Cannabis Extract Effects on Metabolic Parameters and Gut Microbiota Composition in a Mice Model of NAFLD and Obesity

Zandani, Gil; Anavi-Cohen, Sarit; Assa-Glazer, Tal; Gorelick, Jonathan; Nyska, Abraham; Sela, Noa; Bernstein, Nirit; Madar, Zecharia

doi:https://doi.org/10.1155/2022/7964018

Evidence-Based Complementary and Alternative Medicine

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Abstract Introduction Materials and Methods Results Discussion Conclusions Abbreviations Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 7964018 | https://doi.org/10.1155/2022/7964018

Cannabis Extract Effects on Metabolic Parameters and Gut Microbiota Composition in a Mice Model of NAFLD and Obesity

Gil Zandani,¹Sarit Anavi-Cohen,²Tal Assa-Glazer,¹Jonathan Gorelick,³Abraham Nyska,⁴Noa Sela,⁵Nirit Bernstein,⁶and Zecharia Madar¹

Academic Editor: Duygu Ağagündüz

Received08 Apr 2022

Revised26 May 2022

Accepted28 May 2022

Published26 Jun 2022

Abstract

Nonalcoholic fatty liver disease (NAFLD) is a major cause of chronic liver abnormalities and has been linked with metabolic syndrome hallmarks. Unfortunately, current treatments are limited. This work aimed to elucidate the effects of three cannabis extracts on metabolic alteration and gut microbiota composition in a mouse model of NAFLD and obesity. Male mice were fed with a high-fat diet (HFD) for 12 weeks. Following the establishment of obesity, the HFD-fed group was subdivided into HFD or HFD that was supplemented with one of three cannabis extracts (CN1, CN2, and CN6) for additional 8 weeks. Metabolic parameters together with intestinal microbiota composition were evaluated. Except for several minor changes in gene expression, no profound metabolic effect was found due to cannabis extracts addition. Nevertheless, marked changes were observed in gut microbiota diversity and composition, with CN1 and CN6 exhibiting microbial abundance patterns that are associated with more beneficial outcomes. Taken together, specific cannabis extracts’ addition to an HFD results in more favorable modifications in gut microbiota. Although no marked metabolic effect was disclosed, longer treatments duration and/or higher extracts concentrations may be needed. More research is required to ascertain this conjecture and to establish the influence of various cannabis extracts on host health in general and NAFLD in particular.

1. Introduction

Nonalcoholic fatty liver disease (NAFLD) is currently the most common liver disease in the Western world, affecting ∼8–45% of the population, globally. NAFLD comprises a wide spectrum of liver pathologies ranging from simple steatosis, which is generally considered benign, through steatohepatitis, cirrhosis, and liver hepatocellular carcinoma [1]. As indicated by its name, the establishment of hepatic fat accumulation and subsequently further perturbations under this state evolve in the absence of excessive alcohol consumption, autoimmune, infectious, or other established liver diseases [2]. NAFLD is ranked as the second most common cause of hepatocellular carcinoma [3] and is implicated as a risk factor for the development of other metabolic-related pathologies, such as cardiovascular diseases, type 2 diabetes, and chronic kidney disease [4]. This emphasizes the relevancy of this disease as a major health problem worldwide and further calls for the development of new, suitable preventative and clinical treatments. Unfortunately, current treatments are restricted, which poses a major challenge for national health services. In the lack of approved pharmaceutical treatment [1], presently available therapeutic approaches focus predominantly on lifestyle modification, diet, and exercise interventions, thus aiming mainly at controlling body weight (BW) and metabolic syndrome-related cardiovascular risk factors [5]. In advanced stages of NAFLD, high genetic risk, or in the presence of diabetes, intensified lifestyle intervention may be accompanied by secondary pharmacological treatment if necessary.

Throughout human history, plants have been used as a source of medications. The species Cannabis sativa L. includes three subspecies: Cannabis sativa, Cannabis indica, and Cannabis ruderalis. The unique therapeutic effects of cannabis are hypothesized to be achieved through a complex synergy between the multiple phytocannabinoids and many other secondary compounds of the plant, including terpenes and flavonoids [6, 7]. Cannabinoids are the subject of ongoing active research in the field of natural pharmaceutical agents, with evidence implying their potential effectiveness for the treatment of inflammation, cancer, and cardiovascular disorders [8–10]. Furthermore, cannabis usage is associated with a reduced risk for metabolic diseases such as obesity [11], diabetes [12, 13], and NAFLD [14, 15].

The interconnection between gut microbiota and the liver in general and NAFLD specifically is of great interest. Dysbiosis has been implicated as a substantial environmental factor involved in the pathogenesis of NAFLD [16]. In patients with NAFLD, the microbiome abundance [17] and community structure are altered [18, 19], suggesting that the gut microbial population is important in the prevention and treatment of NAFLD. However, the effect of cannabis plant extracts on the pathogenesis and progression of NAFLD is not yet clear. Thus, this investigation aims to examine the potential of different cannabis strains that differ in their phytocannabinoids profile as a supplement in food on mice induced NAFLD model and evaluate changes in metabolic parameters, liver fat accumulation, and gut microbiome composition.

2. Materials and Methods

2.1. Cannabis Plant Extract Preparation, Decarboxylation, and Analysis

Three varieties of Cannabis plants were selected for the study based on the composition of the cannabinoids delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) as previously described [20]. Briefly, inflorescence from three strains was dried and cured over 4 weeks under 19°C and 40% reactive humidity. Ten grams of dry material were sampled from 10 different inflorescences for each strain, from 10 replicated plants per strain (CN1, CN2, and CN6) and ground into powder. Three grams of ground material were mixed with 30 ml ethanol for 60 min and centrifuged. The supernatants were filtered, dried, and stored at -20°C until further use. In order to convert the cannabinoids from their natural acidic state to their active neutral form, decarboxylation at 110°C for 60 min was performed. Dried extracts were heated, redissolved in ethanol, and chemical analysis of the cannabinoids content of the decarboxylated extracts was performed. 10 µl from each decarboxylated extract was syringe filtered (0.22 µm), diluted in methanol (1:100), and injected into a Waters Alliance 2695 Separation Module with a Waters 996 Photodiode Array Detector together with a Micromass Quattro Micro Triple Quadrupole Mass Spectrometer. Chromatographic separation was achieved using a PhenomenexKinetex C18 column (2.6 μm, 150 mm×3 mm i.d.) with guard column and a binaryA/B gradient (solvent A: water with 0.1% formic acid, and solvent B: MeOH with0.1% formic acid). Initial conditions were 65% B for 10 min, raised to 95% B over the next 20 min, held at 95% B for 15 min, decreased to 65% B over the next 5 min, and held at 65% B for 10 min for re-equilibration of the system. The flow rate was 0.2 mL/min and the column temperature was 30°C. MS acquisition was carried out in the ESI positive ionization: capillary voltage -3.5kV, cone voltage - 45V, extractor voltage – 3V, RF lens - 0.2V, source temperature -120°C, desolvation temperature - 350 °C, and nitrogen flow rate of 700 L/h. For each compound, serial dilutions were performed using standard solutions of the selected cannabinoids, obtained from Sigma-Aldrich, and calibrations curves generated for concentrations from 0.5-100 µg/ml using SIR of the molecular ion[M+H]. Quantification of cannabinoids in the extracts was performed based on the external calibration curves. The plant extract from the CN1 strain was rich in CBD (5 mg/ml), the plant extract from CN2 rich in THC (5 mg/ml while the plant extract from CN6 contained comparable concentrations of both phytocannabinoids (2.5:2.5 mg/ml).

2.2. Experimental Animals and Diets

The experiment protocols and procurers were performed within the guidelines of the Authority for Biological and Biomedical Models and approved by the Institutional Animal Care Ethics Committee, both of the Hebrew University of Jerusalem (AG-14922-2). 48 male C57BL/6 J mice, 4-5 weeks old, were purchased from Harlan Laboratories (Jerusalem, Israel). Following the acclimatization period, mice were randomly divided into two experimental groups: (1) mice fed a normal diet (ND, n = 8) and (2) mice fed a high-fat diet (60% kcal, HFD, n = 40) for 12 weeks. At week 13 of the experiment, HFD mice were divided into 4 groups receiving the following diets for 8 weeks: (1) mice continuing to receive HFD (HFD, n = 8), (2) mice fed HFD supplemented with 5 mg/kg·BW of CN1 plant extract (HFD + CN1, n = 10), (3) mice fed HFD supplemented with 5 mg/kg·BW of CN2 plant extract (HFD + CN2, n = 10), and (4) mice fed HFD supplemented with 5 mg/kg·BW of CN6 plant extract (HFD + CN6, n = 10). The amount (mg) of active ingredient was calculated and refers to the most common cannabinoid in each cannabis extract strain, which was incorporated in mice food. Mice were housed under a controlled environment (12/12 h light/dark cycle, 18–24°C, humidity 60%) and provided with ad libitum access to food and water. The diet compositions are presented in Table 1. BWs and food intake were recorded weekly.

2.3. Oral Glucose Tolerance Test (OGTT)

Glucose-loading tests were conducted at weeks 11 and 19 of the experimental period. Prior to the OGTT, the mice fasted for 12 hours and were given D-glucose (3 g/kg BW) via gavage. Blood was drawn from the tail tip at 0, 30, 60, and 120 min after the glucose loading was used to monitor glucose levels by a glucometer (Optimum Xceed).

2.4. Animal Sacrifice and Organ Collection

At the end of the experiment, mice fasted overnight and their BW was recorded thereafter. Mice were sacrificed in a random order by isoflurane (Minard Inc., USA) anesthesia. Blood was collected from the vena cava, centrifuged at 8,000 rpm at 4°C for 10 min, and stored at −80°C. Adipose tissue was removed, weighed, placed in liquid nitrogen, and stored at −80°C. Liver tissue was collected and weighed. A small sample from the right liver lobe was placed in 4% formaldehyde, and the remaining liver tissue was minced in liquid nitrogen and stored at −80°C. The ceca were separated from the large intestines and their contents were collected for microbiota analysis.

2.5. Biochemical Analysis of Serum Parameters

Analysis of serum alkaline phosphatase (ALP), serum alanine aminotransferase (ALT), and serum aspartate aminotransferase (AST) were measured by an automated clinical chemistry analyzer along with total cholesterol, high-density lipoprotein (HDL), and total triglycerides (TG) (American Laboratories Ltd., Herzliya, Israel). Total lipid was extracted from livers using the Folch method [21].

2.6. Liver Histology Examination and Grading

Histological slides were prepared and stained with hematoxylin and eosin (H&E) by Patholab (Rehovot, Israel) as described in [22]. Histopathological examinations were carry out by Dr. Abraham Nyska, DVM, Dipl. ECVP, Fellow IATP, board-certified in toxicologic pathology—https://ebvs.eu/colleges/ECVP/members/prof-abraham-nyska. Examination and scoring of the histopathological changes were done by the study pathologist using semiquantitative grading of five grades (0–4), according to the severity of the changes according to a generic grading criterium: 0 = no lesion; 1 = minimal change; 2 = mild change; 3 = moderate change; and 4 = marked change.

2.7. Western Blot Analysis

Liver tissue lysates were prepared using lysis buffer as previously described [23]. Aliquots of protein were then subjected to western blot analysis. Ponceau S (Sigma) staining was used to verify equal loading and transfer [24, 25]. Blots were respectively incubated with dilutions of primary antibodies: anti-rabbit AMPK, anti-pAMPK (Thr-172), anti-cannabinoid receptor 1 (CB1), and anti-cannabinoid receptor (CB2) (Abcam) at 4° overnight. After several washes, the membranes were incubated with a secondary goat antibody (Jackson Immuno-Research Laboratories, West Grove, PA, USA). The immune reaction was detected by enhanced chemiluminescence, with bands being quantified by densitometry and expressed as arbitrary units.

2.8. Quantitative Real-Time PCR

Total RNA was isolated from liver tissues using Tri-Reagent (Sigma-Aldrich, Rehovot, Israel) method, according to the manufacturer’s protocol. Complementary DNA was prepared with the High-Capacity cDNA Reverse Transcription Kit (Quanta BioSciences, Gaithersburg, MD, USA). Real-time polymerase chain reaction (PCR) was performed using the 7300 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA), using specific primers as follows: glucose 6-phosphatase (G6pase); peroxisome proliferator-activated receptor alpha (PPARα); a cluster of differentiation 36 (CD36); peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α); and phosphoenolpyruvate carboxykinase (PEPCK). Fold change in gene expression was determined by normalizing to 18S mRNA. The primer sequences are listed in Table 2.

2.9. Gut Microbiota Analysis

The effects of each diet on the gut microbiome population were examined with the analysis of the prokaryotic 16S ribosomal RNA gene (16S rRNA) as previously described [26]. Sequences with 97% similarity were assigned to the same operational taxonomic units (OTU). OTUs of representative sequences at a similarity of 97% and their relative abundances were used to calculate and analyze rarefaction curves and bacterial abundance at all taxonomical levels. Bacterial richness and diversity within samples were classified by α-diversity indexes.

2.10. Statistical Analysis

Results are presented as mean ± standard error of mean (SEM). Data were analyzed by the JMP 14 Pro software suites (SAS Institute, Cary, NC, USA). Comparisons between groups were made by one-way analysis of variance (ANOVA) followed by a Tukey–Kramer test or by an unpaired two-tailed Student’s t-test. Statistical significance was defined at .

3. Results

3.1. BW, Food Intake, Tissues Weight, and Liver Steatosis

Mice were divided into two groups, which were fed with ND or HFD. Twelve weeks of HFD-regime resulted in a significant increase in BW (Figure 1(a)) despite a decrease in food intake by this group (Figure 1(b)). Increased BW was accompanied by increased insulin resistance as indicated by higher fasting blood glucose levels and reduced glucose tolerance (Figure 1(c)). For the next 8 weeks (weeks 13–20), HFD-fed mice were subdivided into four groups receiving an experimental diet supplemented with extracts from different cannabis varieties (CN1, CN2, or CN6) or vehicle. BW equally increased, while food intake decreased in all HFD-fed groups, regardless of cannabis extract addition (Figures 2(a) and 2(b)). Liver and adipose tissue weight tended to be or were significantly higher in all HFD-fed groups, respectively (Figures 2(c) and 2(d)). Yet, the group in which HFD was supplemented with CN6 extract demonstrated lower adipose tissue weight compared to other HFD-fed groups. To further appraise the influences elicited by cannabis extracts addition to a HFD, additional analyses of liver histology and blood biochemistry were carried out. A similar grade of liver steatosis was disclosed by histology in all HFD-fed groups and was also corroborated biochemically (Figures 3(a)–3(c)). Consistently, blood liver enzymes levels were unaltered by all treatments, including HFD per se (Table 3). Furthermore, histopathological changes were scored (Figure 3(b)) and show a decrease in the mean severity of centrilobular hepatocytic vacuolation, consistent with fatty change, in the CN1- and CN6-treated groups when compared to the HFD alone group.

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Figure 2

Effect on BW gain, food intake, liver, and adipose tissues weight, and glucose tolerance. Male C57BL/6 J mice fed a high-fat diet supplemented with CN1 or CN2 or CN6 extracts 5 mg/kg BW for 8 weeks. (a) Changes of BW throughout cannabis extracts supplementation. (b) Average food intake per mice per day over the cannabis extract supplementation period; (c) liver weight at sacrifice; (d) adipose tissue weight at sacrifice. (e) Glucose levels between 0 and 120 min during oral glucose tolerance test. All values are expressed as mean ± SEM (n = 8–10). Columns marked with different letters are significantly different ().

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Figure 3

Effect of cannabis extracts on the liver in mice fed with a high-fat diet. (a) Representative liver H&E staining. (b) Steatosis grade. (c) Total lipids in 100 mg liver. All values are expressed as mean ± SEM (n = 8–10). Columns marked with different letters (a, b) are significantly different (). Histopathological changes were scored by the study pathologist, using semiquantitative grading of four grades (0–4), taking into consideration the severity of the changes (0: no lesion; 1: minimal change; 2: mild change; 3: moderate change; and 4: marked change).

3.2. Blood Lipids Profile and Insulin Sensitivity

Blood total cholesterol and HDL levels increased whereas TG levels did not markedly change by HFD-feeding. No noticeable impact for cannabis extracts was found on blood lipid profile (Table 3). Comparably, as shown in Figure 2(e), impaired insulin sensitivity, evoked by HFD feeding, was correspondingly registered in all HFD groups, as judged by fasting glucose and glucose tolerance at the 19th week of the experiment.

3.3. Key Liver Metabolic Pathways/Player and Endocannabinoid Receptors Levels

While HFD decreased gluconeogenic enzymes (G6Pase and PEPCK) gene expression compared with ND, some of the cannabis extracts mitigated this reduction (Figures 4(a) and 4(b)). Interestingly, this was gene dependent, with CN2 increasing only G6Pase expression and CN6 increasing only PEPCK expression. The expression of the co-activator, PGC-1α, and the long fatty-acids transporter CD36 were significantly elevated in CN2- and CN6-treated groups (Figures 4(c) and4(d)). In contrast, no difference was observed in the PPARα gene expression between all groups (Figure 4(e)). The fundamental metabolic enzyme, AMPK, which constitutes a central metabolic hub, was downregulated by HFD as depicted by the ratio between phosphorylated AMPK at thr172 in the alpha subunit and total AMPK, a well-established hallmark for its activation. The addition of cannabis extracts did not alleviate this metabolic aberration (Figure 4(f)). Finally, cannabis extracts’ addition to a HFD did not profoundly affect the protein contents of CB1 and CB2 receptors of the endocannabinoid system in the liver, although a slight decrease wasobserved in CB1 (Figures 4(g) and 4(h)).

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Figure 4

Effect on gluconeogenesis, energy homeostasis, lipid metabolism, and endocannabinoid receptors in the liver. Male C57BL/6 J mice fed high-fat diet supplemented with CN1 or CN2 or CN6 extracts 5 mg/kg BW for 8 weeks. (a) G6pase mRNA levels. (b) PEPCK mRNA levels. (c) CD36 mRNA levels. (d) PGC-1α mRNA levels. (e) PPARα mRNA levels. (f) p-AMPK/AMPK protein levels. (g) CB2 receptor protein expression. (h) CB1 receptor protein expression. All values are expressed as mean ± SEM (n = 6–8). Columns marked with are different from ND (). Columns marked with different letters are significantly different ().

3.4. Microbiome Profile

Alterations in microbiota composition and diversity indexes following the treatments were evaluated at all taxonomical levels and are elaborated in Table 4. The observed species index, which reflects the amount of unique species in each group, was lessened in all HFD groups compared to ND group. CN2-added group exhibited an even lower score than that of CN6-added group. Bacterial richness, here represented by community diversity (Shannon index), declined in all HFD-fed groups compared to ND group with the exception of CN6-added group, in which the score was similar to that of the control.

At the phylum level, CN1 extract supplementation resulted in the lowest Bacteroidetes abundance and the highest Firmicutes abundance compared to all HFD groups. Consequently, Bacteroidetes/Firmicutes (B/F) ratio decreased by this treatment. At the class level, Clostridia and Flavobacteria abundance increased in the HFD + CN1 group compared to all HFD-fed mice. HFD + CN2 group presented lower Betaproteobacteria abundance compared to CN6 supplemented group. At the family level, Deferribacteraceae abundance increased in CN1 and CN2 groups in comparison to the control group. At the genus level, CN6 extract increased Bifidobacterium and Prevotella abundance compared to all HFD groups. Sutterella abundance increased in the CN6 group, but was reduced in the CN1 and CN2 groups, though not signifcantly. A marked increase in Lactobacillus abundance was observed in the group fed with CN6 extract compared to the HFD group. CN1 treatment elevated Roseburia abundance compared to all experimental groups. At the species level, Akkermansia muciniphila abundance was higher in CN6 fed group compared to the control.

4. Discussion

Though much attention has been pointed to synthetic agonists and antagonists of the endocannabinoid system, the role of naturally derived cannabinoids on the pathogenesis and progression of NAFLD has been less elucidated. The present study estimated the prominent metabolic and microbial modifications emanating from the supplementation of HFD with cannabis plant extract on the progression of NAFLD. Specifically, this work deemed the repercussions of different extraction from three cannabis strains, CN1, CN2, and CN6, which differ in their cannabinoid contents, under this pathologic condition. While CN1 extract is CBD-rich, that of CN2 is THC-rich and CN6 comprises equivocal quantities of these cannabinoids.

No profound alterations were disclosed in typical obesity features nor central metabolic hubs by cannabis extracts addition irrespective of the strain tested. Indeed, BW continued to escalate despite cannabinoids addition. Consist with that, liver and adipose weight tended to increase or significantly increased in all HFD-fed groups with only a minor effect elicited by CN6 extract, concerning adipose tissue weight. Likewise, cannabis extracts did not ameliorate blood lipid profile nor alleviate the abnormalities in glucose metabolism induced by HFD consumption. Concomitantly, gluconeogenesis enzymes expression levels and AMPK activation decreased or tended to decrease in all CN treated groups, albeit, levels did not differ considerably from those of the HFD group, excluding the PEPCK expression of the CN6 group. The only distinguished results are of CD36 and PGC-1alpha in which their mRNA levels in the liver exceeded those of the HFD group. Nevertheless, given the inconsistency with liver steatosis (e.g., the absence of changes in the amounts of fat stored in hepatocytes), it appears that the consequences impendent by these modifications are fairly restricted. Collectively, the findings obtained here argue for the lack of eminent metabolic outcomes provoked by the cannabis extracts tested in this study. However, arguably, this incapacity is plausibly due to the relatively short duration of the study and/or low extract quantity used. It is conjectured longer and/or higher extracts concentrations could have implemented more profound metabolic outcomes. It would be tempting to contend that the absence of modifications in CB receptors reinforces the surmise that treatments fall short to harness this pathway. However, the relationship between phytocannabinoids and NAFLD was shown to be independent of cannabinoid receptors [27], thus suggesting the fact that these receptors or fluctuation in their levels are not imperative for executing effects.

The gut microbiota encompasses a fascinatingly diverse population of microorganisms known to impose mark impacts on the host’s health. The interplay between diet pattern and composition, microbiome, and health are attractive subjects in the field of metabolism. A growing body of evidence advocates a profound effect of the gut microbiota in the pathogenesis of obesity and NAFLD [28]. The influential role of the endocannabinoid system on the microbiome has received some attention [29, 30]. Nevertheless, little is known about the repercussion of cannabis and phytocannabinoids specifically on the gut microbiota composition. Accordingly, this work also aimed to delineate whether such modifications exist. At the phylum level, Bacteroidetes and Firmicutes populations were shown to be the most dominant in the gut [31, 32], with ratios being changed according to the treatment applied. When comparing the microbiota of obese humans and mice to the microbiota of lean, healthy individuals, a lower ratio of B/F is observed [33]. Hence, B/F ratio is customarily utilized as a potential hallmark of the obesity phenotype. Surprisingly, an elevated B/F ratio was observed in all HFD-fed groups, excluding CN1 treated group. This finding is in accord with the recent intense criticism that is directed toward the reliability of this ratio. Indeed, human studies, including one conducted in NAFLD patients, have demonstrated an opposite effect, with HFD increasing rather than decreasing B/F ratio [34, 35]. Therefore, a lower B/F ratio in the CN1 group may argue for the favorable aspects of this extract. What is more, while the abundance of the Clostridia class of the Firmicutes phylum was substitutionally diminished by HFD feeding, this effect was mitigated by CN1 extract addition. Several works have proposed that elevated Clostridia richness may confer protective effects against different kinds of allergens as well as facilitate intestinal integrity by upregulating IL-22 expression. Although not manifested here, elevated Clostridia levels may hamper lipid absorption and thus protect from obesity [36–38], Particularly, Roseburia spp. was significantly elevated in CN1 treated mice. It was surmised that these short-chain fatty acids- (SCFA) producing bacteria possess anti-inflammatory activities, among other beneficial effects, and may be utilized as a health biomarker or as a probiotic [39]. A discrepancy was revealed in the Bacteriodetes phylum of the CN1 group. This phylum is composed of two classes, Bacteroidia and Flavobacteria. While the former’s levels flourished by the consumption of a HFD, the latter class remained largely identical. Nevertheless, Bacteroidia overgrowth was lessened by CN1 addition whereas Flavobacteria copiousness was enhanced above that of the other HFD-fed groups. In previous works, Flavobacterium presence was hindered by HFD while its enrichment was positively correlated to physical activity [40, 41]. Moreover, it may abolish the development of obesity and NAFLD [41]. Notwithstanding that those outcomes imbued by CN1 extract do appear desired, a noteworthy alteration is an increase in the presence of Deferribacteraceae, the Deferribacteres phylum and foremost of the Mucispirillum genus. Such growth was described in obese or diabetic animals and was implicated as one of the culprits in the pathogenesis of inflammation [42–44].

In comparison with other extracts, CN6 extract supplementation to an HFD triggered the most notable changes in miscellaneous bacterial groups. Several positive changes in gut microbiota have been revealed particularly in this group.Whereas quantities of Bifidobacterium and Lactobacillus of the Actinobacteria and Bacilli class, respectively, decreased by HFD, their levels remained intact in the CN6 group. Both genera are well acknowledged for their health-promoting effects, including metabolic disorders and/or antitumor role. Nevertheless, it is important to comprehend that inconsistency exists where diverse species and strains may elicit specific effects [45–51]. Likewise, the CN6-treated mice also attained a substantial increase in the Prevotella genus, which was linked to an improvement in glucose metabolism and fermentation potential of complex polysaccharides [52–54].

Comparably, although a propensity toward higher Akkermansia muciniphila was observed in all HFD-fed mice, it reached significance merely in the CN6 group. Recent evidence links Akkermansia muciniphila with improved metabolic and immunologic functions of the host, including those related to glycemic control and gut immunity [52, 55]. Given the beneficial effects attributed to Akkermansia muciniphila, CN6 extract addition to an HFD appears to imbue valuable outcomes, as it naturally accommodates the deployment of these beneficial bacteria. However, as it was disclosed in the CN1 group, not all changes were favorable. Indeed, the levels of Betaproteobacteria class of the Proteobacteia phylum significantly increased in the CN6 supplemented group and were attributed to the flourishment of Sutterella genus. This genus has been suggested to be held responsible for promoting gastrointestinal perturbations. Yet, recent data imply Sutterella does not provoke the initiation of inflammation but rather impinge gut immune homeostasis, though this capability is not conserved [48]. Although these findings reveal a significant impact of phytocannabinoids on the microbiota, a clear vision regarding their consequences on the host’s health is currently difficult to delineate.

5. Conclusions

In summary, in the present work, the addition of different cannabis plant strains extracts of distinct CBD/THC profiles to an HFD unsuccessfully mitigated metabolic perturbations evoked by this diet. Yet, results demonstrated these extracts did establish a considerable impact on the microbiota pattern, which was primarily positive. Further in-depth research is needed to elucidate how diverse cannabis plants affect NAFLD development as well as other metabolic disorders. For this purpose, one must consider the chemical composition of the cannabis strain, the extraction method, doses, given form, and treatment duration.

Abbreviations

ALT:	Alanine aminotransferase
ALP:	Alkaline phosphatase
AST:	Aspartate aminotransferase
BW:	Body weight
CBD:	Cannabidiol
HDL:	High-density lipoprotein
H&E:	Hematoxylin and eosin
HFD:	High-fat diet
NAFLD:	Nonalcoholic fatty liver disease
ND:	Normal diet
OGTT:	Oral glucose tolerance test
THC:	Delta-9-tetrahydrocannabinol
TG:	Triglycerides.

Data Availability

All raw sequence data were uploaded to NCBI Sequence Read Archive (SRA) under BioProject accession no. PRJNA809153.

Conflicts of Interest

The authors declare that they have no conflicts of interest regarding the publication of this paper.

Authors’ Contributions

GZ and SA‐C:writing—original draft preparation. GZ, SA‐C, and ZM: writing—review and editing, ZM: project administration. NB contributed the plant material. T‐AG, JG, AN, NB, and NS contributed to the methodology and data processing. All authors have read and agreed to the published version of the manuscript. GZ and SA‐C equally contributed to this work.

Acknowledgments

This work was funded by the chief scientist of the Israeli Ministry of Agriculture and Rural Development (Grant no. 15-05-0020) as well the Israeli Ministry of Science and Technology.

References

S. L. Friedman, B. A. Neuschwander-Tetri, M. Rinella, and A. J. Sanyal, “Mechanisms of NAFLD development and therapeutic strategies,” Nature Medicine, vol. 24, no. 7, pp. 908–922, 2018.
View at: Publisher Site | Google Scholar
T. G. Cotter and M. Rinella, “Nonalcoholic fatty liver disease 2020: the state of the disease,” Gastroenterology, vol. 158, no. 7, pp. 1851–1864, 2020.
View at: Publisher Site | Google Scholar
N. Stefan, H.-U. Häring, and K. Cusi, “Non-alcoholic fatty liver disease: causes, diagnosis, cardiometabolic consequences, and treatment strategies,” Lancet Diabetes & Endocrinology, vol. 7, no. 4, pp. 313–324, 2019.
View at: Publisher Site | Google Scholar
C. D. Byrne and G. Targher, “NAFLD: a multisystem disease,” Journal of Hepatology, vol. 62, no. 1, pp. S47–S64, 2015.
View at: Publisher Site | Google Scholar
S. A. Polyzos, J. Kountouras, and C. S. Mantzoros, “Obesity and nonalcoholic fatty liver disease: from pathophysiology to therapeutics,” Metabolism, vol. 92, pp. 82–97, 2019.
View at: Publisher Site | Google Scholar
S. Ben-Shabat, E. Fride, T. Sheskin et al., “An entourage effect: inactive endogenous fatty acid glycerol esters enhance 2-arachidonoyl-glycerol cannabinoid activity,” European Journal of Pharmacology, vol. 353, no. 1, pp. 23–31, 1998.
View at: Publisher Site | Google Scholar
E. B. Russo and J. M. McPartland, “Cannabis is more than simply Δ9-tetrahydrocannabinol,” Psychopharmacology, vol. 165, no. 4, pp. 431-432, 2003.
View at: Publisher Site | Google Scholar
A. Ligresti, A. S. Moriello, K. Starowicz et al., “Antitumor activity of plant cannabinoids with emphasis on the effect of cannabidiol on human breast carcinoma,” Journal of Pharmacology and Experimental Therapeutics, vol. 318, no. 3, pp. 1375–1387, 2006.
View at: Publisher Site | Google Scholar
J. D. Wilkinson and E. M. Williamson, “Cannabinoids inhibit human keratinocyte proliferation through a non-CB1/CB2 mechanism and have a potential therapeutic value in the treatment of psoriasis,” Journal of Dermatological Science, vol. 45, no. 2, pp. 87–92, 2007.
View at: Publisher Site | Google Scholar
A. W. Zuardi, “Cannabidiol: from an inactive cannabinoid to a drug with wide spectrum of action,” Revista Brasileira de Psiquiatria, vol. 30, no. 3, pp. 271–280, 2008.
View at: Publisher Site | Google Scholar
Y. Le Strat and B. Le Foll, “Obesity and cannabis use: results from 2 representative national surveys,” American Journal of Epidemiology, vol. 174, no. 8, pp. 929–933, 2011.
View at: Publisher Site | Google Scholar
T. B. Rajavashisth, M. Shaheen, K. C. Norris et al., “Decreased prevalence of diabetes in marijuana users: cross-sectional data from the national health and nutrition examination survey (NHANES) III,” BMJ Open, vol. 2, Article ID e000494, 2012.
View at: Publisher Site | Google Scholar
O. A. Anthony, “Brief report: cannabis smoking and diabetes mellitus results from meta-analysis with eight independent replication samples,” Epidemiology, vol. 26, pp. 597–600, 2015.
View at: Publisher Site | Google Scholar
A. C. Adejumo, S. Alliu, T. O. Ajayi et al., “Cannabis use is associated with reduced prevalence of non-alcoholic fatty liver disease: a cross-sectional study,” PLoS One, vol. 12, no. 4, Article ID e0176416, 2017.
View at: Publisher Site | Google Scholar
D. Kim, W. Kim, M.-S. Kwak, G. E. Chung, J. Y. Yim, and A. Ahmed, “Inverse association of marijuana use with nonalcoholic fatty liver disease among adults in the United States,” PLoS One, vol. 12, no. 10, Article ID e0186702, 2017.
View at: Publisher Site | Google Scholar
A. Wieland, D. N. Frank, B. Harnke, and K. Bambha, “Systematic review: microbial dysbiosis and nonalcoholic fatty liver disease,” Alimentary Pharmacology & Therapeutics, vol. 42, no. 9, pp. 1051–1063, 2015.
View at: Publisher Site | Google Scholar
A. J. Wigg, I. C. Roberts-Thomson, R. B. Dymock, P. J. McCarthy, R. H. Grose, and A. G. Cummins, “The role of small intestinal bacterial overgrowth, intestinal permeability, endotoxaemia, and tumour necrosis factor alpha in the pathogenesis of non-alcoholic steatohepatitis,” Gut, vol. 48, no. 2, pp. 206–211, 2001.
View at: Publisher Site | Google Scholar
L. Zhu, S. S. Baker, C. Gill et al., “Characterization of gut microbiomes in nonalcoholic steatohepatitis (NASH) patients: a connection between endogenous alcohol and NASH,” Hepatology, vol. 57, no. 2, pp. 601–609, 2013.
View at: Publisher Site | Google Scholar
M. Mouzaki, E. M. Comelli, B. M. Arendt et al., “Intestinal microbiota in patients with nonalcoholic fatty liver disease,” Hepatology, vol. 58, no. 1, pp. 120–127, 2013.
View at: Publisher Site | Google Scholar
T. Assa-Glazer, J. Gorelick, N. Sela, A. Nyska, N. Bernstein, and Z. Madar, “Cannabis extracts affected metabolic syndrome parameters in mice fed high-fat/cholesterol diet,” Cannabis and Cannabinoid Research, vol. 5, no. 3, pp. 202–214, 2020.
View at: Publisher Site | Google Scholar
J. Folch, M. Lees, and G. H. S. Stanley, “A simple method for the isolation and purification of total lipides from animal tissues,” Journal of Biological Chemistry, vol. 226, no. 1, pp. 497–509, 1957.
View at: Publisher Site | Google Scholar
G. Zandani, S. Anavi-Cohen, N. Sela, A. Nyska, and Z. Madar, “Broccoli consumption attenuates inflammation and modulates gut microbiome composition and gut integrity-related factors in mice fed with a high-fat high-cholesterol diet,” Food & Nutrition Research, vol. 65, 2021.
View at: Publisher Site | Google Scholar
G. Zandani, S. Anavi-cohen, N. Tsybina-shimshilashvili, and N. Sela, “Broccoli florets supplementation improves insulin sensitivity and alters gut microbiome population—a steatosis mice model induced by high-fat diet,” Frontiers in Nutrition, vol. 8, pp. 1–14, 2021.
View at: Publisher Site | Google Scholar
B. Rivero-gutiérrez, A. Anzola, O. Martínez-augustin, and F. S. D. Medina, “Stain-free detection as loading control alternative to ponceau and housekeeping protein immunodetection in Western blotting,” Analytical Biochemistry, vol. 467, pp. 1–3, 2014.
View at: Publisher Site | Google Scholar
N. T. Smith, A. Soriano-arroquia, K. Goljanek-whysall, M. J. Jackson, and B. McDonagh, “Redox responses are preserved across muscle fibres with differential susceptibility to aging,” Journal of Proteomics, vol. 177, pp. 112–123, 2018.
View at: Publisher Site | Google Scholar
G. Zandani, N. Kaftori-Sandler, N. Sela, A. Nyska, and Z. Madar, “Dietary broccoli improves markers associated with glucose and lipid metabolism through modulation of gut microbiota in mice,” Nutrition, vol. 90, Article ID 111240, 2021.
View at: Publisher Site | Google Scholar
C. Silvestri, D. Paris, A. Martella et al., “Two non-psychoactive cannabinoids reduce intracellular lipid levels and inhibit hepatosteatosis,” Journal of Hepatology, vol. 62, pp. 1382–1390, 2015.
View at: Publisher Site | Google Scholar
M. V. Machado and H. Cortez-Pinto, “Diet, microbiota, obesity, and NAFLD: a dangerous quartet,” International Journal of Molecular Sciences, vol. 17, pp. 1–20, 2016.
View at: Publisher Site | Google Scholar
G. G. Muccioli, D. Naslain, F. Bäckhed et al., “The endocannabinoid system links gut microbiota to adipogenesis,” Molecular Systems Biology, vol. 6, p. 392, 2010.
View at: Publisher Site | Google Scholar
T. Scherer and C. Buettner, “The dysregulation of the endocannabinoid system in diabesity—a tricky problem,” Journal of Molecular Medicine, vol. 87, pp. 663–668, 2009.
View at: Publisher Site | Google Scholar
R. E. Ley, F. Bäckhed, P. Turnbaugh, C. A. Lozupone, R. D. Knight, and J. I. Gordon, “Obesity alters gut microbial ecology,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, pp. 11070–11075, 2005.
View at: Publisher Site | Google Scholar
P. I. Costea, F. Hildebrand, M. Arumugam et al., “Enterotypes in the landscape of gut microbial community composition,” Nature, vol. 3, pp. 1–12, 2013.
View at: Publisher Site | Google Scholar
R. Mathur and G. M. Barlow, “Obesity and the microbiome,” Expert Review of Gastroenterology & Hepatology, vol. 9, pp. 1087–1099, 2015.
View at: Publisher Site | Google Scholar
S. Quesada-Vázquez, G. Aragonès, J. M. Del Bas, and X. Escoté, “Diet, gut microbiota and non-alcoholic fatty liver disease: three parts of the same axis,” Cells, vol. 9, pp. 1–17, 2020.
View at: Publisher Site | Google Scholar
F. Magne, M. Gotteland, L. Gauthier et al., “The firmicutes/bacteroidetes ratio: a relevant marker of gut dysbiosis in obese patients?” Nutrients, vol. 12, 2020.
View at: Publisher Site | Google Scholar
J. Zou, B. Chassaing, V. Singh et al., “Fiber-mediated nourishment of gut microbiota protects against diet-induced obesity by restoring IL-22-mediated colonic health,” Cell Host & Microbe, vol. 23, pp. 41–53, 2018.
View at: Publisher Site | Google Scholar
A. T. Stefka, T. Feehley, P. Tripathi et al., “Commensal bacteria protect against food allergen sensitization,” Proceedings of the National Academy of Sciences of the United States of America, vol. 111, pp. 13145–13150, 2014.
View at: Publisher Site | Google Scholar
C. Petersen, R. Bell, K. A. Klag et al., “T cell–mediated regulation of the microbiota protects against obesity,” Science, vol. 80, p. 365, 2019.
View at: Publisher Site | Google Scholar
K. Nie, K. Ma, W. Luo et al., “Roseburia intestinalis: a beneficial gut organism from the discoveries in genus and species,” Frontiers in Cellular and Infection Microbiology, vol. 11, pp. 1–15, 2021.
View at: Publisher Site | Google Scholar
V. Palmas, S. Pisanu, V. Madau et al., “Gut microbiota markers associated with obesity and overweight in Italian adults,” Scientific Reports, vol. 11, pp. 1–14, 2021.
View at: Publisher Site | Google Scholar
S. Carbajo-Pescador, D. Porras, M. V. Garcia-Mediavilla et al., “Beneficial effects of exercise on gut microbiota functionality and barrier integrity, and gut-liver crosstalk in an in vivo model of early obesity and non-alcoholic fatty liver disease,” DMM Disease Models & Mechanisms, vol. 12, 2019.
View at: Publisher Site | Google Scholar
C. Hoffmann, D. A. Hill, N. Minkah et al., “Community-wide response of the gut microbiota to enteropathogenic citrobacter rodentium infection revealed by deep sequencing,” Infection and Immunity, vol. 77, pp. 4668–4678, 2009.
View at: Publisher Site | Google Scholar
D. Ciobârcă, A. F. Cătoi, C. Copăescu, D. Miere, and G. Crisan, “Bariatric surgery in obesity: effects on gut microbiota and micronutrient status,” Nutrients, vol. 12, 2020.
View at: Publisher Site | Google Scholar
S. Liu, P. Qin, and J. Wang, “High-fat diet alters the intestinal microbiota in streptozotocin-induced type 2 diabetic mice,” Microorganisms, vol. 7, pp. 1–12, 2019.
View at: Publisher Site | Google Scholar
C. Zhang, Y. Zhang, H. Li, and X. Liu, “The potential of proteins, hydrolysates and peptides as growth factors for: Lactobacillus and Bifidobacterium: current research and future perspectives,” Food & Function, vol. 11, pp. 1946–1957, 2020.
View at: Publisher Site | Google Scholar
F. De Filippis, E. Pasolli, and D. Ercolini, “The food-gut axis: lactic acid bacteria and their link to food, the gut microbiome and human health,” FEMS Microbiology Reviews, vol. 44, pp. 454–489, 2020.
View at: Publisher Site | Google Scholar
D. D. Heeney, M. G. Gareau, and M. L. Marco, “Intestinal Lactobacillus in health and disease, a driver or just along for the ride?” Current Opinion in Biotechnology, vol. 49, pp. 140–147, 2018.
View at: Publisher Site | Google Scholar
C. Hidalgo-Cantabrana, S. Delgado, L. Ruiz, P. Ruas-Madiedo, B. Sánchez, and A. Margolles, “Bifidobacteria and their health-promoting effects,” Microbiology Spectrum, vol. 5, 2017.
View at: Publisher Site | Google Scholar
Z. Heydari, M. Rahaie, and A. M. Alizadeh, “Different anti-inflammatory effects of Lactobacillus acidophilus and Bifidobactrum bifidioum in hepatocellular carcinoma cancer mouse through impact on microRNAs and their target genes,” Journal of Nutrition and Intermediary Metabolism, vol. 16, 2019.
View at: Publisher Site | Google Scholar
R. Ayyanna, D. Ankaiah, and V. Arul, “Anti-inflammatory and antioxidant properties of probiotic bacterium Lactobacillus mucosae AN1 and Lactobacillus fermentum SNR1 in wistar albino rats,” Frontiers in Microbiology, vol. 9, pp. 1–13, 2018.
View at: Publisher Site | Google Scholar
M. Gurung, Z. Li, H. You et al., “Role of gut microbiota in type 2 diabetes pathophysiology,” EBioMedicine, vol. 51, Article ID 102590, 2020.
View at: Publisher Site | Google Scholar
R. L. Greer, X. Dong, A. C. Moraes et al., “Akkermansia muciniphila mediates negative effects of IFNγ on glucose metabolism,” Nature Communications, vol. 7, Article ID 13329, 2016.
View at: Publisher Site | Google Scholar
P. Kovatcheva-Datchary, A. Nilsson, R. Akrami et al., “Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of prevotella,” Cell Metabolism, vol. 22, pp. 971–982, 2015.
View at: Publisher Site | Google Scholar
C. Haro, S. García-Carpintero, O. A. Rangel-Zúñiga et al., “Consumption of two healthy dietary patterns restored microbiota dysbiosis in obese patients with metabolic dysfunction,” Molecular Nutrition & Food Research, vol. 61, pp. 1–9, 2017.
View at: Publisher Site | Google Scholar
H. Plovier, A. Everard, C. Druart et al., “A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice,” Nature Medicine, vol. 23, pp. 107–113, 2017.
View at: Publisher Site | Google Scholar

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Copyright © 2022 Gil Zandani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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