https://orcid.org/0000-0002-5188-0453
ABSTRACT
Synthetic cannabinoids are a group of novel psychoactive substances with similar properties to Δ9-THC. Among the vast number of synthetic cannabinoids, designed to be tested in clinical trials, JWH-018 was the first novel psychoactive substance found in the recreational drug marketplace. The consumption of JWH-018 shows typical effects of CB1 agonists including sedation, cognitive dysfunction, tachycardia, postural hypotension, dry mouth, ataxia and psychotropic effects, but appeared to be more potent than Δ9-THC. However, studies on human cells have shown that JWH-018 toxicity depends on the cellular line used. Despite these studies, the underlying molecular mechanisms to JWH-018 action has not been clarified yet. To understand the impact of JWH-018 at molecular and cellular level, we used Saccharomyces cerevisiae as a model. The results showed an increase in yeast growth rate in the presence of this synthetic cannabinoid due to an enhancement in the glycolytic flux at expense of a decrease in pentose phosphate pathway, judging by 2D-Gel proteomic analysis, qRT-PCR experiments and ATP measurements. Overall, our results provide insights into molecular mechanisms of JWH-018 action, also indicating that Saccharomyces cerevisiae is a good model to study synthetic cannabinoids.
INTRODUCTION
The medical use of cannabis predates history, finding its first record in ancient China 4700 years ago (Zuardi 2006). The main psychoactive component of cannabis is Δ9-tetrahydrocannabinol (Δ9-THC) which was first isolated by Gaoni and Mechoulam in 1964 (Gaoni and Mechoulam 1964). During the 1980s, it was found that Δ9-THC action mechanism is receptor-mediated by the cannabinoid receptors 1 (CB1) and 2 (CB2) (Devane et al. 1988). This finding was supported by the observation of adenylate cyclase inhibition by natural and synthetic cannabinoids (SC) (Askew and Ho 1974; Lee and Phillis 1977; Reich et al. 1982; Li and Ng 1984; Heepe and Starke 1985; Howlett, Qualy and Khachatrian 1986). The United Nations acts of 1961 and 1971 imposed the control of narcotics and psychotropic substances. Due to these restrictions in the early 1980s, Pfizer© and Lilly© developed SC mimicking the effects of Δ9-THC (McCarthy and Borison 1981). Subsequently, many other cannabinoids-like molecules have been synthetised aiming therapeutic uses (Howlett et al. 2002). However, just Nabilone was approved by FDA (Urits et al. 2019).
Currently, there are several studies suggesting the protective effects of natural and SC (Shohami, Novikov and Mechoulam 1993; Bar-Joseph et al. 1994; Vered et al. 1994; Shohami et al. 1997; Shen and Thayer 1998; Nagayama et al. 1999; Fernández-López et al. 2006; Koch et al. 2011; Pinar-Sueiro et al. 2013). Actually, WIN 55,212–2 exerts a neuroprotective effect in hippocampal neurons exposed to low extracellular magnesium concentrations, which induces cell death (Shen and Thayer 1998), and in cortical neurons under hypoxia and glucose deprivation (Nagayama et al. 1999). Moreover, HU-211 reduces neuronal damage in animal models of global ischaemia (Bar-Joseph et al. 1994; Vered et al. 1994; Fernández-López et al. 2006; Pinar-Sueiro et al. 2013). The same HU-211 has shown to improve motor and memory functions after a traumatic brain injury by reducing the oedema and blood–brain barrier breakdown (Shohami, Novikov and Mechoulam 1993; Shohami et al. 1997). Overall, cannabinoids seem to play a protective role in neurodegeneration improving muscle spasticity (Petro and Ellenberger 1981) and tremor (Clifford 1983), attenuating the symptoms of Parkinson's disease and multiple sclerosis. These effects seem to be related with cannabinoids’ ability to reduce inflammatory mediators (Gruol et al. 1998; Puffenbarger, Boothe and Cabral 2000; Smith, Terminelli and Denhardt 2000; Koch et al. 2011). Additionally, several studies have been suggesting that cannabinoids have anti-cancer properties as exemplified by the suppressive effect of SC JWH-133 and WIN-55,212–2 in breast tumour (Qamri et al. 2009).
Despite the potential therapeutic of the previously described cannabinoids, there are many clinical reports pointing negative impacts of these molecules (Robinson et al. 2007; Crane 2013; Meijer, Russo and Adhvaryu 2014; Irie et al. 2015; Nurmedov et al. 2015; Rojek et al. 2017; Fantegrossi, Wilson and Berquist 2018). A magnetic resonance imaging study showed a grey matter density reduction in SC’ abusers (Nurmedov et al. 2015). Another study has shown impairment of memory and hippocampal activity induced by HU-210 due to abnormalities in hippocampal cell firing (Robinson et al. 2007). At molecular level, MAM-2201 has been proven to suppress glutamate release in humans CB1-expressing synapses, which, according to the authors, may contribute to some of the symptoms of SC intoxication including impairments in cerebellum-dependent motor coordination and motor learning (Irie et al. 2015). In fact, consumers of SC have been given entry in hospital's emergency room with psychosis (Fantegrossi, Wilson and Berquist 2018), seizures (Malyshevskaya et al. 2017) and auto-inflicting injuries (Meijer, Russo and Adhvaryu 2014). Suicide (Crane 2013) and fatal cases (Rojek et al. 2017) were also reported. Despite the several contraindications described in the literature, a risk assessment study with JWH-018 was conducted in humans for the first time. This pilot study was sufficient to exclude this molecule for further consideration of therapeutic use (Theunissen et al. 2018). Also, other researches on human cells have shown that JWH-018 toxicity depends on the cellular line used, presenting no toxicity to hepatomas (Hep-G2) and neuroblastomas (SY-SH5) and moderate toxicity to human breast cells (MCF-7) and buccal cells (TR146) (Koller et al. 2013; Couceiro et al. 2016).
Taken together, the previous studies and clinical reports suggest the need of additional research to get the full picture of cannabinoids’ effects in human health and disease. To further investigate the molecular mechanism of SC and understand the metabolic implications of these substances, a simplified model is required.
Saccharomyces cerevisiae shares highly conserved molecular and cellular mechanisms with human cells and simplifies the approach to understand cells fundamental biochemistry in a variety of different conditions. Yeast has been proven an invaluable model to study the fundamental molecular mechanisms involved in several human diseases (Tenreiro and Outeiro 2010). Moreover, it has also been used to study the toxicity of synthetic cathinones, pharmaceutical drugs and environmental pollutants impact (Hu et al. 2003; Schmitt et al. 2004; Tenreiro and Outeiro 2010; dos Santos et al. 2012; Tenreiro et al. 2013; Ferreira et al. 2019). Actually, the rapid growth associated to its high degree of conservation with human cellular pathways makes yeast a very interesting model to study SC. Therefore, the present study used S. cerevisiae to understand the impact of JWH-018 at cellular and molecular level. JWH-018, a SC synthetised by John W Huffman in 1994 as an antinociceptive, was the first legal alternative to cannabis used for recreational purposes and since then classified as a novel psychoactive substance (Lainton et al. 1995; Wiley et al. 1998). The present work used S. cerevisiae as a model to understand the cellular and molecular mechanisms of JWH-018 aiming to support target studies in more complex systems.
MATERIAL AND METHODS
Reagents
The YNB–Difco Yeast Nitrogen Base without Amino Acids, agar (Bacto) and peptone (Bacto) were obtained from Quilaban. The amino acids were acquired as a mixture from MPBiomedicals. Glucose, tris-hydrochloride (Tris-HCl), sodium chloride (NaCl) and sodium dodecil sulfate (SDS) were acquired from Panreac. Bradford reagent (Bio-Rad Protein Assay Dye Reagent Concentrate), bovine serum albumin (BSA), ReadyPrep 2-D kit, urea, IPG strips pH 3–10 NL, iodoacetamide, protein marker (Precision Plus ProteinTM Standards Dual Colour) and FlamingoTM Fluorescent Gel Stain were acquired from Bio-Rad. Thiourea was obtained from Millipore. CHAPS from BioChemica, ampholytes (pH 3–10) from GE Healthcare, dithiothreitol (DTT) from Amresco, and glycerol obtained from Scharlau.
Tested compounds
The synthetic cannabinoid JWH-018 [(1-pentyl-1H-indol-3-yl)-1-naphthalenyl-methanone] was acquired from Lipomed AG Switzerland in the form of powder with a purity greater than 98.5%. Stock solutions of this compound were prepared in 100% DMSO in different concentrations: 0.25 mM, 2.50 mM, 12.50 mM, 18.75 mM and 25.00 mM.
Cell culture
The study was performed using S. cerevisiae cells (BY4741 MATa; his3Δ1; leu2Δ0; met15Δ0; ura3Δ0 obtained from Euroscarf collection (Frankfurt, Germany)). Yeast cells were pregrown in YNB (Yeast Nitrogen Base without Amino Acids 0.67%, Glucose 2%, Required amino acids) liquid media at 30°C, with orbital agitation (200 rpm) for 6 hours. After 6 hours, the optical density at 600 nm (OD600nm) was measured and cultures were diluted to a standardised OD600 nm = 2 × 10−3 (∼1.67 × 105 cells/mL) in the same medium. After 19 hours, new cultures were prepared to have an initial standardised OD600nm = 0.03 (2.5 × 104 cells/mL) in the same medium, under the same conditions for 24 hours.
Toxicity experiments
The cytotoxic properties of JWH-018 were investigated through the evaluation of the cell growth kinetics, cell viability and spot assay in the presence of 1 μM, 10 μM, 50 μM, 75 μM and 100 μM of the SC. Growth analysis was monitored for 24 hours by a thermostatic microplate reader PowerWave XS (Biotek) at 30°C at 600 nm with readings every hour. The data were collected by Gene5 data analysis software. In order to count CFUs, several points were collected and diluted during yeast growth and were incubated for 2 days at 30°C on YPD agar plates (Yeast extract 1%, Peptone 2%, Glucose 2%, agar 2%).
Models for fitting toxicity experiments
CFU data were analysed using the GraphPad prism software® v.4 (La Jolla, CA, USA) with the exponential growth equation, whose doubling time was calculated by this software.
Sampling and protein extraction for 2-D Electrophoresis
Three independent growth experiments were carried out to obtain cell replicates with 100µM of JWH-018 and without JWH-018 (control). Both samples were collected in the mid-exponential growth-phase. Cultures samples were centrifuged (12 100 g for 5 min at 4°C) and washed with cold distilled water. The cell pellets were immediately stored at –80°C until use. Cell pellets from the same culture condition were thawed on ice and pooled together by resuspension in lysis buffer (50 mM Tris-HCl, 150 mM NaCl pH 7.3) supplemented with protease inhibitor cocktail (Sigma). Briefly, cell lysis was carried out by consecutive steps of vortexing and ice cooling with an equal volume of glass beads (f = 425–600 mm). The mixture was centrifuged at 12 100 g at 4°C for 5 min to separate cell debris and glass beads from the supernatant, after which the cell pellets were disrupted again as described above. The resulting supernatant was mixed with the one obtained before, and the mixture was clarified by centrifugation at 12 100 g at 4°C for 5 min. Protein concentration of the resulting cell extracts was quantified using the Bio-Rad Protein Assay Dye Reagent Concentrate. Total protein (75 µg) was precipitated using the ReadyPrep 2-D (Bio-Rad), and the protein pellets were dissolved in 170µL of rehydration solution (7 M Urea; 2 M Thiourea; 2% w/v CHAPS; 0.5% ampholytes pH 3–10; 0.28% DTT; 0.002% bromophenol blue). The mixture was homogenised. All reagents used in this study were of analytical grade.
2-D Electrophoresis
Isoelectric focusing (IEF) was performed using the Protein i12 IEF cell (Bio rad), using IPG strips (11 cm Ready Strip, pH 3–10 NL; BioRad) which were rehydrated of 170µL of protein samples in rehydration solution for 17 hours before being transferred to the isoelectric system. IEF was carried out at 150 V for 10 min; 250 V for 20 min; 800 V for 2 hours; 1400 V for 1.75 hours; 3000 V for 2 hours; 6500 V for 1.86 hours and 8000 V for 2 hours. After the first dimension, the strips were equilibrated in two sequential 15 min steps in the adequate buffer (6 M Urea; 50 mM Tris-HCl, pH 8.8; 30% (v/v) glycerol; 2% (m/v) SDS; bromophenol blue) containing 65 mM DTT (step 1), or 135 mM iodoacetamide (step 2). The second dimension was performed using 11 cm SDS polyacrylamide (CriterionTM TGXTM precast gels 8–16%, Bio-Rad). In the second dimension were applied molecular weight standards (Precision Plus ProteinTM Standards Dual Colour, Bio-Rad). Proteins were separated at 120 V. After the second-dimension run, protein spots were stained with fluorescent dye (FlamingoTM Fluorescent Gel Stain, Bio-Rad) according to the manufacturer's instructions. After destaining, the gels were washed two times and scanned in a laser imager with emission at 473 nm (FLA-5000 Imaging System).
Analysis of protein expression results from 2-DE
In total, three 2-DE gels for each experimental condition, corresponding to triplicates of biological samples per condition, were analysed. The images were analysed using Progenesis Same Spots software (Nonlinear Dynamics, Durham, NC, USA). Individual spot volumes were normalised against the total spot volumes for each gel. For each growth condition, the average value was compared by their normalized volume using one-way analysis of variance between-group testing. Statistically significant spots (P < 0.05) were selected for putative identification. This was performed by comparison with gels 2D in SWISS-2DPAGE (https://world-2dpage.expasy.org).
Interaction networks between proteins were obtained from STRING (http://string.embl.de/) (von Mering et al. 2007), a web-based resource containing a database that encompasses known and predicted protein–protein interactions for 373 organisms, including S. cerevisiae and humans. Human proteins with sequence homologues that were found to be involved in the response to JWH-018 in S. cerevisiae were identified by BLASTP sequence homologuey searches at NCBI (http://www.ncbi.nlm.nih.gov/). Pathway queries for yeast and human proteins were performed at SGD (www.yeastgenome.org) and NCBI.
Quantitative Real-time -PCR (qRT-PCR)
RNA was extracted according to the manufacturer's instructions kit (E.Z.N.A. Yeast RNA Kit, OMEGA). The cDNA was synthesised from 500 ng of DNAse-treated total RNA using the SuperScript II Reverse Transcriptase (Invitrogen) following the supplier's recommendations. qRT-PCR was performed using LightCycler®480 SYBR Green I Master. The reaction mixture without template cDNA was run as a control. The expression was normalised to PGK1. PCR conditions used in the reaction were as follows: initial denaturation at 95°C for 10 s, followed by 45 cycles with the following cycling parameters – denaturation at 95°C for 10 s, annealing at 60°C for 10 s and elongation at 72°C for 30 s. Fold change was calculated using double delta Ct method. The data were expressed as mean ± standard error. Primers’ sequences are indicated in Suplementary data.
ATP quantification
Three independent growth experiments were carried out to obtain cell replicates with 100µM of JWH-018 and without JWH-018 (control). The quantification of ATP was carried out using ATP Colorimetric/Fluorimetric assay kit from Sigma-Aldrich. Both samples were collected in the mid-exponential growth-phase. Three hundred microliters of cultures samples were centrifuged (12 100 g for 5 min at 4°C) and washed with cold distilled water. The cell pellets were immediately frozen in liquid nitrogen. After that, the cells were resuspended in ATP assay buffer and cell lysis was carried out by consecutive steps of vortexing and ice cooling with an equal volume of glass beads (f = 425–600 mm). The mixture was centrifuged at 12 100 g at 4°C for 1 min to separate cell debris and glass beads from the supernatant, after that, the ATP quantification was performed according to conditions described under colorimetric detection. In order to normalise the ATP values, protein quantification was performed using Bio-Rad protein assay dye reagent concentrate.
Statistical analysis
Linear mixed effects models were used to analyse the relationship between SC's concentration and each of the parameters resulting from the non-linear fit to the logistic equation, where repetitions nested within replicates were considered as random effects. Models were fitted through restricted maximum likelihood (RML) with the function lmer, provided by the package lmerTest v.3.0–1 for R v.3.5.1. Model estimates were summarised in tables using the sjt.lmer function provided by the sjPlot v.2.4.0 package for R v.3.5.1. P-values were computed through an analysis of variance of type III with the Kenward–Roger approximation for the degrees of freedom. Plots were made using ggplot2 v.3.0.0 package for R.
Student's t-test was used to analyse the differences in CFU viability assay, qRT-PCR results and ATP quantification.
RESULTS
Cytotoxicity assays
To investigate the effect of JWH-018, S. cerevisiae wild-type was exposed to growing concentrations of this SC. Cellular grow with 0, 1, 10, 50, 75 and 100 µM of JWH-018 was monitored by optical density at 600 nm during a time frame of 24 hours (Fig. 1A). Yeast growing rates in the presence of JWH-018 do not seem to be considerably different when compared to the control, suggesting the absence of an effect of this substance within the range of concentrations used. To further understand putative differences between yeast growth rates in the presence and absence of JWH-018, the curves were fitted to a logistic equation, which originates final biomass, growth rate (Fig. 1B) and the doubling time (Fig. 1C). Linear mixed effects models were used to statistically evaluate putative differences on growth rate (r), doubling time (td) and final biomass (K), with SC concentration. These models showed to be significantly different from the null model for concentrations equal or higher than 50 µM (P < 0.05). Actually, growth rate shows an estimated average value for 0 µM of JWH-018 of 0.5398 ± 0.0077. This value significantly increases by 0.0132 ± 0.0059, 0.0240 ± 0.0059 and 0.0265 ± 0.0058 for the concentrations 50 µM, 75 µM and 100 µM (t(92.37) = 2.23, P = 0.026; t(92.37) = 4.04, P < 0.001; t(92.20) = 4.46, P < 0.001). Consequently, it originates doubling time values of 1.2839 ± 0.0175 hours for concentration 0 µM, which decreases by 0.0287 ± 0.0134, 0.0521 ± 0.0134 and 0.0570 ± 0.0134 hours, for the concentrations of 50 µM, 75 µM and 100 µM, respectively. In opposite, the model developed to evaluate the effect of concentration on final biomass did not show significant differences between the absence and the presence of 100 µM of JWH-018. The non-linear analysis of the data shows differences in growth rate which were hidden in the initial picture.
Saccharomyces cerevisiae BY4741 growth curves (A) with and without JWH-018 were fitted to the standard form of the logistic equation (Equation (1)), where r is the growth rate (B). The doubling time values (td) were computed from the growth rate using the formulae Ln2/r (C). These models showed to be significantly different from the null model for concentrations equal or higher than 50 µM of JWH-018 (* P < 0.05 or *** P < 0.001).
To confirm the decrease on yeast duplication time in the presence of JWH-018 registered on the growth curves, CFU assays were performed (Fig. 2). The non-linear adjust shows that duplication time obtained was lower for cells in the presence of 50 µM of JWH-018 (Fig. 2), corroborating the results obtained in the analysis of the growth curves previously described (Fig. 1). However, after statistical analysis, the decrease in doubling time in the presence of 50µM of the SC was not significantly different from the culture without the compound.
Exponential phase of Saccharomyces cerevisiae BY4741, in the absence (○) and presence of 50 µM of JWH-018 (□), determined by viable cell concentration, assessed as the number of CFU/mL of cell culture and its doubling time represented by the bar chart (the results are representative of at least three independent growth experiments).
S.cerevisiae proteome analysis
To grasp the molecular mechanisms underlying the increase in the growth rate of S. cerevisiae in the presence of JWH-018, a proteomic study was conducted. The concentration of JWH-018 chosen for this assay was 100 µM as it combines a higher effect on growth rate with the absence of toxicity. The proteome of S. cerevisiae in the absence and in the presence of 100 μM of JWH-018 was analysed in 2D electrophoresis gel system. Comparative and quantitative analyses of the 2D protein profiles were carried out using Progenesis SameSpots (Totallab, Reino Unido). In this study 2D gels triplicates for each of the S. cerevisiae cultures were analysed. The range of protein separation obtained by a 2DE was 10–250 kDa and 3–10 pH and 2181 protein spots were detected (Fig. 3). After a statistical analysis comparing yeast proteome in the absence and in the presence of JWH-018, eight proteins were found with statistically significant differences in their levels (P < 0.05). Fig. 3 shows the proteomic pattern of S. cerevisiae in the presence of JWH-018. One to eight marked spots show differences between the control and proteome with the SC. Among them, seven spots show a decrease in protein levels, while one reveals an increase in protein level. Half of the spots are within basic or acidic regions of the isoelectric focusing gel strip. Besides, the remaining spots are within the most crowded region of the gel. Thus, the spots are in the working limits of the 2D-gel analysis (Rabilloud and Lelong 2011; Yaoita et al. 2014). To understand which proteins were changing in the presence of JWH-018, a table of soluble proteins matching the isoelectric point and molecular weight was generated for the eight spots under analysis using a list of all S. cerevisiae identified protein from UniProt database (https://www.uniprot.org) (UniProt Consortium 2019). The rationale for protein selection was based on the hypothesis that growth rate increment was due to expression changes in central metabolism enzymes and in protein production machinery. In the end, 15 proteins were selected and their mRNA expression was quantified by qRT-PCR, namely ADH4, ADH5, EMI2, FBA1, GPM1, GPP1, NPY1, PGI1, PRM15, REE1, SER33, SOL3, SOL4, TAL1, ZWF1. PGK1 was used as control.
Proteome of Saccharomyces cerevisiae BY4741 in the presence of 100 μM of JWH-018. The proteome in the absence and in the presence of JWH-018 were analysed using Progenesis SameSpots (Totallab, UK). Throughout these comparisons triplicate 2D gels for each of the S. cerevisiae culture conditions were analysed. The range of protein separation obtained by a 2DE was 10–250 kDa and 3–10 pH and 2181 protein spots were detected through its analysis. Eight protein spots were found with statistically significant differences in their expression (P < 0.05).
mRNA expression analysis
To further understand the changes in the proteome, the mRNA expression of ADH4, ADH5, EMI2, FBA1, GPM1, GPP1, NPY1, PGI1, PRM15, REE1, SER33, SOL3, SOL4, TAL1, ZWF1 in the presence of JWH-018 was quantified by qRT-PCR. The concentration of JWH-018 chosen was 100µM, the same used in the 2D-Gel analysis. Three S. cerevisiae biological replicates were used both in the presence and absence of the drug. The results showed one gene upregulated, eight genes downregulated and five genes with no changes in mRNA expression after exposition to the SC (Fig. 4). The gene found upregulated in the presence of JWH-018 was FBA1 (1.21). In contrast, the genes found downregulated were ADH5 (0.87), EMI2 (0.66), NPY1 (0.77), PRM15 (0.84), REE1 (0.76), SOL3 (0.87), SOL4 (0.61) and ZWF1 (0.70). The genes without differences in mRNA expression were ADH4 (1.02), GPP1 (0.99), PGI1 (0.98), SER33 (0.92) and TAL1 (1.03). The statistical comparison between yeast mRNA expression in the absence and in the presence of JWH-018 showed six genes which present statistically significant differences, namely ADH5 (P < 0.1), EMI2 (P < 0.02), FBA1 (P < 0.02), SOL3 (P < 0.1), SOL4 (P < 0.02) and ZWF1 (P < 0.05) (Fig. 4). The mRNA results point an increase in the expression of several genes encoding glycolytic enzymes and a decreasing in the expression of the genes encoding enzymes from the oxidative phase of pentose phosphate pathway. These results suggest an increment in glycolytic pathway and a decrease in the pentose phosphate pathway flux (Fig. 4).
qRT-PCR showing relative changes of S. cerevisiae BY4741 in the presence of 100 μM of JWH-018. The results showed one gene upregulated, eight genes downregulated and six genes with no changes in mRNA expression after exposition to the SC. PGK1 was used as control.
To further understand the relationship between these yeast enzymes, an in silico network of proteins was generated (Fig. 5). A network map of known protein–protein associations was obtained using STRING database for yeast proteins altered in response to JWH-018, as well as for the correspondent human orthologues (Fig. 5). This network includes interactions derived from functional associations (dos Santos et al. 2012). A higher number of interactions between the yeast enzymes identified were found with the yeast dataset. Most of the proteins are involved in glycolysis and gluconeogenesis pathways. In spite of all yeast proteins having a human counterpart, the interactions predicted for the human dataset are considerably less numerous, but still find a correspondence for the aforementioned pathways. It is noteworthy that the conservation of the yeast dataset in the human proteome and the analogies observed in the two biological systems implies that the results obtained in here may allow to gain insights into the molecular mechanisms of JWH-018 in human cells.
Protein interactions established with the entire JWH-018-responsive yeast protein dataset and in the corresponding dataset of human functional homologs. The protein interaction network maps shown were adapted from the output of STRING database tools, using the default parameters. The yeast proteins whose showed differences when compared to the control were submitted to obtain the S. cerevisiae map, whereas for the Homo sapiens map a few homologs were identified. All the yeast and human proteins indicated in the clusters are involved in glycolysis and pentose phosphate pathway.
ATP quantification
To confirm if the glycolytic pathway is upregulated in the presence of JWH-018, the ATP intracellular content was quantified on the different experimental conditions. Our results showed a decrease of 17.4% in ATP levels when S. cerevisiae was exposed to JWH-018 (Fig. 6). This decrease was shown to be significantly different from the culture without SC (P < 0.001).
Intracellular ATP levels present in S. cerevisiae in the presence of 0 and 100 µM of JWH-018. The results showed a decrease in ATP levels in the presence of 100 µM of the SC (P < 0.001).
DISCUSSION
To evaluate the cellular impact of JWH-018, yeast growth curves were used. The analysis of the growing curves showed an increase in cell growth rate, in the presence of the SC, which implies a decrease in duplication time when compared to the control. For concentration values of JWH-018 equal or higher than 50 µM, the increase in growth rate is significant (P < 0.05) suggesting a faster grow of yeast in the presence of this SC. To verify the outcomes of the previous analysis, a CFU assay was performed. Despite not statistically significant, the results indicate a decrease in yeast doubling time in the presence of 50 µM of JWH-018. Interestingly, a previous study with SH-SY5Y and HEK293T human cell lines showed that JWH-018 seems to increase cell viability, suggesting that yeast response to this SC is similar to human cell response (44). However, this study did not clarify the mechanims underlying JWH-018 effect on cellular growth.
To understand which proteins were involved in the increment of S. cerevisiae growth rate when in the presence of the SC, a proteomic study was conducted. 2D gel electrophoresis from soluble yeast proteome exposed to JWH-018 showed 2181 spots. The statistical comparative analysis of the gels in the absence and presence of 100 µM of the SC showed significative differences among eight spots. However, these spots were within basic or acidic regions of the isoelectric focusing gel strip or in the most crowded region of the gel limiting the identification of the spots. To overcome this problem, a bioinformatic analysis was performed to identify relevant proteins whose isoelectric points and molecular weights were in agreement with the spots that showed statistical differences. This approach showed 15 proteins whose genes expression was monitored by qRT-PCR.
The monitored genes are mostly related with the glycolytic and pentose phosphate pathways (Fig. 7). One of the genes under analysis was EMI2 encoding a putative glucokinase-2 (EC:2.7.1.2) which is highly homologous to the glucokinase (EC:2.7.1.2). When expressed, this enzyme feeds both glycolysis and pentose phosphate pathway. However, EMI2 is completely repressed by high concentrations of glucose and is expressed in the presence of moderate concentrations of glucose (Lutfiyya et al. 1998). The quantitative mRNA analysis of EMI2 in the presence of JWH-018 showed a 50% decrease in its expression. This downregulation of EMI2 suggests that yeast in the presence of JWH-018 wrongly perceives a higher amount of glucose in the medium.
Representation of genes, enzymes and intermediates from glycolysis, pentose phosphate pathways and associated off-pathways in S. cerevisiae. The scheme shows the differences in gene expression from ADH4, ADH5, EMI2, FBA1, GPM1, GPP1, NPY1, PGI1, PRM15, REE1, SER33, SOL3, SOL4, TAL1, ZWF1 in the presence of JWH-018.
Regarding the glycolytic pathway, the mRNA expression of PGI1, FBA1, GPM1 and REE1 was monitored. PGI1 encodes phosphoglucose isomerase (EC:5.3.1.9), the first exclusive enzyme of glycolysis, which is responsible for the interconversion of glucose-6-phosphate to fructose-6-phosphate. FBA1 encodes frutose-1,6-biphosphate aldolase (EC:4.1.2.13) which catalyses the reversible cleavage of fructose-1,6-bisphosphate to glyceraldehyde 3-phosphate and dihydroxyacetone phosphate (Cieśla et al. 2014). GPM1 encodes phosphoglycerate mutase 1 (EC:5.4.2.11), a glycolytic enzyme responsible for the conversion of 3-phospholgycerate to 2-phosphoglycerate in glycolysis (Rodicio & Heinisch 1987; Smits et al. 2000; Price & Jaenicke 1982). Finally, REE1 encodes Ree1, a cytoplasmatic protein involved in the negative regulation of Eno1 (EC:4.2.1.11). Eno1 catalyses the transformation of 2-phospoglycerate to phosphoenolpyruvate. Among these four genes, PGI1 and GPM2 showed no differences in mRNA expression, whereas FBA1 (P < 0.02) showed an increase in mRNA expression and REE1 showed a decrease in mRNA expression in the presence of the SC. Previous studies demonstrated that the increment in FBA1 expression improves the glucose consumption rate (Wang et al. 2011). Consequently, an increment on FBA1 expression points to an increase in yeast glycolytic flux when JWH-018 is present. Furthermore, the observed decrease in REE1 expression reinforces the suggestion that glycolysis is upregulated by the SC, once Eno1 might be more active.
Additionally, four genes, indirectly related to glycolysis, were also studied, namely (i) GPP1, encoding a glycerol-3-phosphate phosphatase (EC 3.1.3.21), which is involved in glycerol biosynthesis (Norbeck et al. 1996), (ii) SER33 which encodes a D-3-phosphoglycerate dehydrogenase 2 and is involved in the reversible oxidation of 3-phospho-D-glycerate to 3-phosphonooxypyruvate, the first step of the phosphorylated L-serine biosynthesis pathway (Albers et al. 2003), and (iii) ADH4 and ADH5 genes which encodes alcohol dehydrogenase 4 and 5 (1.1.1.1) respectively, and are involved in the interconversion between aldehydes and primary alcohols. The GPP1, SER33 and ADH4 mRNA expressions have not showed differences between control and 100µM of JWH-018. ADH5 (P < 0.1) mRNA expression is decreased in the presence of the SC. This decrease does not seem relevant once five different alcohol dehydrogenases are present in yeast cells.
Concerning the pentose phosphate pathway, the mRNA expression of ZWF1, SOL3, SOL4 and TAL1 was relatively quantified. ZWF1 encodes glucose-6-phosphate dehydrogenase (EC:1.1.1.49) which catalyses the first step of the pentose phosphate pathway and competes with glycolysis for glucose 6-phosphate. When Zwf1 is not expressed, the pentose phosphate pathway is blocked (Jeppsson et al. 2002). SOL3 and SOL4 are paralogues genes encoding 6-phosphoglucolactonase 3 and 4 (EC:3.1.1.31) which catalysis the second step of pentose phosphate pathway (Stanford et al. 2004). In spite of the hydrolysis of the lactone ring is spontaneous, the lack of these enzymes decreases substantially the flux of the above-mentioned pathway (Yang et al. 2013). PRM15 encodes the major phosphoribomutase (EC:5.4.2.7) that converts ribose 1-phosphate to ribose 5-phosphate in ribose salvage via the pentose phosphate pathway (Tiwari and Bhat 2008). Finally, TAL1 encodes a transaldolase (EC:2.2.1.2) from the non-oxidative branch of pentose phosphate pathway which converts sedoheptulose 7-phosphate and glyceraldehyde 3-phosphate to erythrose 4-phosphate and fructose 6-phosphate (Schaaff, Hohmann and Zimmermann 1990). Among these five genes, TAL1 showed no differences in mRNA expression, while ZWF1 (P < 0.05), SOL3 (P < 0.1), SOL4 (P < 0.02) and PRM15 showed a decrease in mRNA expression in the presence of the JWH-018. The significative decrease in mRNA expression of ZWF1, SOL3 and SOL4 mRNA clearly points to a reduction in the flux of glucose-6-phosphate in the oxidative branch of pentose phosphate pathway. Moreover, the decrease in PRM15 mRNA expression suggests a tendency to reduce the transformation of ribose-5-phosphate to ribose-1-phosphate, diminishing the outflux of ribose-5-phosphate from the pentose phosphate pathway.
Lastly, mRNA expression of NPY1, a gene not directly associated with glycolysis and pentose phosphate pathway, was also monitored. NPY1 encodes a diphosphatase (pyrophosphatase) (EC:3.6.1.22) with NADH as the preferred substrate, originating NMNH and AMP as products (AbdelRaheim et al. 2001). NPY1 mRNA expression is decreased in the presence of JWH-018. Once NAD+ is essential for glyceraldehyde 3-P dehydrogenase phosphorylative oxidation, a key step in glycolysis, the results suggest that decreasing NAD+ turnover might turn this molecule more available to anaerobic glucose consumptions.
In general, metabolic pathways are controlled by regulation of enzymes activity and variation in their amount. For example, the flux of glucose-6-phosphate through the pentose-phosphate pathway in yeast cells is controlled by variation in the amounts of glucose-6-phosphate dehydrogenase (Yang et al. 2013). The mRNA expression results suggest the increase in Fba1 and Gpm1 in glycolysis and the decrease in Zwf1, Sol3 and Sol4 in pentose phosphate pathway. These data support that JWH-018 increases the flux of glucose through glycolysis at expenses of pentose phosphate pathway (Fig. 7). Such metabolic shift might reduces NADPH available to cytochrome P450 used in phase I metabolism SC. However, yeast has not homologous of the cytochromes P450 which metabolized JWH-018 in humans, namely CYP1A2 and 2C9 (Chimalakonda et al. 2012).
An increase of glycolysis should be combined with a change in ATP levels. In fact, there is a strong negative correlation between glycolytic flux and intracellular ATP content, meaning that the lower the ATP content the higher the rate of glycolysis (Lagunas 1976; Larsson et al. 1997; Somsen et al. 2000; Özalp et al. 2010). To corroborate the upregulation of glycolysis by JWH-018 an ATP measurement of yeast cells in the presence and absence of the SC was taken. The results showed a decrease of 17.4% in ATP yeast levels in the presence of this SC, confirming an upregulation of glycolysis.
Overall, the results strongly suggest that the SC JWH-018 promotes cellular growth by modulating protein levels of glycolytic and pentose phosphate enzymes. Moreover, the conservation of the yeast dataset in the human proteome and the analogies observed in the two biological systems implies that the results obtained in here may allow to gain insights into the molecular mechanisms of JWH-018 in human cells.
CONCLUSION
Previous studies showed that JWH-018 increases human neuroblastoma cells viability (Couceiro et al. 2016). The present work observed an increase in the growth rate of S. cerevisiae exposed to the same compound, in line with the above-mentioned results. However, the underlying mechanisms of this effect was not clarified before. In fact, since the appearance of JWH-018 in the headshops for recreational use, ten years ago, there are no published studies explaining the metabolic changes associated to the increase in cellular growth. The results in this study showed that the increase in S. cerevisiae growth rate, in the presence of JWH-018, is due to an enhanced glycolytic flux at expense of a decrease in pentose phosphate pathway. The present work implements S. cerevisiae as a model to better understand SC at fundamental molecular and cellular levels, allowing to design oriented studies in human cells or animal models. Along with recent studies on synthetic cathinones, yeast has been revealing an excellent model to study novel psychoactive substances (Ferreira et al. 2019).
ACKNOWLEDGEMENTS
This work was funded by Egas Moniz, Cooperativa de Ensino Superior. We thank its unconditional support. TFO is supported by the DFG Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB).
CNS and ST also acknowledge Fundação para a Ciência e Tecnologia financial support (IF/01097/2013 and SFRH/BPD/101646/2014, respectively) iNOVA4Health Research Unit (LISBOA-01–0145-FEDER-007344), which is cofounded by FCT through national funds, and by FEDER under the PT2020 Partnership Agreement, is acknowledged. The Forensic and Psychological Sciences Laboratory has a license to possess and use, for scientific purposes, drugs of abuse. The authors declare no conflict of interest.
Conflict of interest. None declared.
