Real-time PCR:

Total cellular RNA from mice gastric tissue was extracted using TRIzol (Invitrogen), and cDNA was synthesized with the M-MLV Reverse Transcription Kit (Promega). Primers for IL-6, IL-1β, TNF-α, and 18S ribosomal RNA were purchased from Invitrogen. Quantitative real-time polymerase chain reaction was performed using LightCycler® 480 SYBR Green I Master (Roche). All samples were run in triplicate. The 18S ribosomal RNA expression was used to normalize the relative mRNA expressions. The primer sequences were:

18S: 5’-GTAACCCGTTGA ACC CCATT-3’, 5’-CCATCCAATCGGTAGTAGCG-3’; IL-6: 5’-TGGCTAAGGACCAAGACCATCCAA-3’, 5’ AACGCACTAGGTTTGCCGAGTAGA-3’; TNF-α: 5’- CCTCCCTCTCATCAGTTCTATGG-3’, 5’-CGTGGGCTACAGGCTTGTC-3’; IL-1β: 5’- GGCAGGCAGTATCACTCATT-3’, 5’-AAGGTGCTCATGTCCTCATC-3’ (Zhang et al., 2019).

Detection of apoptotic cells in the gastric mucosa:

Apoptotic activity in the gastric tissues was detected by fragmented DNA was stained by the terminal deoxynucleotidyltransferase (TdT)-mediated dUDP-biotin nick end labeling (TUNEL) assay as described by the manufacturer (apoptosis detection kit, Abcam). Briefly, 4 µm sections of gastric tissue sections were dewaxed, rehydrated, and digested with Proteinase K. Then they were labeled with TUNEL reaction mixture (biotin-labeled), which catalyzes the addition of biotin-labeled deoxynucleotides and incubated with streptavidin-horseradish peroxidase (HRP) conjugate. Tissue sections treated with DNase I are a positive control, and where TdT was substituted with water, were included as negative controls. The signal was detected using 3,3′-diaminobenzidine (DAB) substrate, and the sections were counterstained with Methyl green. Tissue sections were screened for positive nuclei under a light microscope. Data from all fields were pooled to obtain the apoptotic index and are presented as the percentage of TUNEL-positive cells in the overall cell population, manually counted in 10 randomly selected fields.

Immunohistochemistry

Details of immunohistochemical protocols are described in the following, according to BJP guidelines (Alexander et al., 2018). Briefly, 4 µm sections of gastric tissue sections were deparaffinized by heating at 56°C overnight and then hydrated by dipping in xylene for 15 min (two times), and in 100% ethanol, 90% ethanol, 70% ethanol, and PBS for 5 min each. The slides were then steamed with a pH 9 reveal decloaker (BIOCARE Medical, S2367) for antigen retrieval, and blocked in Dako serum-free blocker (Agilent technologies). The primary antibody against TLR2 (Abcam, #ab209261 (RRID: AB_2924223), #ab11864 (RRID:AB_298646), 1:100 dilution), pan-cytokeratin (1:100 dilution, Abcam, #ab7753, RRID:AB_306047) or cleaved caspase-3 (Cell signalling, #74860SF, RRID: AB_2687881, 1:100 dilution,) was added overnight. Next, slides were washed three times in PBS, secondary antibodies (Alexafluor-conjugated) were diluted in SNIPER (BIOCARE Medical, BS966L), and slides were stained for 30 min at room temperature. Slides were washed again three times in PBS and mounted using Prolong Gold anti-fade with DAPI (4,6-diamidio-2-phenylindoldilactate, Thermo Fisher Scientific, #P36935) for nucleus staining. Slides were dried overnight and imaged by imaged using Leica DFC7000T microscope (Leica microsystems). Primary antibodies for TLR2 (#ab209261) was combined with Alexa555 donkey anti-Rabbit Ig (Thermo Fisher Scientific, #A- 31572, RRID: AB_162543 dilution 1:1000) and TLR2 (#ab11864) was combined with Alexa488 goat anti-rat IgG (Abcam, #ab150157, RRID: AB_2722511 dilution 1:1000), whereas pan-cytokeratin was combined with Alexa488 goat anti-mouse IgG ( Abcam, #ab150113, RRID: AB_2576208 dilution 1:500) and cleaved caspase-3 was combined with Alexa555 donkey anti-Rabbit Ig (Thermo Fisher Scientific, #A-31572, dilution 1:1000).

Measurement of Intragastric pH:

As previously described gastric pH was measured in the gastric lumen using a pH electrode. Briefly, the pH electrode (Sigma-Aldrich micro pH combination electrode) was inserted into the lumen to measure the pH of the gastric content (Brenneman et al., 2014).

Acid tolerance test

Mice were implanted with a 25 mg slow-releasing morphine or placebo pellet for 24 hours. Total gastric content was collected and suspended in 1 ml sterile PBS, filtered through a 70 µm cell strainer, and centrifuged at 6000 x g for 20 mins. The pellet is a gastric microbiome then dissolved in MRS broth that was previously adjusted to pH 3.5 with HCl. The cultures were incubated for 3 hours at 37°C and then culture samples were removed and plated on blood agar. The plates were incubated at 37°C for 48 hours under aerobic conditions and compared with the counts of the pH 7.5-control culture. The experiments were repeated three times.

Gastric microbiome transfer:

SPF mice were pelleted with placebo and morphine pellets. Total gastric content was collected and suspended in 1 ml sterile PBS, filtered through a 70 µm cell strainer, and centrifuged at 6000 x g for 20 mins. The gastric microbiome was gavaged to germ-free mice and sacrificed at 48 hours.

16S rRNA gene sequencing:

The DNA from the mice’s gastric content was isolated under aseptic conditions using DNeasy PowerSoil® kits (Qiagen, Germantown, MD), which were modified to include a bead-beating step. Sequencing was performed by the University of Minnesota Genomic Center, MN, United States, and Microbiome Insights, Vancouver, BC, Canada. Hypervariable regions V5 and V6 of 16S rRNA were polymerase chain reactions amplified using primers with the V5F RGGATTAGATACCC and V6R CGACRRCCATGCANCACCT gene-specific sequences, Illumina adaptors, and molecular barcodes as described to produce 427 base pair (bp) amplicons. Samples were sequenced on an Illumina MiSeq (Illumina, San Diego, CA) using MiSeq 600 cycle v3 kit (Gohl et al., 2016). At Microbiome Insights, the V4 region was amplified with adapter-barcode-pad/linker-16S primer as shown below: AATGATACGGCGACCACCGAGTCTACACCTACTATATATGGTAATTGTGTGCCA GCMGCCGCGGTAA and CAAGCAGAAGACGGCATACGAGATAACTCTCGAGTCAGTCAGCCGGACTACHV GGGTWTCTAAT. Samples were sequenced by Illumina MiSeq.

Bioinformatic analysis:

Demultiplexed sequence reads were clustered into amplicon sequence variants (ASVs) with the DADA2 package (version 1.16.0) implemented in R (version 4.0.3) and RStudio (version 1.4.1106). The ASV table generated by DADA2 was imported into the QIIME2 pipeline for diversity analyses and taxonomic assignment (Quantitative Insights Into Microbial Ecology, 2015). Taxonomic assignment of ASVs was done to the genus level using a naive Bayesian classifier implemented in QIIME2 with the Silva reference database. Taxonomic analysis to detect differentially abundant taxa across groups was generated by uploading the taxonomic assignment table to the web-based tool microbiome analyst (Chong et al., 2020). Individual differential taxa were identified and significance was determined based on a false discovery rate of 0.05 (FDR). The threshold on the logarithmic linear discrimination analysis (LDA) score for discriminative features was set to 2. BugBase software was used to predict high-level microbial phenotypes (BugBase, 2016). The raw sequencing data supporting the findings of this study have been submitted to the Sequence Read Archive (SRA) of the NCBI under accession number PRJNA746294. All other data that support the findings of this study are available upon request.

The metagenomic functional composition was predicted from the latest Kyoto Encyclopedia of Genes and Genomes (KEGG) database using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) approach (Kanehisa et al., 2012).

Based on the KEGG database, PICRUSt analysis was applied to predict the functional profiling of microbial communities according to 16S sequencing data.

Statistical analysis:

The data and statistical analysis comply with the recommendations on experimental design and analysis in pharmacology, as set out in the relevant Editorial in the British Journal of Pharmacology (Curtis et al., 2022). Laboratory animals were randomly assigned to experimental groups, and treatments were assessed blindly. The order of treatment administration was also randomized. All animal samples were studied, and analysis was carried out in a blinded manner. The level of significance was set at 5% with 80% power to get the minimum possible number of mice required for the experiment. Statistical analysis was undertaken only for experiments with a sample size of at least n = 5 per group. The group size is the number of independent values, and the statistical analysis was done using these independent values. All data were analyzed using GraphPad Prism software (version 5.02; GraphPad Software Inc., La Jolla, CA, USA; RRID:SCR 002798) and the results are presented as mean ± SEM. Outliers were included in the data analysis. Unpaired Student’s t-tests and two-way ANOVA with Bonferroni or Tukey post hoc tests were used for group comparison. Post hoc tests were conducted only if F in ANOVA achieved P < 0.05. Parametric tests were performed after testing for normal distribution and confirming that there was no significant variance in homogeneity.

Materials:

Omeprazole (cat: 0104–100MG), Triton-X-100, isoflurane, HCL, paraformaldehyde, xylene, ethanol, PBS, hydrogen peroxide, phenol, chloroform and isoamyl alcohol were purchased from Sigma-Aldrich (St. Louis, MO, USA). Details of other materials and suppliers were provided in the specific sections.

Key protein targets and ligands:

Key protein targets and ligands in this article are hyperlinked to corresponding entries in http://www.guidetopharmacology.org, and are permanently archived in the Concise Guide to PHARMACOLOGY 2021/22 (Alexander Christopoulos, et al., 2021; Alexander, Fabbro, et al., 2021; Alexander, Mathie, et al., 2021).

Results:

Morphine treatment resulted in significant alteration of the gastric microbiome.

Microbial dysbiosis is associated with various diseases. We have previously reported that morphine treatment results in gut microbial dysbiosis, disruption in gut barrier function, and bacterial translocation. To determine the effect of morphine on the gastric microbial profile, mice were implanted with either morphine or placebo pellet, and their gastric contents were analyzed for microbial composition. The two “non-phylogeny-based” metrics, the Shannon index and Chao1, were used to describe α-diversity. The Shannon index, which accounts for both abundance and evenness, showed significant differences between the placebo and morphine-treated groups (p<0.001), as the morphine-treated group, exhibited less bacterial diversity compared to the placebo group (Fig. 1A). The Chao1 diversity analysis indicated a significant decrease in OTU richness in the morphine group (p<0.001) (Fig. 1A). This result was consistent at both the 24- and 48-hours’ time points (Fig. S1). Moreover, β-diversity analysis (Bray Curtis) revealed distinct clustering of the bacterial communities in the morphine-treated animals at both time points (p<0.001; permutation ANOVA (PERMANOVA) test with Bonferroni correction) (Fig. 1B). The taxonomical analysis further revealed a net expansion of the relative abundance of Firmicutes and Verrucomicrobia, and a reduction of Bacteroidetes, actinobacteria, and tenericutes at the phylum level in the morphine-treated animals (Fig. 1C, D). Notably, morphine treatment reduced the Bacteroidetes/Firmicutes ratio, which signifies an increased level of inflammation (Fig. S2). Additionally, 27 bacterial families showed significant changes in relative abundance between the morphine-treated and placebo groups at 24 hours. In particular, an elevated abundance of the bacterial families Enterococcaceae, Staphylococcaceae, Peptostreptococcoceae, Streptococcaceae, Erysipelotrichaceae, Pseudomonaceae, Akkermansiaceae, Coriobacteriaceae and Neisseriaceae were observed in the morphine-treated group, most of which belong to phylum Firmicutes (Fig. 1E). At 48 hours, we noted that the abundance of 15 bacterial families was significantly altered between the morphine-treated and placebo groups (Fig. S1E). Furthermore, the morphine group displayed a reduction in Lactobacillaceae, Lachnospiraceae, Muribaculaceae, Ruminococcaceae, Burkholderiaceae, Eggerthelaceae, and Peptococcaceae at the family level at both time points (Fig. 1E, S1E). Further analysis revealed a significant difference in the abundance of bacteria at the genus level between the groups (Fig. 1F, S1F). Potential pathogenic bacteria that were increased in abundance at the genus level in the morphine-treated group included Staphylococcus, Enterococcus, Turicibacter, and Pseudomonas. Additionally, lactobacillus, known to prevent inflammation and maintain barrier function, was significantly reduced in the morphine-treated group. Furthermore, we found that the microbiome of the naloxone-pretreated morphine mice clustered with the placebo group, signifying the role of opioid receptors in gastric microbiome dysbiosis (Fig. S3).

Figure 1.

Figure 1.

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Morphine induces global changes in gastric microbiota. α-diversity is shown by Shannon and Chao1 index (A). t-test was conducted on the Shannon and Chao1 index. NMDS scaling analysis of microbiota was performed to visualize the Bray−Curtis distance between the groups. Red bars depict samples from morphine mice; blue bars represent placebo mice. β-diversity was found to be significantly different between the morphine and placebo groups (P = 0.00256) (B). Taxonomic distribution at phylum level showing individual sample (C). Lefsa plots show changes in abundance of bacteria at phylum, family and genus levels (D, E, F). Microbial taxa with significant differences in placebo mice were selected at a false discovery rate < 0.1. nMorphine = 10; nPlacebo= 10. NMDS, nonmetric multidimensional scaling.

Microbial functions associated with inflammation enriched upon morphine treatment.

To investigate the functional role of the dysbiotic gastric microbiota in morphine-induced gastric inflammation, predicted genomic functions of the bacterial community were performed using PICRUSt from 16s rRNA data based on the KEGG database (Fig. S4). A total of 45 and 55 KEGG pathways were significantly altered between the morphine and placebo groups after 24 and 48 hours of treatment. We found that 13 pathways were enriched at 24 hours and 15 pathways were enriched at 48 hours after morphine treatment. The level 2 KEGG pathway data indicated that the pathways related to human diseases, infectious diseases, staphylococcus aureus infection, and bacterial invasion of epithelial cells were enriched in the morphine group. We also found a significant increase in pathways involved in xenobiotic biodegradation, membrane transport, phosphotransferase system, ABC transporters pathways in the morphine group. Upon further examination of the metabolism pathways, we found amino acid metabolism, lipid metabolism, glycerolipid metabolism, glutathione metabolism, pyruvate metabolism, tyrosine metabolism, terpenoids and polyketides metabolism were more prevalent in morphine-treated animals. We also found peptidoglycan biosynthesis, Ubiquinone and other terpenoid−quinone biosynthesis pathway increased in morphine-treated group. Thus, the functional metagenomic data indicate significant differences in putative microbiome functionality between the morphine and placebo groups.

Morphine treatment induces gastric inflammation.

To investigate the effect of morphine on gastric damage, mice were pelleted with either a morphine or placebo pellet. Histological observation of the mice gastric tissue indicated that morphine treatment caused the rupture of the surface epithelium, disruption of the gastric pit, and increase infiltration of inflammatory cells compared to placebo mice at both 24 and 48 hours (Fig. 2A, S5). Moreover, the number of apoptotic (TUNEL-positive) cells in the gastric mucosa of morphine-treated mice were also increased (Fig. 2B, C). Co-immunofluorescence of apoptosis marker cleaved caspase-3 and epithelial cell marker pan-cytokeratin revealed that gastric epithelial cells in the mucosal region undergo apoptosis upon morphine treatment (Fig. S6). Moreover, morphine treatment significantly increased the expression of inflammatory cytokines TNF-α, IL-1β, and IL-6 at 24 hours time points in mice gastric tissue (Fig. 2D, E. F). Altogether, these data show that morphine use results in significant gastric inflammation.

Figure 2.

Figure 2.

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Morphine induces gastric inflammation at 24 hours. Representative H&E stained sections of placebo and morphine mice gastric tissues at 24 hours (A) (Scale bars: 75 μm). Apoptosis in mice gastric tissues was detected by TUNEL assay (B) (Scale bars: 50 μm). . The average number of tunnel-positive cells (D). Expression of inflammatory cytokines TNF-α (D), IL-1β (E), and IL-6 (F) in mice gastric tissues. Data were analyzed by t-test (two-tail). **P < 0.01; ***P < 0.001. Data are represented as mean ± SD. Experiment was repeated three times (n=6 in each group/experiment). Gastric epithelial disruption and apoptotic cells are shown by the black arrow.

Morphine-induced dysbiotic microbiome causes gastric pathology that is attenuated in germ-free mice.

To establish the role of the microbiome in gastric inflammation, germ-free (GF) mice and conventionally raised, specific pathogen-free (SPF) mice were treated with either placebo or morphine pellets. In the morphine-treated SPF mice, we observed the denudation of the surface epithelium, disruption of the gastric pit, and infiltration of inflammatory cells in the gastric mucosa. Interestingly, in the morphine-treated GF mice, no significant damage was observed (Fig. 3A). The number of apoptotic cells in the gastric mucosa of GF morphine-treated mice was also significantly lower than in the SPF mice (Fig. 3B, C).

Figure 3.

Figure 3.

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Morphine-induced gastric inflammation is mediated by the dysbiotic gastric microbiome. Representative H&E stained sections of mice gastric tissues of WTplacebo, WTmorphine, germ-free mice gavage with placebo or morphine microbiome (A) (Scale bars: 75 μm). Apoptosis in gastric tissues of mice was measured (B) (Scale bars: 50 μm). An average number of tunnel +ve cells (C). Representative H&E stained sections of gastric tissues of mice gavage with placebo and morphine microbiome (D) (Scale bars: 75 μm). Data were analyzed by t-test (two tail). ***P < 0.001. Data are represented as mean ± SD. Experiment was repeated three times n=6 both in each group/experiment. The black arrow shows gastric epithelial damage and tunnel-positive apoptotic cells.

To further support the role of morphine-induced dysbiotic gastric microbiome in gastric damage, we isolated the microbiome from placebo or morphine-treated SPF mice and gavaged them into germ-free mice. Mice gavaged with morphine microbiome showed significant gastric damage compared to mice gavaged with placebo microbiome (Fig. 3D) indicating that morphine-induced gastric disruption is mediated by the dysbiotic gastric microbiome.

Morphine-induced gastric pathology is mediated by TLR2 signaling.

Our bug base analysis revealed an increase in gram+ve bacteria (Fig. 4A, S7) and a decrease in gram-ve bacteria upon morphine treatment (p<0.001) (Fig. S7). Because TLR2 is the major receptor that mediates the host’s response to gram-positive bacteria, we performed immunohistochemistry of the TLR2 receptor on the gastric mucosa. We found TLR2 expression increased in the gastric tissue of morphine-treated animals (Fig. 4B). Moreover we found that TLR2 overexpressed cells in gastric mucosa undergoing apoptosis upon morphine treatment (Fig. S8). To further establish the role of TLR2, we used the TLR2KO mice. Histological analysis revealed less gastric damage and a reduced number of apoptotic cells in TLR2KO morphine-treated mice compared to WT morphine-treated mice (Fig. 4C, D, E). Altogether, our data suggest a crucial role for TLR2 in morphine-induced gastric inflammation.

Figure 4.

Figure 4.

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Gastric microbiome mediates morphine-induced gastric inflammation via TLR2 signaling. Bugbase analysis exhibits an increase of gram-positive bacteria in the morphine group compared to placebo (A). Mann-Whitney-Wilcoxon test was performed for bug base analysis. Representative immunostaining of TLR2 in mice gastric tissue (B) (Scale bars: 50 μm). Representative H&E stained sections of mice gastric tissue of WTPlacebo, WTMorphine, TLR2KO placebo and TLR2KO morphine (C) (Scale bars: 75 μm). Apoptosis in mice gastric tissues was measured (D) (Scale bars: 50 μm). The average number of tunnel positive cells (E). Data were analyzed by t-test (two-tail). ***P < 0.001. Data are represented as mean ± SD. Experiment was repeated three times n=6 both in each group/experiment. The black arrow shows gastric epithelial damage and tunnel-positive apoptotic cells.

Morphine-induced gastric pH alteration drives morphine-associated gastric dysbiosis and gastric pathology.

We found morphine treatment reduces both bacterial abundance and diversity in the stomach. Gastric fluid pH was found to be associated with bacterial abundance and diversity in the stomach. Thus, we next checked the pH of the gastric content after morphine treatment. Gastric pH of morphine-treated and placebo mice was measured by inserting the pH electrode directly inside the mice’s stomach. The pH of the gastric content of morphine-treated mice has been shown significantly lower compared to placebo at both time points (Fig. 5A). To further check whether the morphine-induced dysbiotic microbiome is resistant to low pH, acid tolerance was evaluated in terms of survival and capacity to restart growth after exposure to low pH values for 3 hours. Similar bacterial counts were observed in morphine gastric microbiome treated at low pH and pH 7.5 control culture. While in the placebo group number of bacteria reduced significantly in low pH treatment compared to pH 7.5 control culture (Fig. 5B). Altogether, our data suggest that gastric acid plays a significant role in morphine-mediated gastric dysbiosis.

Figure 5.

Figure 5.

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Morphine-induced gastric dysbiosis is associated with low gastric pH. C57BL/6 mice were pelleted with 25mg morphine pellet for 24 hours. pH of the stomach content was measured by inserting a micro pH electrode inside the stomach (A). In another set of experiment, gastric microbiome is treated at pH 3.5 for 3 hours followed by plated on blood agar plate(B). Data were analyzed by t test (two tail). **P < 0.01; ***P < 0.001. Data are represented as mean ± SD. Experiment was repeated three times n=6 both in each group/experiment.

Morphine-induced gastric microbiome dysbiosis is rescued by inhibition of gastric acid secretion using a proton pump inhibitor.

To investigate whether morphine-induced gastric acidity has any role in morphine-mediated gastric dysbiosis, mice were gavaged with omeprazole before morphine treatment. We observed that there was a significant difference in the gastric microbiome pattern between the morphine-treated and omeprazole-pretreated morphine groups (Fig. 6). Omeprazole pretreatment resulted in a significant increase in α-diversity compared to the group treated with morphine alone (p<0.001) (Fig. 6A). The β-diversity analysis also revealed that the omeprazole-pretreated group clustered distinctly compared to the morphine-treated group (p<0.001) (Fig. 6B). Notably, no significant differences in the gastric microbiome pattern were observed between the omeprazole alone and the placebo groups.

Figure 6.

Figure 6.

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Omeprazole attenuates morphine-induced gastric dysbiosis. α-diversity was assessed using the Shannon index (A). t-test was conducted on the Shannon index. NDMS scaling was used to visualize the Bray-curtis distance between different groups (B). Taxonomic distribution of different groups at phylum level showing individual sample (C). Lefsa plots showing changes in abundance of bacteria at phylum, family, and genus levels (D, E, F). Microbial taxa with significant differences between groups were selected at a false discovery rate<0.1. nMorphine = 10; nPlacebo= 10; nOmez=8; nOmez+Morphine=8, NMDS, nonmetric multidimensional scaling.

Interestingly, bacterial communities that were significantly reduced in relative abundance in the morphine-treated group were substantially restored by omeprazole. An increased abundance of Bacteroidetes, Tenericutes, and Actinobacteria, and a decreased abundance of Firmicutes at the phylum level were found in the omeprazole-pretreated morphine group (Fig. 6C, D). Moreover, omeprazole restored the Bacteroidetes/Firmicutes ratio (Mor/omez+mor; Firmicutes p=0.0026; Bacteroidetes p<0.00037) (Fig. S9). Additionally, omeprazole pretreatment reduced the families Erysipelotrichaceae, Streptococcoceae, Pseudomonaceae, Coriobacteriaceae and Neisseriaceae that were increased by morphine treatment (Fig. 6E). Omeprazole pretreatment also increased the abundance of Lactobacillaceae at the family level and lactobacillus at the genus level (Fig. 6E), while decreasing the potentially pathogenic bacteria Streptococcus and Pseudomonas (Fig. 6F). Furthermore, omeprazole pretreated morphine group had decreased gram+ve bacteria (p=0.002) and increased gram-ve bacteria (p<0.002) compared to the morphine-treated group (Fig. S10).

Discussion:

Chronic use of opioids is associated with significant co-morbidities, including gastrointestinal problems. Due to the lack of a better alternative, morphine is still considered one of the best pain management drug (Pergolizzi et al., 2008). We have been actively working for a considerable time on understanding the phenomenon of, and deciphering the mechanism underlying the gastrointestinal side effects of the morphine (Wang & Roy, 2017). In the current study, we investigated the effect of morphine on gastric inflammation and the underlying mechanisms in morphine-associated gastric damage. Our data show that morphine treatment results in gastric dysbiosis that causes gastric inflammation through TLR2-mediated inflammatory pathway activation. The role of the microbiome in disease development and progression is becoming increasingly evident. Depletion or alteration of the gut microbiome is associated with various gastrointestinal and systemic diseases. Several studies have provided evidence that alterations of gastric microbiota are also associated with gastric diseases other than gastric cancer (Nardone & Compare, 2015). However, the overall knowledge of the roles of gastric microbes apart from H. pylori in gastric diseases is still limited. We have previously reported the role of the dysbiotic gut microbiome in morphine-induced gut barrier disruption. However, the role of morphine in gastric microbial dysbiosis and its association with gastric pathology has never been investigated.

In the current study, we found significant alterations in the gastric microbial community following morphine administration (Fig.1, S1). Morphine treatment resulted in gastric dysbiosis, featuring increased expansion of potentially pathogenic bacterial communities. Morphine also caused a decrease in α-diversity and distinct clustering in β-diversity (Fig.1, S1). The Bacteroidetes/Firmicutes ratio is one of the markers of pro-inflammatory changes in the microbiome (Mariat et al., 2009; Power et al., 2014). We previously reported that morphine treatment decreases the Bacteroidetes/Firmicutes ratio in the gut and that was associated with gut barrier disruption (Banerjee et al., 2016). In our current study, we observed that morphine skewed this ratio towards a pro-inflammatory phenotype (Fig. S2). Our current results also revealed that morphine treatment shifted the abundance of specific bacterial taxa, including Enterobacteriaceae (Enterococcus), Streptococcaceae (Streptococcus) and Pseudomonaceae (Pseudomonas). Notably, these families are comprised of several pathogenic taxa which are well documented in several inflammatory diseases (Zeng et al., 2017). It has been reported that patients with atrophic gastritis have an increased abundance of Streptococcus (Nardone & Compare, 2015). Patients with antral gastritis show a decrease of Proteobacteria and an increase of Firmicutes at the phyla level, and a significant increase of Streptococcus at the genera level has also been reported. Our study also showed a decrease in the bacterial family Lachnospiraceae and Ruminococcaceae in the morphine-treated groups. Certain Lachnospiraceae and Ruminococcaceae species were reported to down-regulate harmful inflammatory responses by the expansion of the regulatory T cells (Payen et al., 2020). Interestingly, morphine treatment also reduces the abundance of beneficial bacteria, such as lactobacillus which is well known for its anti-inflammatory properties (Azad et al., 2018). Hence, morphine-induced skewing of the microbial composition towards more pathogenic taxa may account for gastric inflammation. Our predictive functional metagenome analysis demonstrated that morphine significantly enriched several pathways related to human diseases, infectious diseases, staphylococcus aureus infection, and bacterial invasion of epithelial cells suggesting that morphine mice have an inflammatory microbiota (Fig. S4).

We found that morphine treatment resulted in significant disruption of the gastric mucosa and an increased number of apoptotic cells which signify gastric damage (Fig. 2, S5, S6). Cytokines play a key role in many inflammatory diseases, including gastric inflammation (Aziz et al., 2019; Kany et al., 2019). We found an elevated expression of IL-1β, TNF-α and IL-6 in morphine-treated mice gastric tissue (Fig. 2). These observations are consistent with other studies that indicate the administration of opioids, including morphine, augments gastric mucosal injury in a variety of experimental models (Esplugues & Whittle, 1990; Esplugues, Whittle, et al., 1992; Playford et al., 1991). However, the underlying mechanisms are not clear, and the contribution of the gastric microbiome was not investigated.

In the current study, we investigated the role of morphine-induced gastric dysbiosis as a major contributor in gastric inflammation. To validate our hypothesis, we treated GF mice with morphine and found that morphine-induced gastric inflammation was dramatically attenuated in the absence of the microbiome in the GF mice, thus establishing the potential role of the gastric microbiome in morphine-induced gastric inflammation (Fig. 3). Furthermore, germ-free mice gavage with dysbiotic microbiome from morphine-treated animals showed significant gastric damage, thereby supporting our hypothesis implicating the dysbiotic microbiome as a major driver for gastric damage.

Our bug base analysis revealed the enrichment of gram+ve bacteria following morphine treatment, leading us to speculate that TLR2 signaling is involved in morphine-mediated gastric inflammation (Fig. 4, S7). The role of TLRs is well established in mucosal pathogenic complications, and both TLR2 and TLR4 play a major role in morphine-mediated gut barrier compromise and inflammation (Banerjee et al., 2016; Zhang et al., 2019). We have previously reported that morphine treatment results in gut barrier compromise and translocation of predominantly gram-positive bacteria across gut mucosa (Banerjee et al., 2016). Interestingly, in this study, we found that morphine treatment increases TLR2 expression and that morphine-mediated gastric inflammation is diminished in TLR2KO mice (Fig. 4). This observation suggests that dysbiotic microbe-mediated TLR2 signaling plays a crucial role in morphine-mediated gastric inflammation. Thus ultimately, our data support a link between gastric dysbiosis and morphine-induced gastric inflammation.

The composition of the gastric microbiota depends on gastric acid secretion. Gastric acidity is a barrier to microbial overgrowth. Naylor et al. reported that bacterial overgrowth occurred when the gastric pH was >3.8 (Naylor & Axon, 2003). Acid-reducing drugs reportedly increase bacterial colonization in the stomach (Thorens et al., 1996). However, a number of potential pathogens have evolved multiple acid resistance mechanisms to increase their survival during gastric transit (Castanie-Cornet et al., 1999). We have previously noted that morphine treatment causes a decrease in α-diversity. Thus, we measured the pH of the stomach content in morphine-treated mice to unravel the underlying mechanism of morphine-induced gastric damage (da Silva et al., 2018). We showed a significant decrease in stomach pH after morphine treatment, which implies that gastric acid might play a role in morphine-induced gastric dysbiosis and the decrease in pH might lead to a decrease in α-diversity (Fig. 5). In line with our observation, Esplugues et al. has previously been reported a dose-dependent increase in the basal level of acid secretion by morphine (Esplugues, Barrachina, et al., 1992). However contradictory results exist. We think the dose and duration of morphine treatment and the species affect the outcome of morphine treatment in gastric acid secretion (Feldman et al., 1980; Konturek et al., 1980). Owing to the decrease in gastric pH after morphine treatment, dysbiotic gastric bacteria must combat acidic environments thus they must be resistant to low pH. We found bacterial colony count in the morphine gastric microbiome are similar at low pH and pH 7.5 culture. While in placebo bacterial colony significantly reduced under low pH culture condition (Fig. 5). This observation indicates that acid-tolerant bacteria are enriched in morphine gastric microbiome. Hence, the administration of opioid analgesic drugs causes gastric acidity and that may be one of the reasons for morphine-mediated gastric dysbiosis and subsequent gastric damage.

Omeprazole (PPI) is widely used to prevent gastric damage and is believed to offer its antiulcer activity through acid suppression (Biswas et al., 2003; Wilde & McTavish, 1994). Cumulative evidence has also suggested that omeprazole can act as an anti-inflammatory molecule (Kedika et al., 2009). However, the effects of omeprazole on morphine-induced gastric inflammation have not been studied. In the current study, we hypothesized that blocking gastric acid secretion through proton pump inhibitor might restrict morphine-mediated gastric dysbiosis. We found that omeprazole increased α-diversity and significantly shifted the β-diversity cluster towards the placebo (Fig. 6). Moreover, gastric acid inhibition increased the Bacteroidetes/Firmicutes ratio (Fig. S9). Omeprazole also reduced the morphine-mediated gram-positive bacteria enrichment (Fig. S10) and significantly decreased the abundance of pathogenic taxa Streptococcus and Pseudomonas, while increasing lactobacillus (Fig. 6). Hence, the pharmacological inhibition of gastric acid secretion enabled the survival and proliferation of other microbes that are normally killed by acid. PPI-mediated gastric microbiome alteration has been reported previously (Freedberg et al., 2014), but this is the first report showing morphine-induced gastric dysbiosis and its attenuation by omeprazole.

In conclusion, morphine-induced gastric damage is due to alteration of the gastric microbiome driven by acidic pH. The dysbiotic gastric microbiome induces gastric inflammation through TLR2-mediated signaling. While omeprazole rescued morphine-induced gastric damage by regulating gastric acid secretion and restoring normal gastric microbiota. Our study has clear clinical implications and suggests that omeprazole treatment at the time of morphine administration is a promising, safe, and inexpensive approach for reducing morphine-induced GI pathology.

Supplementary Material

spindo

BULLET POINT SUMMARY:

What is already known

  • Opioids are effective analgesics for pain management however results in adverse gastrointestinal side effect.

What this study adds

  • Morphine treatment significantly causes gastric dysbiosis, gastric pH alteration and results in gastric inflammation.

  • Morphine-induced gastric inflammation is alleviated by proton pump inhibition.

Clinical significance

  • Proton pump inhibitors may be utilized therapeutically for the clinical management of morphine-associated gastric pathophysiology.

Grant Support and acknowledgments:

The authors and their work were supported by National Institutes of Health Grants R01 DA050542, R01 DA047089, R01 DA044582, R01 DA043252, R01 DA037843, and R01 DA034582. We thank Dr. Maria T. Abreu for providing germ-free mice and facilities. We would also like to thank Dr. Valerie Gramling at the writing center of the University of Miami for assistance with manuscript editing.

Abbreviations list:

OBD

opioid-induced bowel dysfunction

GI

Gastrointestinal

TLR

Toll-like receptors

PPI

Proton pump inhibitors

GF

Germ-free

LDA

linear discrimination analysis

SPF

Specific pathogen-free

KEGG

Kyoto Encyclopedia of Genes and Genomes

Footnotes

Conflict of interest statement: The authors have declared that no conflict of interest exists.

Ethics and Integrity statements:

Declaration of transparency: This Declaration acknowledges that this paper adheres to the principles for transparent reporting and scientific rigour of preclinical research as stated in the BJP guidelines for Design and Analysis, Immunoblotting and Immunochemistry, and Animal Experimentation, and as recommended by funding agencies, publishers and other organizations engaged with supporting research.

Data Availability:

All data supporting the findings in the study are available upon reasonable request to the corresponding author. Sequence data are available at the NCBI database under BioProject accession number PRJNA746294.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

spindo
NIHMS1861880-supplement-spindo.docx (24.7MB, docx)

Data Availability Statement

All data supporting the findings in the study are available upon reasonable request to the corresponding author. Sequence data are available at the NCBI database under BioProject accession number PRJNA746294.

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