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Tolerance to Chronic Delta-9-Tetrahydrocannabinol (Δ9-THC) in Rhesus Macaques

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Although Δ9-THC has been approved to treat anorexia and weight loss associated with AIDS, it may also reduce well-being by disrupting complex behavioral processes or enhancing HIV replication. To investigate these possibilities, four groups of male rhesus macaques were trained to respond under an operant acquisition and performance procedure, and administered vehicle or Δ9-THC before and after inoculation with simian immunodeficiency virus(SIVmac251, 100 TCID50/ml, i.v.). Prior to chronic Δ9-THC and SIV inoculation, 0.032— 0.32 mg/kg of Δ9-THC produced dose-dependent rate-decreasing effects and small, sporadic error-increasing effects in the acquisition and performance components in each subject. Following 28 days of chronic Δ9-THC (0.32 mg/kg, i.m.) or vehicle twice daily, delta-9-THC-treated subjects developed tolerance to the rate-decreasing effects, and this tolerance was maintained during the initial 7—12 months irrespective of SIV infection (i.e., +THC/−SIV, +THC/+SIV). Full necropsy was performed on all SIV subjects an average of 329 days post-SIV inoculation, with postmortem histopathology suggestive of a reduced frequency of CNS pathology as well as opportunistic infections in delta-9-THC-treated subjects. Chronic Δ9-THC also significantly reduced CB-1 and CB-2 receptor levels in the hippocampus, attenuated the expression of a proinflammatory cytokine (MCP-1), and did not increase viral load in plasma, cerebrospinal fluid, or brain tissue compared to vehicle-treated subjects with SIV. Together, these data indicate that chronic Δ9-THC produces tolerance to its behaviorally disruptive effects on complex tasks while not adversely affecting viral load or other markers of disease progression during the early stages of infection.
Dronabinol (Marinol) is a synthetic form of the psycho-active constituent of marijuana, delta-9-tetrahydrocannabinol (Δ9- THC), which has been approved by the U. S. Food and Drug Administration for the treatment of HIV-associated anorexia (Beal et al., 1995). Although this approval has been in place since the mid-1990s, only a small amount of scientific data exists to support the efficacy of such an intervention (Beal et al., 1995, 1997). Moreover, concerns have been raised regarding the adverse effects of the cannabinoids and how those effects could interact with a severely compromised immune system (Benito et al., 2005; Bredt et al., 2002) or neurobehavioral dysfunction in HIV-infected individuals (Cristiani, Pukay-Martin, & Bornstein, 2004; Whitfield, Bechtel, & Starich, 1997).
The potentially deleterious interaction between the cannabinoids and AIDS-related neuronal and cognitive dysfunction is not fully understood nor has it been fully investigated. In individuals diagnosed with an "AIDS-defining" illness, a wide variety of studies have helped characterize the extent to which neuropsychological performance changes following infection (Grant, Heaton, & Atkinson, 1995; Navia, Jordan, & Price, 1986; Newman, Lunn, & Harrison, 1995; Perdices & Cooper, 1990; Selnes et al., 1995). From these studies, a clinical triad has been identified that includes cognitive impairment, motor dysfunction, and behavioral abnormalities (Power & Johnson, 1995). These progressive signs of decline have also been posited to represent subcortical neuronal damage that likely result from neurological injury instigated during the disease process (Grant et al., 1995; Navia et al., 1986; Power & Johnson, 1995). Among the many areas of cognitive functioning that can be altered by neurodegenerative diseases are changes in motor speed, attention, verbal behavior, nonverbal behavior, acquisition or learning, and memory. According to Grant et al. (1995), for example, HIV-1-associated cognitive impairments generally involve at least two ability domains (e.g., memory and attention), but impairment of multiple domains is common.
In contrast to HIV or SIV, chronic Δ9-THC has not been shown to produce marked neuropathological alterations that lead to neuropsychological manifestations, and has not been as well studied in animals or man (Carlini, 2004; Landfield, Cadwallader, & Vinsant, 1988). Moreover, many of the existing studies in humans suffer from experimental limitations such as a failure to control for the redistribution of accumulated Δ9-THC from fat stores (see Gruber & Yurgelun-Todd, 2001). In all likelihood, chronic consumption of Δ9-THC could compromise functioning of some of the same structures in the brain that are affected by immunodeficiency viruses. For example, Δ9-THC is known to affect reaction time (Hunault et al., 2009; Wilson, Ellinwood, Mathew, & Johnson, 1994) and working memory (Hampson & Deadwyler, 1999; Lichtman, Dimen, & Martin, 1995), which is thought to involve subcortical structures and the hippocampus, respectively. In addition, there is the possibility that the disruptive effects of Δ9-THC on neurobehavioral processes might be greater in the presence of an immunodeficiency virus (IV), just as the disruptive effects of alcohol on neurobehavioral processes have been shown to be greater in the presence of HIV in humans (Green, Saveanu, & Bornstein, 2004; Rothlind et al., 2005) and of SIV in monkeys (Winsauer, Moerschbaecher, et al., 2002).
One of the most important attributes of the SIV-monkey model is that some of the same behavioral and neuropathological sequelae that are observed in the human neuroAIDS syndrome have also been shown to occur in SIV-infected monkeys (da Cunha, Eiden, & Rausch, 1994; Heyes et al., 1992; Murray, Rausch, Lendvay, Sharer, & Eiden, 1992; Prospero-Garcia et al., 1996). For example, one common finding between humans and monkeys has been that motor-skill deficits occurred earlier and more frequently than cognitive impairments (Gold et al., 1998; Horn, Huitron-Resendiz, Weed, Henriksen, & Fox, 1998; Marcario et al., 1999; Rausch et al., 1994). Although all of the findings between HIV-infected humans and SIV-infected monkeys have not been this straightforward, the vast majority of findings in monkeys have strong parallels with the findings in humans (see Cheney, Riazi, & Marcario, 2008, or Fox, Gold, Henriksen, & Bloom, 1997). In fact, studies involving rhesus monkeys have established the SIV model as an important tool for directly investigating cognitive and motor deficits produced by infection and have allowed investigators to eliminate many of the variables that confound human studies, such as the effects of antiviral therapy, education level, age, and socioeconomic status.
The purpose of the present study was to examine the interaction of chronic Δ9-THC with SIV infection in male rhesus monkeys trained to respond on two separate behavioral tasks. The first task required subjects to learn a new stimulus-response pattern each day (acquisition), and the second task required subjects to repeat an unchanging stimulus-response pattern each day (performance). The purpose of this behavioral baseline was to measure any occurrence of behavioral impairments as part of a longitudinal experimental design to examine the effect of Δ9-THC and SIV on host defense. Therefore, assessment of an animal's ability to acquire new information (an ability that has been shown to be quite sensitive to disruption by both HIV and Δ9-THC) might help elucidate deficits produced by both types of insult and also establish an interaction between the two. The specific technique chosen for measuring learning in this study is commonly referred to as "repeated acquisition" among behavioral scientists, and it was first described by Boren (1963) as a means for studying the effects of drugs on learning. Since that time, this technique has been widely used in the characterization of drug effects on the acquisition, performance, and retention of complex discriminations in both experimental animals and humans (Thompson & Moerschbaecher, 1979). Identifying preclinical research techniques that can also be used with humans is a significant challenge, particularly for relatively complex cognitive abilities that require learning or memory, because few techniques are capable of repeatedly assaying the effects of a neurotoxic insult over time (see Winsauer & Mele, 1993). The repeated-acquisition task is particularly well suited for studying the effects of drugs on learning because it is (a) valid and generalizable in humans, nonhuman primates, and rodents (Bickel, Hughes, & Higgins, 1989; Brodkin & Moerschbaecher, 1997; Kamien, Bickel, Higgins, & Hughes, 1994; Nakamura-Palacios, Winsauer, & Moerschbaecher, 2000; Winsauer, Lambert, & Moerschbaecher, 1999), (b) able to assess both the quantity and quality of behavior (Cohn, Ziriax, Cox, & Cory-Slechta, 1992), (c) sensitive to numerous drugs and drug classes (e.g., Thompson, Winsauer, & Mastropaolo, 1987; Winsauer, Delatte, Stevenson, & Moerschbaecher, 2002), (d) capable of detecting the chronic effects of a drug (Cohn, Cox, & Cory-Slechta, 1993; Delatte, Winsauer, & Moerschbaecher, 2002; Thompson, 1974), and (e) useful for assessing the effects of pharmacological challenges in animals with neurotoxic lesions (Cohn & Cory-Slechta, 1993).
In addition to assessing the effects of chronic Δ9-THC on complex operant behaviors, disease progression was monitored by assaying plasma viral load and examining each subject's health status. As SIV infection progressed and subjects met specified criteria for euthanasia, they were sacrificed, underwent necropsy, and tissues were banked for further analysis. Disease progression in the brain was examined histopathologically and by analyses of viral load and the expression of specific inflammatory cytokines. Cannabinoid receptor expression in the hippocampus was also determined by Western blot. Hippocampal expression of cannabinoid receptors was conducted because this brain area is known to be integrally involved in working memory (Hampson & Deadwyler, 1999, 2000) and has been shown to be affected by both acute (e.g., Lichtman et al., 1995) and chronic cannabinoid administration (e.g., Coutts et al., 2001; Landfield et al., 1988).

Fifteen domestically bred 4- to 5-year-old, experimentally naïve male rhesus monkeys of Indian origin (Macaca mulatta) were assigned to four treatment groups–vehicle/uninfected (Veh/−SIV, n = 3), vehicle/SIV infected (Veh/+SIV, n = 4), Δ9-THC/uninfected (THC/−SIV, n = 4), and Δ9-THC/SIV infected (THC/+SIV, n = 4)–to serve as subjects. These subjects were housed individually in aluminum cages (BREC, Inc., Bryan, TX) in a room maintained on a 12:12-hour light:dark cycle at approximately 22 °C (range, 18 to 26 °C), with a 30% to 70% relative humidity. The subjects were maintained at about 90% of their free-feeding weights on a diet consisting of banana-flavored food pellets (Purina Mills TestDiet, Richmond, IN), standard primate chow (Formula 2050, Harlan Teklad, Madison, WI), fruit, and vitamins. Body weights of the individual subjects in each treatment group are shown in Table 1, and these values are consistent with normative growth curves for rhesus macaques (van Wagenen & Catchpole, 1956; Vančata, Vančatova, Chalyan, & Meishvilli, 2000). The pellets were earned during experimental sessions, whereas the monkey chow, fruit, and vitamins were given to each subject after the daily session. Water was available ad libitum except during behavioral testing.
For behavioral testing, subjects were tested serially in subgroups of four monkeys throughout the day due to the small number of response panels. This resulted in the subjects being tested at varying times after the morning administration of Δ9-THC or vehicle (see "Chronic Δ9-THC Administration" section). These times ranged from 30 minutes to 7 hours after the morning injection; however, the various treatments were pseudorandomly distributed throughout the subgroups to control for the time of day and time after the chronic injection. Prior to testing, each subject was removed from the colony-room cage and transported via a macaque restrainer (Primate Products, Inc., Redwood City, CA) to experimental test cages located in another room. These studies were carried out in accordance with the Institutional Care and Use Committee of the Louisiana State University Health Sciences Center and the guidelines of the Committee on Care and Use of Laboratory Animal Resources, as adopted and promulgated by the U.S. National Institutes of Health.

Similar to that portrayed in Thompson and Moerschbaecher (1978), removable response panels were attached to the side of each of four experimental test cages. Each aluminum response panel contained three translucent response keys aligned horizontally with stimulus projectors mounted behind them. The in-line stimulus projectors projected colors and geometric forms onto the key. In addition to these stimuli, there was a single, tricolored stimulus located above the center stimulus key and a single response key that could be illuminated with white light located above the aperture for food pellets. A pellet dispenser (BRS/LVE, Beltsville, MD) located behind the response panel delivered 500-mg pellets into the aperture. Response keys required a minimum force of 0.15 N for activation, and each correct response on the keys produced an audible click of a feedback relay. Recessed fluorescent lights in the ceiling of the room provided overhead lighting. All four experimental test cages were connected via an interface to a computer programmed in MED-PC for Windows, Version IV (Med Associates, Inc., St. Albans, VT).

Behavioral Procedure
Responding by subjects was stabilized under a multiple-schedule procedure comprised of three components: timeout, repeated-acquisition (acquisition), and performance. The presentation of each of the three components comprised one cycle of this multiple schedule, and behavioral testing sessions were comprised of 1— 4 cycles. This baseline of behavioral responding was also stabilized before the respective treatments with Δ9-THC and/or SIV commenced. This was critical not only for ensuring that the behaviors established were stable, but also for establishing a baseline of responding for acquisition and performance behavior for each animal and each group of animals prior to any treatments. During the acquisition component, all three response keys were illuminated simultaneously with one of five geometric symbols projected on a black background: squares, horizontal bars, triangles, vertical bars, or circles. The task for each subject was to respond (key press) on the correct key in the presence of each sequentially illuminated set of geometric symbols (e.g., keys with squares, center correct; keys with horizontal bars, left correct; keys with triangles, center correct; keys with vertical bars, right correct; keys with circles, left correct). When the response sequence was completed, the key lights were turned off and the response key over the food pellet aperture was illuminated. A press on this key reset the sequence. Responding on this sequence (in this case, center-left-center-right-left, or, CLCRL) was then maintained by food presentation under a FR-3 (training) or FR-4 (testing) schedule, that is, every third or fourth completion of the sequence produced a 500-mg food pellet following a press on the key located over the food pellet aperture. When the subject pressed an incorrect key (in the example, the left or right key when the square symbols were presented), the incorrect response (error) was followed by a 5-s timeout. During timeouts, the key lights were off and responses had no programmed consequence. An incorrect response did not reset the five-response sequence, as the stimuli were the same before and after the timeout.
To establish a steady state of repeated acquisition, the five-response sequence in this component was changed from cycle to cycle. An example of a typical set of six sequences was as follows: LRCRC, CLRLR, LRLCL, RCRLC, CLCRL, RCLCR, with the order of the geometric symbols always squares, horizontal bars, triangles, vertical bars, and circles. The sequences were carefully selected to be equivalent in several ways and there were restrictions on their ordering across sessions (for restrictions, see Winsauer, Moerschbaecher, et al., 2002).
During the performance components of the multiple schedule, the geometric symbols that identified each response in the five-response sequence were projected on a green background. The green background on each key served as discriminative stimuli for this component. Unlike the acquisition component, the five-response sequence in the performance component remained the same from cycle to cycle and session to session (i.e., LCLRL) and was never used in the acquisition components. In all other aspects (second-order FR-3 or FR-4 schedule of food presentation, timeout duration of 5 s, etc.), the performance component was identical to the acquisition component.
All experimental sessions began with a timeout component, which was followed immediately by an acquisition component and a performance component. On days in which no injections were administered (baseline days), timeout components were 10 min in duration, repeated-acquisition components were 15 min in duration and performance components were 5 min in duration. Due to the relatively long onset period for the effects of Δ9-THC, on days in which Δ9-THC was administered, timeout components were 30 min in duration, whereas the durations for the acquisition and performance components did not change. The principal dependent measures for both the acquisition and performance components were response rate and the percentage of errors. Responding for each subject was considered stable when overall response rates in the acquisition and performance components did not differ from the respective mean rate by ± 20% and the percentage of errors did not exceed the mean percentage of errors by more than 20% from cycle to cycle. In addition, the daily pattern of errors in the acquisition components had to be characterized by a steady state in terms of within-session error reduction, which was determined by visual inspection of the data and indicated by a distinct decrease in the number of errors and a concomitant increase in errorless completions of the response sequence.
The overall timeline for the experimental protocol, including inoculation, is depicted in Figure 1. Vehicle and delta-9-THC administration occurred twice daily (Monday through Sunday), whereas behavioral testing occurred over five consecutive days (Monday through Friday). When blood work and physicals were scheduled, behavioral testing was suspended for that day. Behavioral testing and food restriction were also suspended every 3 months for 5 days when the subjects were placed in metabolic cages to assess a range of physiological measures such as nutrient intake and metabolic rate (e.g., Molina et al., 2010). In general, blood analyses were scheduled for postinoculation Days 7, 14, 28, and monthly thereafter.

Cumulative-Dosing Procedure and Acute Δ9-THC Administration
Following training and stable responding under the behavioral procedure, a cumulative-dosing procedure was used to determine dose-effect curves for the acute effects of Δ9-THC in all of the subjects. The acute effects of Δ9-THC were also established in both vehicle- and Δ9-THC-treated subjects throughout the study for the purposes of continuously assessing any within- or between-groups changes in response to drug. The use of a cumulative-dosing procedure was particularly important because it allowed for the determination of an entire dose-effect curve in a single session, which limited the dose-to-dose variability that might result from the progression of the disease if a more traditional acute-dosing procedure was used. Essentially, because a given experimental session was comprised of two to five cycles of the multiple schedule, increasing dosages of drugs could be administered prior to the start of each timeout component to obtain the entire dose-effect curve. In general, the dose-effect curve ranged from one that produced little or no effect to one that substantially reduced responding. All of the doses were given intramuscularly (i.m.) in the gluteus muscles, and successive injections increased the cumulative dose by [1/4] or [1/2] log-units. For example, 0.032 mg/kg of Δ9-THC was injected 30 min before the first acquisition cycle, 0.024 mg/kg before the second cycle, 0.044 mg/kg before the third cycle, and 0.08 before the fourth cycle, thereby producing a cumulative dose-effect curve of 0.032, 0.056, 0.1, and 0.18 mg/kg for Δ9-THC. Saline or vehicle alone was also administered 30 min prior to the start of the first timeout component and at the start of each successive timeout component as a control. Dose-effect determinations in the same range were made before chronic treatment began (prechronic) and then after each subject received 28 days of chronic treatment (postchronic) to probe for the development of tolerance. Dose-effect curves for increasing dose ranges of Δ9-THC were also determined periodically between SIV inoculation and euthanasia (see criteria in Post-SIV Necropsy section) to assess the effect of this treatment on the magnitude and duration of tolerance development. Therefore, the number of determinations that comprised an individual subject's dose-effect data varied from subject to subject. If the cumulative dose-effect curves for Δ9-THC were determined in the morning, the chronic dose administered in the morning was replaced with vehicle. If the cumulative dose-effect curves were determined in the afternoon, the chronic dose administered in the evening was omitted.

Chronic Δ9-THC Administration
After determining dose-effect curves for Δ9-THC in all of the subjects, they were divided into two groups based on their baseline of behavior and response to infectivity. Comparability of baseline behavior was determined by a comparison of each subject's response rate and percentage of errors under the multiple schedule, whereas comparability of infectivity was determined by an in vitro assay using peripheral blood mononuclear cells (Seman, Pewen, Fresh, Martin, & Murphey-Corb, 2000). Following these assessments, subjects were treated chronically with either Δ9-THC (n = 8) or vehicle (n = 7) i.m. twice daily (8:30 a.m. and 5:30 p.m.) for 28 days. Chronic administration of Δ9-THC was initiated with 0.18 mg/kg and then increased to 0.32 mg/kg, the dose administered for the rest of the study; 0.18 mg/kg was the initial dose because this was a significantly effective dose for the entire group of subjects prior to chronic treatment. The increase in dose to 0.32 mg/kg was individualized for every subject, but it was increased for almost all of the subjects after approximately two weeks when responding was no longer affected by 0.18 mg/kg on a daily basis (i.e., tolerance developed). This was determined simply by administering the chronic dose 30 min prior to the respective behavioral session for each subgroup and noting the amount of responding that occurred by visual inspection. For one subject (RX), the chronic dose was kept at 0.18 mg/kg for the entire study due to this subject's sensitivity to Δ9-THC.
Simian Immunodeficiency Virus (SIV) Inoculation and Quantification by Real-Time PCR
After 28 days of Δ9-THC or vehicle administration, and a redetermination of the acute effects of Δ9-THC, 4 subjects from each chronically treated group were anesthetized with ketamine hydrochloride (10 mg/kg, i.m.) and inoculated intravenously with SIVmac251. Subjects were inoculated with 100 times the 50% tissue culture infective dose (TCID50) of SIVmac251 via the saphenous vein using a 23-gauge catheter. Following inoculation, samples of both plasma and CSF were collected at 2 weeks postinoculation and then monthly thereafter, as indicated in Figure 1. Viral load in brain tissue was determined in samples collected from frozen brain (see below).
Viral levels in plasma, CSF, and brain tissue were determined by a quantitative real-time reverse-transcriptase PCR assay (qRT-PCR) that targets a highly conserved region of the SIV genome located within the structural gene gag. Virus particles were isolated from 1 ml plasma or 200 μl CSF by centrifugation at 20,000 g for 60 min, and viral RNA were purified with Trizol reagent (Invitrogen, part of Life Technologies, Carlsbad, CA), according to manufacturer's directions. For the isolation of RNA from brain tissues, flash frozen samples of tissue were placed in RNA-later-ICE solution (Ambion Inc., Austin, TX) overnight and then homogenized in Trizol reagent. One tenth and one fourth of the total RNA isolated from plasma and CSF samples were added to duplicate qRT-PCR reactions, respectively, whereas approximately 100 ng of RNA prepared from brain tissues were assayed in duplicate reactions. qRT-PCR reactions contained Multiscribe reverse transcriptase and Taq-Man universal master mix from Applied Biosystems (Foster City, CA), as well as 250 nM of SIV-gag specific forward and reverse primers and 150 nM of FAM-labeled probe. The SIV Primers were as follows: Forward, 5′ GCGTCATTTGGTGCATTCAC-3′; Reverse, 5′ TC-CACCACTAGATGTCTCTGCACTAT-3′; and Probe, 5′ 6FAM-TGTTTGCTTCCTCAGTATGTTTCACTTTCTCTT-CTG-TAMRA. Fluorescence was measured with an ABI 7300 RT-PCR detection system (Applied Biosystems) in each of 40 cycles, and the threshold cycle (CT) at which fluorescence was greater than background was determined. SIV RNA copy number in each sample was determined by extrapolation from a standard curve generated from serial dilutions of a SIV RNA control template produced in vitro, which includes the gag sequences targeted by the primers. Cell-associated viral RNA levels were normalized by the simultaneous amplification of rhesus GAPDH mRNA levels (Applied Biosystems), expressed as SIV-RNA copies per μg mRNA. The assay has a sensitivity of 1 copy, and the limit of reproducible quantification is 5 copies of SIV/reaction, which translates to a quantitation limit of 50 copies SIV/ml of plasma, 100 copies SIV/ml of CSF, and approximately 50 copies SIV/μg mRNA in these studies.

Post-SIV Necropsy
Subjects meeting at least two of the criteria for euthanasia were necropsied by one of two autopsy pathologist prosectors. The specific criteria for euthanasia were (a) loss of 25% of body weight from the maximum body weight since assignment to the protocol; (b) decreased serum albumin to less than 3 mg/dl associated with edema; (c) anemia and thrombocytopenia; (d) three days of complete anorexia; or (e) major organ failure or medical conditions unresponsive to treatment such as respiratory distress, intractable diarrhea, or persistent vomiting. Evisceration and tissue collection was performed according to standardized collection procedures for each organ and tissue of interest. Representative pieces from selected tissues were fixed in 10% buffered formalin and subsequently paraffin embedded, sectioned and stained with hematoxylin and eosin. When indicated, Gomori methanamine silver stain (GMS) was utilized for confirmation of Pneumocystis organisms and Fite's acid stain was utilized for confirmation of Mycobacterium. For neuropathological examination, the scalp was incised coronally and then reflected to reveal the outer calvarium. A Stryker autopsy saw (Stryker Instruments, Kalamazoo, MI) was used to circumferentially remove the superior calvarium and its underlying dura mater. The brain was removed by incision of the tentorium and any adherent cranial nerves. The brain was then weighed in its fresh state and the most distal tip of one temporal lobe was retained in 10% buffered formalin for tissue fixation. The remainder of the brain was then flash frozen in liquid nitrogen and stored for subsequent analyses of cannabinoid (CB) receptors and cytokine expression. Histopathological analysis on all tissues of interest was performed blindly by both participating autopsy pathologists.

Expression of CB-1 and CB-2 Receptors by Western Blot Analysis
After all of the subjects were sacrificed, analysis of CB-1 and CB-2 receptor protein levels in the hippocampus was conducted as described previously (Winsauer et al., 2011). For these analyses, 200 μg of hippocampal tissue was resuspended in a lysis buffer (20 mM Tris pH 8.0, 137 mM NaCl, 0.5 mM sodium orthovanadate, 2 mM okadaic acid, 10% glycerol, 1% Nonidet P40, 2% protease inhibitor) and processed for protein extraction using MicroRotofor Lysis Kit (Bio-Rad Laboratories, Inc., Hercules, CA) following the manufacturer's protocol. The protein concentration was determined using the Bradford Method (Bradford, 1976) and diluted to 1 μg/μl with 1×SDS buffer. Equal amounts of protein from each area (50 μg) were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE 12%) and transferred to nitrocellulose PDVF membranes (Bio-Rad Laboratories, Inc.). The membranes were subsequently immunoblotted for 2 hr at room temperature with specific antibodies: a rabbit anti-CB-1 receptor (1:500 dilution) from Enzo Life Sciences (Plymouth Meeting, PA), a rabbit anti-CB-2 receptor (1:500) from Cayman Chemical (Ann Arbor, MI), and a mouse anti-β-actin (1:2000 dilution) from Santa Cruz Biotechnology (Santa Cruz, CA). These antibodies were then followed by specific secondary antibodies (1:2000 for 90 min) and visualized using ECL Plus (PerkinElmer Life Sciences, Waltham, MA) and a Fuji Film luminescent image analyzer (LAS-1000 Plus, Fuji Photo Film Co. LTD., Tokyo). The identity of the CB1R and CB2R bands were confirmed by their disappearance in samples exposed to preadsorption of the antibody with the control antibody. The images were quantified by densitometry using the Image Gauge program (Filipeanu, Zhou, Claycomb, & Wu, 2004). For each sample, the value of CB-1 and CB-2 receptor expression was normalized to β-actin values.

Brain Cytokine Content Determination by Luminex-Based Multiplex Immunoassay
Tissue from three brain areas was excised from the brains of subjects in the two SIV-infected treatment groups. The three brain areas were the lateral cerebellum (LC), prefrontal cortex (PFC), and orbital frontal cortex (OFC). LC was chosen for its density of cannabinoid receptors (Herkenham et al., 1991). PFC was chosen for its involvement in complex cognitive processes (Silva de Melo et al., 2005), and OFC for its involvement in drug abuse and the inhibitory control of behavior (Goldstein et al., 2007). Luminex immunoassays were carried out using the Bio-PlexTM Protein Array System (Bio-Rad Laboratories, Inc.) according to the manufacturer's recommended protocols. Because the levels of cytokines in each of the three brain areas were very similar, only an average for the three areas is presented for each vehicle- and Δ9-THC-treated subject.

Data Analyses
The behavioral data for each session were analyzed in terms of overall response rate (total responses/min, excluding timeouts) and overall accuracy, expressed as the percentage of errors [(incorrect responses/correct + incorrect responses) × 100]. These data were grouped and analyzed statistically depending on the particular treatment period. For example, prior to chronic Δ9-THC, the data for response rate and the percentage of errors were analyzed using a one-way repeated measures ANOVA (SigmaStat Statistical Software, SPSS, Inc.), with dose serving as the repeated measure. Holm-Sidak tests were then used to make post hoc comparisons to determine Δ9-THC doses that were significantly different from acute saline administration. To compare the effects of Δ9-THC before and after the 28 days of chronic Δ9-THC or vehicle administration, a two-way repeated measures ANOVA (general linear model) was used. The factors in this case were the time of administration (prechronic vs. postchronic) and the dosage of Δ9-THC depending on the group. Significant interactions determined by a two-way ANOVA were also followed by one-way ANOVA tests and Holm-Sidak post hoc tests to compare the effects of the different doses of Δ9-THC from saline administration (control) and to compare the effects of individual doses before and after chronic Δ9-THC administration. To compare viral load data between chronic treatment groups, the factors for the two-way ANOVA were chronic treatment (vehicle vs. Δ9-THC) and time (i.e., months post SIV), whereas the factors for viral load in brain tissue were treatment and brain area. A two-way repeated measures ANOVA was also conducted to assess the effects of Δ9-THC on cytokine expression in the brain of SIV-infected subjects. For this analysis, chronic treatment served as a between-groups factor and the type of cytokine served as a repeated measure. The quantitative analysis of the Western blots was conducted separately for each receptor subtype and consisted of a one-tailed t test that compared Δ9-THC-treated and vehicle-treated values. The analysis was a one-tailed test due to the assumption that chronic Δ9-THC would only decrease cannabinoid receptor expression.
Changes in the sensitivity of each of the 4 groups (Veh/−SIV, THC/−SIV, Veh/+SIV, THC/+SIV) to the effects of Δ9-THC as a result of chronic treatment were also quantified by comparing the ED50 values of the dose-effect curves depicted in Figures 4 and ​and5.5. These ED50 values were determined by linear regression using two or more data points reflecting the slopes of the descending curve for response rate or the ascending curve for the percentage of errors. For response rate, the ED50 represented the estimated dose of Δ9-THC that decreased responding from control levels by 50%. For the percentage of errors, the ED50 represented the estimated dose of Δ9-THC that increased the percentage of errors from control levels by 50% (i.e., ED150).


Acute Effects of Δ9-THC on Behavioral Responding
Figure 2 shows that prior to chronic treatment or SIV inoculation, cumulative doses of 0.032—0.32 mg/kg of Δ9-THC produced dose-dependent rate-decreasing effects in the acquisition, F(4, 56) = 13.62, p < .001, and performance, F(4, 56) = 7.23, p < .001, components in the entire group of subjects when compared to acute administrations of saline. Post hoc tests also revealed that the 0.18-mg/kg dose of Δ9-THC significantly decreased responding in both behavioral components of the task when compared to saline administration. In contrast to the effects on response rate, the same doses of Δ9-THC had little or no effect on the mean percentage of errors or accuracy in the acquisition, F(4, 37) = 0.44, p > .05, and performance, F(4, 37) = 0.96, p > .05, components up to doses that eliminated responding.

Acute Effects of Δ9-THC on Behavioral Responding After 28 Days of Chronic Δ9-THC Administration
Figure 3 shows the acute disruptive effects of Δ9-THC on the rate and accuracy of responding after 28 days of either vehicle or Δ9-THC (0.18 — 0.32) administration. In the vehicle-treated group, the acute disruptive effects of Δ9-THC on rate and accuracy in the acquisition and performance components were similar before and after chronic administration. More specifically, for the three doses of Δ9-THC tested, there were no significant main effects of chronic treatment and no significant interactions between chronic treatment and the dosage in either behavioral component. The only significant effect in this group was a main effect of dose on response rate in the acquisition components, F(3, 16) = 5.44, p = .007. Subsequent post hoc tests indicated that 0.18 mg/kg significantly reduced response rate compared to saline administration.
In the Δ9-THC-treated group, the acute disruptive effects on rate and accuracy in the acquisition and performance components were substantially different from those obtained prior to chronic administration, particularly for response rate, where there was a significant main effect of chronic treatment in the acquisition components, F(1, 14) = 12.10, p = .01, and a significant interaction between chronic treatment and dose in the performance components, F(2, 8) = 16.70, p < .001. Both of these results from the two-way ANOVA tests are indications of the large differences between the disruptive effects of Δ9-THC before and after chronic Δ9-THC administration. For example, prior to chronic Δ9-THC administration, 0.18 mg/kg decreased responding in the acquisition component from 17.4 responses per minute under control conditions (acute saline administration) to 2.7 responses per minute (an 85% reduction). After chronic Δ9-THC administration, a larger dose, 0.32 mg/kg, had little or no effect on response rate or errors in this component compared to the respective postchronic control data. Similarly, responding in the performance component was substantially reduced by 0.18 and 0.32 mg/kg of Δ9-THC prior to chronic Δ9-THC administration as compared to saline administration, F(3, 18) = 23.44, p < .001, whereas 0.32 mg/kg had no effect on response rate after 28 days of chronic Δ9-THC and was significantly different from the effects obtained for that dose prior to chronic administration, F(1, 9) = 9.14, p = .014.
Although the data for the percentage of errors could not be compared statistically because responding was virtually eliminated in the acquisition components prior to chronic treatment at doses of 0.18 mg/kg and above, changes in this measure were evident after chronic THC. For example, response rates after chronic treatment at these doses exceeded five responses per minute, the cutoff for plotting the percentage of errors. This fact alone indicates a clear reduction in sensitivity to the disruptive effects of these doses of Δ9-THC after chronic treatment. With regard to the percentage of errors in the performance components, there were no significant differences in the acute effects of Δ9-THC before and after chronic administration of Δ9-THC; however, the number of subjects that were able to respond above five responses per minute at the 0.32 mg/kg dose after chronic Δ9-THC administration was greater than the number that were able to respond at this dose prior to chronic Δ9-THC administration (see numbers adjacent to symbols in figure 3).

Acute Effects of Δ9-THC on Behavioral Responding After SIV Inoculation
Figures 4 (acquisition component) and ​and55 (performance component) show the Δ9-THC dose-effect curves for response rate and percent errors in the vehicle-treated (left-hand panels) and Δ9-THC-treated (right-hand panels) subjects after SIV inoculation. In the infected and noninfected vehicle-treated groups, the Δ9-THC dose-effect curves overlapped substantially at the doses tested, indicating that the rate-decreasing and error-increasing effects of Δ9-THC did not change as a result of SIV inoculation. The similarity of the dose-effect curves before and after SIV inoculation was also evident in the ED50s for response rate. As shown in Table 2, the ED50s were not substantially altered by either vehicle administration or SIV inoculation.
The acute disruptive effects of Δ9-THC on rate and accuracy in both the acquisition and performance components in the infected and noninfected Δ9-THC-treated subjects (right-hand panels) also remained relatively consistent after SIV inoculation. In this case, however, the curves for the Δ9-THC-treated subjects were shifted to the right approximately tenfold, which indicated that tolerance had developed to the effects of all of the doses that were administered prior to chronic treatment. This tenfold shift was also readily apparent in the ED50s for both the infected and noninfected subjects (see Table 2). For example, in the acquisition component, the ED50 values for decreasing response rate prior to chronic Δ9-THC were 0.14 and 0.14 mg/kg, whereas after chronic Δ9-THC, the ED50 values were 1.21 and 1.34 mg/kg.
The shifts in the Δ9-THC dose-effect curves for the individual subjects in the two Δ9-THC-treated groups (infected and noninfected) are shown in Figures 6 and ​and7.7. As shown in these figures, the Δ9-THC dose-effect curves for all of the noninfected subjects (i.e., left-hand panels) were shifted rightward, which reflects the decreased sensitivity of these subjects to the rate-decreasing and occasional error-increasing effects of Δ9-THC in both behavioral components after treatment with the chronic dose (0.32 mg/kg). In the infected subjects (i.e., right-hand panels), although there were some differences in overall sensitivity, the Δ9-THC dose-effect curves were also shifted rightward approximately tenfold for each subject except subject RX; this subject's sensitivity to the rate-decreasing and error-increasing effects was similar in both the acquisition and performance components before and after chronic treatment with Δ9-THC.

Effects of Δ9-THC on Viral Load After SIV Inoculation
The three plots in Figure 8 show viral load in plasma (A), CSF (B), and brain (C) for the subjects inoculated with SIV in the vehicle- and Δ9-THC-treated groups, and verifies that all of the inoculated subjects were successfully infected with SIV, as each subject had measurable levels of viral RNA during the first 6 months of infection. However, there were no statistically significant differences in viral load between vehicle- and Δ9-THC-treated subjects in either plasma, F(1, 6) = 2.73, p > .05, or CSF, F(1, 6) = 1.87, p > .05, even though Δ9-THC-treated subjects tended to have lower geometric mean values for viral load in plasma and CSF throughout this period of infection. The only significant effect was the main effect of time for CSF viral load, F(6, 26) = 7.27, p < .001, which indicated that CSF viral load was higher at 2 weeks than it was at 2, 3, 4, or 6 months post infection (p < .05). There was no main effect of time for plasma, F(6, 34) = 1.38, p > .05, and no significant interactions between treatment and time for plasma, F(6, 34) = 0.913, p > .05, or CSF, F(6, 26) = 0.912, p > .05. Although several subjects had detectable viral loads in the brain at the time of necropsy, a two-way ANOVA indicated that there was no significant effect of treatment, F(1, 6) = 0.601, p > .05, or brain area, F(2, 12) = 0.52, p >.05, and no significant interaction of treatment and brain area, F(2, 12) = 1.44, p > .05.

Effects of Δ9-THC on CB-1 and CB-2 Receptor Protein Expression After SIV Inoculation
Western-blot analysis of the hippocampus in SIV-infected subjects showed that chronic Δ9-THC decreased CB-1 and CB-2 receptor levels in the hippocampus compared to vehicle administration (Figure 9A and Figure 9B). Furthermore, among the four SIV-infected subjects that were treated with Δ9-THC, there were a few notable differences across subjects and receptor subtypes. For example, CB-1 receptor levels were higher for subjects RX and MK than subjects AA and PO, whereas subject AA had higher receptor levels for CB-2 than CB-1 (Figure 9A). Overall, though, chronic Δ9-THC significantly decreased both CB-1 (p = .047) and CB-2 (p = .033) receptors in the SIV-infected subjects (Figure 9B).

Effects of Δ9-THC on Cytokine Expression After SIV Inoculation
Table 3 shows the effects of chronic vehicle or Δ9-THC on the expression of specific cytokines in the brain tissue of SIV-infected subjects. Despite an overall trend indicating that the brain tissue of SIV-infected subjects had lower expression levels of IFN, IL-1β, GM-CSF, IL-6, IL-8, MCP-1, and IL-18 after Δ9-THC administration than vehicle administration, the effect of chronic treatment was not significant, F(1, 6) = 2.24, p >.05. However, there was a main effect for the different levels of cytokine expression, F(8, 46) = 3.25, p =.005, and a significant interaction, F(8, 46) = 2.69, p = .016, indicating that the effect of chronic treatment depended on the cytokine. Subsequent Holm-Sidak post hoc tests then confirmed that there was a significant (p < .05) difference in the effect of chronic vehicle or Δ9-THC on the expression MCP-1 in SIV-infected subjects. Interestingly, irrespective of the specific cytokine, some of the highest individual levels of cytokine expression in the brain were observed in subjects that also had substantial CNS histopathology findings (e.g., subjects BA and TR).

Necropsy Findings
Two of the four SIV-infected subjects that received vehicle chronically had substantial CNS findings. In one subject (TR), there was acute necrotizing vasculitis of the medium-sized vasculature (Figure 10A), along with menin-goencephalitis characterized by a lymphocytic infiltrate of the cerebral parenchyma and meninges with mild adjacent cerebral edema (Figure 10B). In the other subject (BA), neuropathological findings were characterized by an acute neutrophilic meningoencephalitis with abundant Cytomegalovirus (CMV) inclusions and adjacent parenchymal necrosis (Figure 10C). By contrast, among the four SIV-infected monkeys that received Δ9-THC chronically, none demonstrated substantial CNS findings on routine histological analysis.
Also, opportunistic infection by Mycobacterium and/or Pneumocystis, confirmed by special stains, were identified in two of the vehicle-treated subjects (TR and MJ) as well as one of the Δ9-THC-treated subjects (MK). Collectively, Pneumocystis pneumonia occurred in three cases while Mycobacterium was confirmed in two cases, with evident diffuse dissemination in only one subject (MK).

The main findings from the present study were that 0.032— 0.32 mg/kg of Δ9-THC produced dose-dependent rate-decreasing effects and small, variable error-increasing effects in male rhesus monkeys responding under a complex behavioral procedure with acquisition and performance components. Given the variability of the error-increasing effects across doses and individual subjects in both components, these effects were not captured by plotting the group means for each dose. When 0.18 — 0.32 mg/kg of Δ9-THC or vehicle was then administered chronically over 28 days, tolerance to the disruptive effects of Δ9-THC developed in the Δ9-THC-treated group while there was little evidence of tolerance in the vehicle-treated group. For the Δ9-THC-treated group, this meant that the rate-decreasing effects in both the acquisition and performance components were largely eliminated and responding was not significantly different from saline administration; the effects in the vehicle-treated group were similar before and after chronic vehicle administration. More important, SIV inoculation did not change the respective sensitivity of either of these groups to Δ9-THC, nor did it affect the type of deficit produced by Δ9-THC administration under this behavioral procedure (i.e., rate-decreasing effects). A change in the type of deficit might have signaled an interaction in areas of the brain not previously suspected to play a role in the CNS effects of either Δ9-THC or SIV (e.g., see Weed et al., 2004). The single exception to the change in sensitivity was an SIV-inoculated subject that did not develop tolerance even though it was chronically administered Δ9-THC. Another important piece of data from the present study was that chronic Δ9-THC did not adversely affect viral load in the plasma, CSF, or brain tissue of SIV-inoculated subjects. Additionally, necropsy findings indicated that the incidence of significant neuropathology and of opportunistic infections was lower in SIV-infected subjects chronically treated with Δ9-THC than in SIV-infected subjects chronically treated with vehicle. Finally, SIV-infected subjects that were chronically treated with Δ9-THC had significantly lower expression of the inflammatory cytokine MCP-1 than SIV-infected subjects chronically treated with vehicle, with the highest individual cytokine levels occurring almost universally in the two subjects found to have notable CNS pathology.
Both i.v. and i.m. administration of increasing doses of Δ9-THC has been shown to disrupt responding under operant learning and performance procedures in monkeys (Evans & Wenger, 1992; Schulze et al., 1988; Winsauer et al., 1999), and it appears to do so in a manner similar to that observed in humans (Bickel et al., 1989; Kamien et al., 1994). For example, Kamien et al. (1994) found that Δ9-THC dose-dependently decreased response rate in eight healthy adult volunteers, but only increased errors marginally in the acquisition component of the task. These investigators also found that the time course for the effects of Δ9-THC on the percentage of errors during the acquisition component varied among subjects, with peak effects ranging from 90 to 300 min after drug ingestion. Similar to the present study, Schulze et al. (1988) reported that Δ9-THC (across a similar dose-range) produced dose-dependent rate-decreasing effects, yet had little or no effect on the mean percentage of correct responses, in nine male rhesus monkeys responding in an incremental repeated-acquisition task. However, neither of these studies nor the present study should be taken as an indication that Δ9-THC is incapable of disrupting accuracy in complex tasks; rather, these studies should be taken as indications of (a) the greater potency with which acute Δ9-THC disrupts response rate compared to accuracy in Δ9-THC-naïve subjects, and (b) the sensitivity of second-order FR schedules to the rate-decreasing effects of Δ9-THC. The sensitivity of the behavioral baseline to the rate-decreasing effects also produced marked decreases in the rate of reinforcement, which has been shown to directly contribute to the development of tolerance to Δ9-THC (Branch, Dearing, & Lee, 1980; Ferraro, 1978) and other psychoactive drugs (Schuster, Dockens, & Woods, 1966).
The historical purpose of using the repeated-acquisition technique in drugs studies was for studying the effects of drugs on the acquisition behavior of individual subjects. This was necessary because many of the earliest studies examining the effects of drugs on learning only compared groups of subjects (i.e., drug vs. nondrug controls), and this resulted in drug effects that were generally small in magnitude and of little practical significance (see Thompson & Moerschbaecher, 1979). The same could also be said for the mean effects of Δ9-THC on percent errors in this study, because the grouped data did not accurately reflect the variability in the data for this dependent measure. For example, an examination of the individual subject data in Figures 6 and ​and77 shows that the percentage of errors was quite high for some subjects and that the error rates after chronic treatment with Δ9-THC were reduced more in the performance components than the acquisition components.
In close agreement with previous studies conducted in monkeys (Gonzalez, Cebeira, & Fernandez-Ruiz, 2005) and humans (Jones, Benowitz, & Bachman, 1976), the present study also found that chronic administration of an effective dose of Δ9-THC (0.32 mg/kg) produced tolerance to the effects of the chronic dose in that a tenfold shift to the right in the dose-effect curves was obtained for response rate and the percentage of errors. The results from the present study also add to the small amount of existing data in the literature involving either monkeys or humans showing that tolerance develops to the disruptive effects of Δ9-THC on transitional behaviors or learning (see Thompson & Moerschbaecher, 1979). In nonhuman primates, for example, most of the studies examining the development of tolerance have used steady-state operant behaviors (e.g., Beardsley, Balster, & Harris, 1984; Branch et al., 1980; Elsmore, 1972; Ferraro & Grisham, 1972), and none, to our knowledge, have used a learning task. The same is true for the studies involving other animal species (e.g., Lamb, Jarbe, Makriyannis, Lin, & Goutopoulos, 2000; McMillan, Hardwick, & Wells, 1983), with only a few exceptions (Delatte et al., 2002; McMillan, 1988). A rodent study by Delatte et al. (2002), for example, characterized the development of tolerance to Δ9-THC in the acquisition and performance components of a behavioral task that was very similar to the one used in the present study. In humans, these types of prospective data are difficult to obtain because of a host of uncontrolled variables, including retrospective reporting of drug use; possible impurities or differences in potency of the Δ9-THC; poly drug abuse (particularly alcohol); and potential differences in intelligence, socioeconomic status, and nutrition.
Based on the data obtained from this study, there is little to suggest that SIV infection can alter the development of tolerance to the rate-decreasing or variable error-increasing effects of Δ9-THC in monkeys after it has been established or that Δ9-THC's disruptive effects on behavior are altered during SIV infection. Despite the absence of tolerance in one SIV-infected subject, the Δ9-THC dose-effect curves for both rate and accuracy in each component of the behavioral task did not shift after SIV infection in any of the other subjects in this group, and the magnitude of the shifts remained similar to those of the uninfected subjects that received chronic Δ9-THC (compare Figures 6 and ​and7).7). Such effects would also be consistent with the effects of marijuana use on cognitive function reported by Cristiani et al. (2004) in HIV-infected humans. In their study, marijuana use interacted significantly with responding on a battery of neuropsychological tests in HIV-infected individuals who were symptomatic, but it did not interact significantly with cognitive function in uninfected individuals or HIV-infected individuals who were asymptomatic. In the present study, three of the four SIV-infected subjects that received vehicle chronically reached the symptomatic stage of SIV infection prior to the subjects that received Δ9-THC chronically. Another reason to eliminate SIV infection as a reason for the absence of tolerance in the one Δ9-THC-treated subject is that this same subject (RX) did not show any tolerance after receiving the chronic dose for the 28-day period prior to infection and was consistently more sensitive to the disruptive effects of the initial chronic dose (0.18 mg/kg) than any of the other Δ9-THC-treated subjects. In addition, subject RX had the highest levels of CB-1 receptor expression in the hippocampus of this entire group (see Figure 9A), which would suggest an increased sensitivity for this subject compared to the other Δ9-THC-treated subjects in the group. In general, chronic Δ9-THC produced a significant reduction of CB-1 and CB-2 receptors, and this effect is similar to findings in both rats (Breivogel et al., 1999; Oviedo, Glowa, & Herkenham, 1993; Rodriguez, Gorriti, Fernandez-Ruiz, Palomo, & Ramos, 1994; Romero et al., 1998) and humans (Villares, 2007), showing that chronic Δ9-THC or other cannabinoid receptor agonists such as CP 55,940 reduced cannabinoid receptor levels. The fact that CB-2 receptors were significantly reduced in the hippocampus is intriguing because of the recent debate surrounding their relevance and function in the CNS (e.g., see Van Sickle et al., 2005). CB-2 receptors are mostly found in the periphery on structures such as the spleen, thymus, tonsils, bone marrow, and testes (for a review, see Howlett et al., 2002), and they are known to be integrally involved in immune function as they are found on immune cells (Cabral & Griffin-Thomas, 2009; Klein, Friedman, & Specter, 1998). Recently, however, CB-2 receptor mRNA has been detected in total RNA from cultured microglia isolated from the cerebral cortex of neonatal rat brain (Carlisle, Marciano-Cabral, Staab, Ludwick, & Cabral, 2002), but these receptors are not thought to have a functional role in the CNS (e.g., Yao et al., 2009). A limitation of our measures is the fact that expression was determined in tissue extracts that did not allow for the differentiation of receptors on the cell surface versus those that are localized intracellularly. Nevertheless, these data indicate the existence of both CB-1 and CB-2 receptors in the brain. If one or both of the receptors are mediating the cannabinoids behavioral and immunological effects, specific or nonspecific agonists could serve as potential pharmacotherapeutics. One of the particularly appealing aspects of CB-2 agonists as pharmacotherapeutics is that they might be able to produce therapeutic effects without producing the psychotropic CNS effects characteristic of CB-1 agonists or nonselective CB-1/CB-2 agonists (e.g., Chin et al., 2008).
Interestingly, the findings from the present study are the first in vivo experimental data to suggest that the cannabinoids such as Δ9-THC might have the capacity to lower plasma viral load in SIV-infected subjects. Although the effects on viral load were not significant up to 6 months after SIV inoculation, there was a downward trend during the later months postinfection, and these data would support existing in vitro data indicating that the synthetic cannabinoid WIN 55,212—2 can potently inhibit HIV-1 expression in a concentration and time-dependent manner in CD4+ lymphocytes and microglial cell cultures (Peterson, Gekker, Hu, Cabral, & Lokensgard, 2004). In a follow-up study, Rock et al. (2007) also established that CB-2 receptors may be integrally involved in WIN 55,212—2's antiviral activity as a selective CB-2 receptor antagonist, SR144528, blocked its antiviral effect in microglial cells; a similar antagonism was not obtained with the CB-1 receptor antagonist SR141716A.
Among the difficulties in ascertaining the impact of certain drugs on HIV-infected humans is that the drugs must often be tested in conjunction with therapeutic drugs such as protease inhibitors or nucleoside reverse transcriptase inhibitors (e.g., Bredt et al., 2002; Gorter, Seefried, & Volberding, 1992). In the present study, no other drugs were administered chronically with Δ9-THC; therefore, there seems to be fairly compelling evidence that Δ9-THC alone can have some persistent beneficial effects for SIV-infected subjects at the chronic dose tested even though they were clearly tolerant to the behaviorally disruptive effects of this dose. For example, in the current study, none of the chronic Δ9-THC-treated subjects had significant neuropathological findings at the time of necropsy compared to two of the four vehicle-treated subjects. Additionally, only one of the four Δ9-THC-treated subjects was found to harbor an opportunistic infection by histopathological analysis compared to three of four vehicle-treated subjects. The organisms identified were generally comparable to those described in human autopsies of AIDS patients. Specifically, in a large retrospective review of 565 adult AIDS autopsies, Klatt, Nichols, and Noguchi (1994) reported a 54% incidence of Pneumocystis carinii pneumonia, 32% incidence of Myocobacterium spp., and 7% incidence of CMV encephalitis. A similar pattern of infections was documented in the current primate study, with Pneumocystis being the most commonly observed opportunist (3 of 8, 37%), followed then by Mycobacterium (2 of 8, 25%), and, finally, CMV encephalitis (1 of 8, 12.5%).
The relative absence of deleterious effects in SIV-inoculated subjects after chronic Δ9-THC would also seem to set cannabinoids apart from other drugs of abuse such as alcohol, which has clearly been shown to increase viral replication (Poonia et al., 2006) and decrease the life span of SIV-infected monkeys (Bagby, Zhang, Purcell, Didier, & Nelson, 2006). Opiates have also been shown to increase viral replication and increase the rate of SIV disease, although there is some contradictory evidence suggesting a protective effect (for review, see Noel, Rivera-Amill, Buch, & Kumar, 2008). Lastly, in vitro studies with CNS stimulants such methamphetamine (Liang et al., 2008) and cocaine (Peterson et al., 1993) have indicated that these drugs can enhance infection of macrophages by HIV-1; however, these data have not been extended to in vivo experimental data involving SIV. Data from the present study also contrast with reports suggesting that the cannabinoids can decrease immunity. For example, Zhu et al. (2000) found that Δ9-THC can inhibit antitumor immunity by a CB-2 receptor-mediated, cytokine-dependent pathway in immunocompetent mice. This effect was hypothesized to have been the result of Δ9-THC-induced increases in IL-1 and tumor growth factor beta (TGF-β) and decreases in IL-2 and IFN-γ release. These data were also consistent with data from Newton, Klein, and Friedman (1994) showing that Δ9-THC can alter the development of cell-mediated immunity to primary infection from agents such as L. pneumophila, which depends on the production of type-1 cytokines such as IL-2 and IFN-γ. Together, results such as these have led to the hypothesis that Δ9-THC generally decreases type-1 cytokines and increases type-2 cytokines to produce its overall immunosuppressive effects (Klein et al., 1998). This hypothesis does not fit the cytokine data obtained from brain in the present study, however. In the present study, with the exception of IL-12/23 and IL-4, Δ9-THC-treated subjects tended to have lower levels of the cytokines measured than vehicle-treated subjects, and this includes both type-1 (e.g., IFN-γ) and type-2 (e.g., IL-6) cytokines. Whether these changes in cytokine expression reflect an important immunomodulatory effect of the cannabinoids on SIV remains to be investigated.
In summary, chronic administration of Δ9-THC produced tolerance to its rate-decreasing effects in both the learning and performance components of a complex behavioral task when compared to chronic administration of vehicle. Following SIV inoculation, the disruptive effects of Δ9-THC were similar to those obtained prior to SIV inoculation in both the vehicle- and Δ9-THC-treated groups. These data support the notion that there may be little interaction between the disruptive effects of Δ9-THC and SIV infection, or the disease state created by SIV infection, during the early stages of infection. Though the current study is limited by small numbers, the notable difference in neuropathology, and in opportunistic infection rates, between vehicle- and Δ9-THC-treated SIV-infected subjects warrants further investigation. Finally, chronic Δ9-THC did not adversely impact viral load in plasma, CSF, or the brain during the early stages of infection, or increase the levels of inflammatory cytokines, but it did decrease CB-1 and CB-2 levels in the hippocampus.

Source with Charts, Graphs and Links: ncbi.nlm.nih.gov
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