Acute and short-term administrations of delta-9-tetrahydrocannabinol modulate major gut metabolomic regulatory pathways in C57BL/6 mice
0000 0000 9075 106Xgrid.254567.7Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, SC USA
0000 0000 9075 106Xgrid.254567.7Department of Pathology, Microbiology, and Immunology, School of Medicine, University of South Carolina, Columbia, SC USA
Creative Proteomics Inc., Shirley, New York USA
0000 0000 9075 106Xgrid.254567.7Department of Statistics, University of South Carolina, Columbia, SC USA
0000 0000 9075 106Xgrid.254567.7Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC USA
0000 0001 2297 5165grid.94365.3dNational Institutes of Health, Bethesda, MD USA
0000 0000 9075 106Xgrid.254567.7Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC USA
0000 0000 9840 6850grid.417757.7National Ocean Service, Hollings Marine Laboratory, Charleston, SC USA
Abstract
Delta-9-tetrahydrocannabinol (THC) is the primary psychoactive compound in Cannabis, which is studied extensively for its medicinal value. A central gap in the science is the underlying mechanisms surrounding THC’s therapeutic effects and the role of gut metabolite profiles. Using a mass-spectrometry based metabolomics, we show here that intraperitoneal injection of THC in C57BL/6 mice modulates metabolic profiles that have previously been identified as integral to health. Specifically, we investigated the effects of acute (single THC injection denoted here as ‘1X’) and short -term (five THC injections on alternate days denoted as ‘5X’) THC administration on fecal and intestinal tissue metabolite profiles. Results are consistent with the hypothesis that THC administration alters host metabolism by targeting two prominent lipid metabolism pathways: glycerophospholipid metabolism and fatty acid biosynthesis.
Introduction
The medical use of Cannabis (commonly termed ‘marijuana’), a product from the plant, Cannabis sativa, is becoming increasingly popular worldwide for its medicinal value1–3. In the USA, 33 states and District of Columbia have already legalized the medical use of marijuana4. Delta-9-tetrahydrocannabinol (THC) that was first described in 1964 is the primary psychoactive compound in Cannabis and is known to display therapeutic potentials as an analgesic, antiemetic and appetite stimulant2,3,5–8. Additionally, THC can be used for the treatment of multiple acute and chronic health disorders9–11. These include treatment of nausea and vomiting associated with cancer chemotherapy, anorexia, and cachexia associated with HIV and AIDS patients, pain and muscle spasms in multiple sclerosis12. Their anti-inflammatory effects have been tested in experimental models for autoimmune disorders such as multiple sclerosis, rheumatoid arthritis, colitis, hepatitis and cancer9. Importantly, during the past several years, THC content in marijuana has been steadily increasing13,14; and recreational use has expanded15. Hence, the scientific premise for studying the mechanisms involved in the modulation of disease by THC is strong.
It is well established that THC can be useful in preventing and ameliorating the symptoms of intestinal inflammation such as abdominal pain, diarrhea and reduced appetite in patients suffering from inflammatory bowel disease (IBD)16–19. In animal models of colitis, THC demonstrated successful reduction of 2,4,6-Trinitrobenzene sulphonic acid-induced mucosal damage of the intestine, neutrophil infiltration and in vitro motility disturbances. It has been shown20–22 that the epithelial wound healing, inhibition of pro-inflammatory cytokine and chemokine release, immune cell recruitment, are mediated through activation of cannabinoid receptors (CB1 and CB2) of the endogenous cannabinoid system. Activation of these receptors is critical in the neuromodulatory actions on the sensory and autonomic nervous system connected with the pharmacological function of THC23.
Despite such well-documented evidence supporting THC’s therapeutic impact in safeguarding the healthy functioning of the intestine, the entire mechanism underlying the intestinal protection and other pharmacological effects of THC still remains unclear. This gap in understanding is at least in part due to the absence of knowledge on whether THC influences host metabolism and how. The knowledge of the key metabolic targets of THC is critical for appropriate therapeutic applications of THC. Hence the main objective of this study was to investigate the influence of acute and short-term THC administration on fecal and intestinal tissue metabolome of wild-type C57BL/6 mice. A comparative untargeted metabolome profiling of fecal and intestinal tissue samples obtained from THC administered mice and control mice that received vehicle instead, was conducted using an ultra-performance liquid chromatography-time of flight-mass spectrometry (UPLC-TOF-MS). Presented in this report are the results of this investigation.
Materials and Methods
Experimental animals, diet and THC administration
Female C57BL/6 (BL6) mice, aged 8–10 weeks, obtained from Jackson Laboratories were used for this study. All mice were housed in pathogen-free conditions and allowed ad libitum access to filtered water and Teklad rodent diet 8604 (regular chow) at the Animal Research Facility located at the University of South Carolina School of Medicine. To understand the effect of THC on gut metabolites we have followed our previously published protocols of THC administration24–28, whereby we injected 20 mg/Kg THC intraperitoneally. The experimental group (n = 5) received THC every 48 h. THC was dissolved in a vehicle of 100% ethanol, and both treatment and vehicle were administered in 100 µL of a combination of ethanol, Tween-80, and saline, at a ratio of 2:1:17. Animals were regularly monitored during the period of the experiment for any body weight changes, signs of toxicity and mortality. Fecal samples analyzed for comparative metabolomics were collected 24 h after the first administration (denoted here as 1X) and 24 h after the 5th administration (denoted here as 5X). The 1X samples and 5X samples were used to study respectively, the acute and short-term effects of THC administration.
The rationale for using an intraperitoneal mode of exposure was the fast bioavailability of THC in the bloodstream, due to which we have consistently used this model25–27 as the closest intravenous self-administration paradigm because THC is unable to sustain in rodents upon intravenous administration29. As in our previous studies25–27, we reasoned here as well, that since i.p. administration would allow the THC to directly diffuse across the peritoneal membrane to the blood vessels of the abdominal viscera, musculature and mesentery, it would help in avoiding any possible artifacts resulting differential nutrient absorption rates caused by oral administration of THC.
The dose of THC in this study and our previous studies was determined according to body surface area normalization based calculations described earlier by Reagan-Shaw et al.30. Based on these calculations, our applied 20 mg/kg THC dose translates to 60 mg/m2 in humans, which is well within the maximum human recommended dose (MHRD) of synthetic THC of 90 mg/m2/day. We point out here that THC (also termed Dronabinol) has consistently been used for clinical use to reduce neuropathic pain in multiple sclerosis patients31–33. No teratogenic effects was reported in mice administered THC at up to 30 times the MRHD and up to 5 times the MRHD for patients with AIDS and cancer, respectively (see FDA data34). We have chosen mice of a single gender for our experiments to rule out any previously reported sex difference effects on THC metabolism35,36. Given that the prevalence of multiple sclerosis is about two to three times higher in women than men37, we have chosen female mice for this study.
Ethics statement
The mice employed in this study were housed at the American Association for the Accreditation of Laboratory Animal Care (AAALAC)-accredited Animal Resource Facility at the University of South Carolina, School of Medicine, Columbia, SC. All experimental procedures were performed according to National Institutes of Health (NIH) guidelines under protocols approved by the University of South Carolina Institutional Animal Care and Use Committee.
Sample size determination and statistical analyses
We used power analysis to determine the ideal sample size for our experiments. With the assumption of a normal distribution, a 20% change in mean and 15% variation in THC effect on gut metabolome, we determined that a sample size ≥4 would be required per group to surpass 80% power for the study, given that we used concurrent controls for the study. Hence, have used 5 animals per group. Metabolites that showed significant differences between THC administered and control (vector administered) groups were identified by combining the results of students t-test (p < 0.05), fold change (FC > 2) and variable importance in projection values (VIP > 1). To address the cases when the quantified metabolites fail to satisfy the normality and equality of variance based on Kolmogorov-Smirnov test and Levene’s test respectively, we used the non-parametric Kruskal-Wallis test to determine metabolite differences between the THC-administered and the vector-control groups. The acquired p-values were corrected for multiple testing using Benjamini and Hochberg False Discovery Rate (FDR)40, a method applied previously for untargeted metabolomic analysis41.
Results
Analysis of fecal metabolite profile shifts upon acute THC administration
The experimental design of the experiment is illustrated in Fig. 1. To understand the acute effects of THC administration on gut metabolite profiles in C57BL/6 mice, we compared their fecal metabolite profiles 24 h after THC administration (sampling time point denoted here as ‘1X’). The results from our initial examination of base peak intensity chromatographs (SI Fig. 2) are shown as volcano plots in Fig. 2a, which indicated a shift in the profiles upon THC administration. An unsupervised evaluation of metabolic signatures was conducted using the indices PCA1 and PCA2 that were obtained upon PCA analysis by reduction of the multi-dimensional datasets to optimized and comparable datasets. As shown in Fig. 2b, the PCA scores scatter plot demonstrates a clear separation between the THC-treated and control fecal samples. Following PCA analysis, the identification of differential metabolites was performed using supervised partial least square- discriminant analysis (PLS-DA) on the MS data to predict the class membership and assess the significance of class discrimination. As shown in Fig. 2c, the PLS-DA scores scatter plot suggested that THC administration again demonstrates a significant class separation between the THC-treated and control fecal samples.
Analysis of fecal metabolite profile shifts upon short-term THC administration
To understand the short-term effects of THC administration on gut metabolite profiles in C57BL/6 mice, we compared fecal metabolite profiles 24 h after five THC injections(sample denoted here as ‘5X’). The volcano plot (Fig. 3a) summarizing our initial examination of the fecal metabolites indicates a shift in fecal metabolite profiles with this multiple exposure protocol (base peak intensity chromatographs of fecal metabolites of 5x are shown in SI Fig. 2). The separation between the fecal metabolite profiles of the THC administered and control groups was further confirmed by the PCA scores scatter plot (Fig. 3b). Finally, the supervised PLS-DA scores obtained from the MS data was used to predict the class membership and demonstrate the significance of class discrimination (Fig. 3c).
Identification of potential biomarkers
From the comparisons of fecal metabolite profiles between THC administered and the control groups, a group of ‘significant metabolites’ was identified using the criteria of VIP value > 1. The higher VIP values were indicative of a higher contribution from these metabolites toward the differential profiles between the THC and the control groups. A list of these metabolites from the comparisons of 1x with their controls and the 5x with their controls is provided in Table 1 a and b. Along with the VIP values for each metabolite, Table 1 also indicates the fold change of the increase or decrease in the metabolite concentration upon THC administration. The list of identified significant metabolites that were identified differentially upon THC administration was entirely different between samples 1X and 5X, suggesting that acute and short-term administrations have different functional impacts on the mouse gut.Metabolite Name (§) Associated metabolic process (*) Molecular Mass +/− Fold Change Adjusted P value
Fecal metabolites showing differential abundance in 1X samples
PE(18:3(6Z,9Z,12Z)/0:0) Glycerophospholipid metabolism 474.2582 + 550.97 0.0049 Methylxanthine Caffeine metabolism 189.0431 + 2959.7 0.006 DG(22:5(4Z,7Z,10Z,13Z,16Z)/20:3(5Z,8Z,11Z)/0:0) Glycerophospholipid metabolism 691.5149 + 2793.4 0.006 PS(20:5(5Z,8Z,11Z,14Z,17Z)/20:1(11Z)) Glycerophospholipid metabolism 834.5193 + 2729.4 0.006 N-Acetylcarbocysteine Unknown 220.0348 + 351.89 0.006 6-Hydroxymelatonin Tryptophan metabolism 249.1269 + 618.81 0.0272 PG(20:4/0:0) Glycerophospholipid metabolism 531.287 + 987.45 0.0356 Asp Ser Gln Unknown (possibly endogenous opioid peptide synthesis) 347.1231 + 63.882 0.037 Pro Tyr Val Unknown (possibly endogenous opioid peptides synthesis) 376.1897 + 850.9 0.045 PS(O-16:0/13:0) Glycerophospholipid metabolism 678.4848 − 1470.5 0.003 S-Prenyl-L-cysteine Unknown 187.0634 − 8333.3 0.005 PC(6:0/6:0) Glycerophospholipid metabolism 424.2828 − 400 0.006 2E-methyl-glutaconic acid Valine, Leucine, Isoleucine degradation 145.0528 − 909.1 0.007 Tiglylglycine Isoleucine degradation 158.0826 − 500 0.011 2-Methyleneglutarate Nicotinate and Nicotinamide metabolism 145.0527 − 20 0.045
Fecal Metabolites showing differential abundance in 5X samples
Metabolite Name (§)
Associated Metabolic process (*)
M/Z
+/−
Fold Change
Adjusted P Value
Sphingosine Sphingolipid metabolism 317.3092
+
35.5 0.04 2S-Hydroxytetradecanoic acid Fatty acid biosynthesis 244.370
+
8202.6 0.009 Beta-Hydroxy palmitic acid Fatty acid biosynthesis 272.429
+
10835 0.01 Dodecanamide Fatty acid biosynthesis 217.2332
+
2.8 0.03 1-Phenyl-1,3-eicosanedione Unknown 387.3372
+
70.286 0.01 N-stearoyl taurine Taurine and Hypotaurine metabolism 390.2781
+
445.28 0.02 2-Hydroxyenterodiol Unknown 336.1884
−
200 0.005 Hydroxymalonate Unknown 138.0423
−
4.4 0.03 PA(17:2/22:2) Lipid metabolism 797.5431
−
3333.3 0.0001
Interpretation of metabolic signatures
To investigate the latent relationships of the differential metabolites listed in Table 1 and gain insights into metabolite enrichment representing specific metabolic pathway and molecular network perturbations induced by THC exposure, the information of the significant metabolites was imported to the MBRole platform. The enrichment analysis of the metabolic signatures was performed on Mus musculus background using annotations from the KEGG42 and HMDB databases43. While the former database primarily annotates metabolites with their associated pathways and enzymes, the later annotates metabolites with diseases, pathways, tissues, biofluids and the cellular localization. Figure 4 summarizes our observations from this enrichment analysis and indicates the potential metabolic processes that are impacted by THC administration, which in turn, could help explain its therapeutic impact on many diseases. Specifically, acute administration of THC demonstrated a positive correlation with the metabolic intermediates of Glycerolipid metabolism, PI3K/AKT/mTOR and lysophopholipid signaling, opioid peptide biosynthesis and endocannabinoid signaling. Also, the acute administration was negatively correlated with the metabolite intermediates of nicotinate degradation. Nicotinate being the precursor for the generation of nicotinamide adenine nucleotide (NAD+)44, our study suggests a positive effect of THC on cellular NAD+ levels and upregulation of systemic NAD+ is demonstrated to have profound health beneficial effects45–47. Short-term administration of THC was positively correlated with the fatty acid degradation pathway, sphingosine metabolism, caffeine metabolism as well as endocannabinoid signaling. Additionally, the short-term THC administration was negatively correlated with xenobiotic metabolism. These processes have significant cross-talks with several important metabolic processes such as amino acid, carbohydrate, and nucleotide metabolism. Specifically, the glutathione metabolism and branched chain amino acid (BCAA) pathways are highly enriched. Interestingly, dysregulation of these metabolic pathways are known to be associated with the etiology of diabetes, obesity, cancer, and neurodegeneration48–50. Our future work will investigate the specific metabolite changes in various preclinical models such as mice models for acute and short-term intestinal inflammation. Those studies will further elucidate the mechanistic details of the THC-mediated health benefits.
Discussion
This study highlights the metabolic changes induced by acute and short-term administration of THC in the gut of a murine model that has historically been used to demonstrate the positive health impacts of THC. To study these metabolic changes, comparative metabonomic profiling of fecal samples of THC-administered mice, and vector-administered mice were performed using a highly sensitive, accurate, and precise UPLC-ESI-QTOF-MS-based approach that has broad applications in metabonomic studies51,52.
With this, we have shown here that lipid metabolism, especially glycerophospholipid metabolism and fatty acid biosynthesis, is a key metabolic pathway targeted by THC following i.p. administration. Importantly, this pathway is intricately connected with several health disorders that are protected by THC; examples include Parkinson disease53, schizophrenia54, brain ischemia55, multiple sclerosis56 and cancer development57,58. Glycerophospholipids are precursors for several lipid mediators that, in collaboration with sphingolipids, participate in major signal transduction processes (see review by Farooqui et al.59) and along with sphingolipid metabolism, are functionally linked with several physiological and pathophysiological conditions that include but are not limited to pain, inflammation, metabolic syndrome, fibrosis, fertility, cancer and autoimmune and neurodegenerative disorders60. Others have also shown that glycerophospholipid and sphingolipid metabolism are the most significantly impaired pathways associated with the atherosclerosis progression61,62. Much of the protective role of cannabinoids on atherosclerotic coronary heart disease involves 15-lipoxygenase inhibitory activity, which in turn prevent lipid peroxidation, oxidative stress and atherosclerosis63. Based on our findings it is reasonable that THC-mediated protection against atherosclerosis and cardiovascular disorders can be linked to its regulatory effects on of glycerophospholipid and sphingolipid metabolism. We have conducted this study using adolescent mice to keep experimental consistency with our previous reports. While this age may seem irrelevant for some of the neurological disorders discussed above, we point out here that both young and adult mice have been used to understand the therapeutic impacts of THC on neuroinflammation and the associated health disorders such as autoimmune encephalitis64, Alzheimer’s disease65 and Parkinson’s disease66. Interestingly the increase in anti-inflammatory cytokine release in the brain of young mice can be mimicked by peripheral immune cells67.
Fecal metabolomics revealed an influence of THC on some additional major metabolic pathways which although connected with lipid metabolism, were not highlighted in our tissue metabolomic study. For example, a critical metabolite that feeds into Sphingolipid metabolism is L-serine, which is a metabolic output from the glycine, serine, and threonine metabolism68. The glycine, serine and threonine metabolic pathway feeds phosphatidylethanolamine to glycerophospholipid metabolism69. An upregulation of sphingolipid and glycerophospholipid metabolism, therefore, suggests an upregulation in Serine metabolism as well. Reduction of 2E-methyl glutaconic acid and tiglyglycine was observed upon 1X administration. These metabolites are often detected in human urine samples when the catabolism of branched-chain amino acids (BCAA) (especially isoleucine) is impaired70,71, suggesting that THC possibly influences BCAA catabolism. Emerging evidence supports the importance of BCAA catabolism in lowering the risk of type-2 diabetes72. While a previous study has shown that cannabidiol significantly reduces the incidence of diabetes in non-obese diabetic mice73, the relation between marijuana use and diabetes remains unclear. We also noted a significant reduction 2- methylene glutarate upon 1X administration suggesting downregulation of the metabolite flow from nicotinate degradation into pyruvate metabolism. This observation is in line with two recent reports that demonstrate a modulatory effect of cannabinoids and cannabinoid receptors on pyruvate (and energy) metabolism: (i) a report from Mendizabal-Zubiaga et al.74 which showed that expression of pyruvate metabolism genes increased in the striated muscle cells of CB1-knockout mice and, (ii) a report by Arrabal et al., which showed that pharmacological blockage CB1 was able to upregulate pyruvate metabolism enzymes75. It is hypothesized that such modulatory effects of THC and cannabinoids on energy metabolism may in part, contribute to their anti-tumor effects. Finally, an increased occurrence of two endogenous peptides upon 1X administration suggesting an activation of the endogenous opioid system. These peptides have receptors widely distributed in the central and peripheral nervous system and play key roles in immunity76, pain modulation64, emotion and stress response65, gut functioning66, neuroprotection with important implications in Parkinson’s disease67.
We point out here that, our study being untargeted in nature had three limitations that are typical for untargeted metabolomics: (1) a bias toward high-abundant metabolites (typical for LC-MS/MS), (2) the influence from exogenous metabolites such as those from gut microbiota (a common issue in fecal metabolome analysis) and (3) high-throughput analysis of samples without authentic standards, which although gives the advantage of the absence of a priori decisions, may lead to quantitative inaccuracy and in some cases compromise metabolite identity. The very high fold changes of enriched metabolites in fecal metabolite profiling could be either reflective of the influence of gut microbial metabolites while the differential abundance of certain metabolites only at one time point may indicate a bias toward high abundant metabolites. Regardless of these limitations, the strength of our study was our ability to conduct a comparative metabolomic examination of the fecal and intestinal tissue matrices (THC treated versus non-treated animals) in a holistic unbiased manner, which was helpful to test our central hypothesis and obtain a global understanding of how THC influences the host metabolic network. This provides us a scientific premise for developing new hypotheses for our future targeted metabolomic studies with diseased models. Such studies will focus on the cause-effect nature of the relationship between THC and the metabolic pathways identified in this study, under different pathophysiological conditions.
Supplementary information
Untitled section
Supplementary information
Supplementary information accompanies this paper at 10.1038/s41598-019-46478-0.
Acknowledgements
A pilot project award to A.C. from the Center of Biomedical Research Excellence (Center for Dietary Supplements and Inflammation) at University of South Carolina, Columbia, funded this study. The parent grant # 1P20GM103641 from the National Institutes of Health (NIH) and the National Institute of General Medical Sciences (NIGMS) to P.N. funded this pilot project. Creative Proteomics Inc. provided support in the form of salary for author L.C., but did not have any role in the research design, data collection and analysis, and preparation of the manuscript.
Competing Interests
The authors declare no competing interests.