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Tolerance to Chronic Delta-9-Tetrahydrocannabinol (Δ9-THC) inRhesus Macaques Infected With Simian Immunodeficiency Virus

Peter J. Winsauer,Department of Pharmacology, and the Alcohol and Drug Abuse Research Center, LSU HealthSciences Center, New Orleans, Louisiana

Patricia E. Molina,Department of Physiology, and the Alcohol and Drug Abuse Research Center, LSU HealthSciences Center

Angela M. Amedee,Department of Microbiology Immunology and Parasitology, LSU Health Sciences Center

Catalin M. Filipeanu,Department of Pharmacology, LSU Health Sciences Center

Robin R. McGoey,Department of Pathology, LSU Health Sciences Center

Dana A. Troxclair,Department of Pathology, LSU Health Sciences Center

Edith M. Walker,Department of Physiology, LSU Health Sciences Center

Leslie L. Birke,Department of Physiology, LSU Health Sciences Center

Curtis Vande Stouwe,Department of Physiology, LSU Health Sciences Center

Jessica M. Howard,Department of Physiology, LSU Health Sciences Center

Stuart T. Leonard,Department of Pharmacology, LSU Health Sciences Center

Joseph M. Moerschbaecher, andDepartment of Pharmacology, LSU Health Sciences Center

Peter B. LewisDepartment of Pharmacology, LSU Health Sciences Center

AbstractAlthough Δ9-THC has been approved to treat anorexia and weight loss associated with AIDS, itmay also reduce well-being by disrupting complex behavioral processes or enhancing HIVreplication. To investigate these possibilities, four groups of male rhesus macaques were trained torespond under an operant acquisition and performance procedure, and administered vehicle or Δ9-

© 2011 American Psychological AssociationCorrespondence concerning this article should be addressed to Peter J. Winsauer, Department of Pharmacology and ExperimentalTherapeutics, LSU Health Sciences Center, 1901 Perdido Street, New Orleans, LA 70112. [email protected].

NIH Public AccessAuthor ManuscriptExp Clin Psychopharmacol. Author manuscript; available in PMC 2011 July 21.

Published in final edited form as:Exp Clin Psychopharmacol. 2011 April ; 19(2): 154–172. doi:10.1037/a0023000.

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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 produceddose-dependent rate-decreasing effects and small, sporadic error-increasing effects in theacquisition 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 therate-decreasing effects, and this tolerance was maintained during the initial 7–12 monthsirrespective of SIV infection (i.e., +THC/−SIV, +THC/+SIV). Full necropsy was performed on allSIV subjects an average of 329 days post-SIV inoculation, with postmortem histopathologysuggestive 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 levelsin the hippocampus, attenuated the expression of a proinflammatory cytokine (MCP-1), and didnot increase viral load in plasma, cerebrospinal fluid, or brain tissue compared to vehicle-treatedsubjects with SIV. Together, these data indicate that chronic Δ9-THC produces tolerance to itsbehaviorally disruptive effects on complex tasks while not adversely affecting viral load or othermarkers of disease progression during the early stages of infection.

Keywordsdelta-9-tetrahydrocannabinol; simian immunodeficiency virus; tolerance; operant behavior; rhesusmonkey

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 andDrug 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 ofscientific 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 andhow 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 neuronaland cognitive dysfunction is not fully understood nor has it been fully investigated. Inindividuals diagnosed with an “AIDS-defining” illness, a wide variety of studies havehelped characterize the extent to which neuropsychological performance changes followinginfection (Grant, Heaton, & Atkinson, 1995; Navia, Jordan, & Price, 1986; Newman, Lunn,& Harrison, 1995; Perdices & Cooper, 1990; Selnes et al., 1995). From these studies, aclinical triad has been identified that includes cognitive impairment, motor dysfunction, andbehavioral abnormalities (Power & Johnson, 1995). These progressive signs of decline havealso been posited to represent subcortical neuronal damage that likely result fromneurological 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 bealtered by neurodegenerative diseases are changes in motor speed, attention, verbalbehavior, nonverbal behavior, acquisition or learning, and memory. According to Grant etal. (1995), for example, HIV-1-associated cognitive impairments generally involve at leasttwo ability domains (e.g., memory and attention), but impairment of multiple domains iscommon.

In contrast to HIV or SIV, chronic Δ9-THC has not been shown to produce markedneuropathological alterations that lead to neuropsychological manifestations, and has notbeen as well studied in animals or man (Carlini, 2004; Landfield, Cadwallader, & Vinsant,1988). Moreover, many of the existing studies in humans suffer from experimental

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limitations such as a failure to control for the redistribution of accumulated Δ9-THC from fatstores (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 areaffected by immunodeficiency viruses. For example, Δ9-THC is known to affect reactiontime (Hunault et al., 2009; Wilson, Ellinwood, Mathew, & Johnson, 1994) and workingmemory (Hampson & Deadwyler, 1999; Lichtman, Dimen, & Martin, 1995), which isthought to involve subcortical structures and the hippocampus, respectively. In addition,there is the possibility that the disruptive effects of Δ9-THC on neurobehavioral processesmight be greater in the presence of an immunodeficiency virus (IV), just as the disruptiveeffects of alcohol on neurobehavioral processes have been shown to be greater in thepresence of HIV in humans (Green, Saveanu, & Bornstein, 2004; Rothlind et al., 2005) andof SIV in monkeys (Winsauer, Moerschbaecher, et al., 2002).

One of the most important attributes of the SIV-monkey model is that some of the samebehavioral and neuropathological sequelae that are observed in the human neuroAIDSsyndrome 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 andmonkeys has been that motor-skill deficits occurred earlier and more frequently thancognitive 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 betweenHIV-infected humans and SIV-infected monkeys have not been this straightforward, the vastmajority of findings in monkeys have strong parallels with the findings in humans (seeCheney, Riazi, & Marcario, 2008, or Fox, Gold, Henriksen, & Bloom, 1997). In fact, studiesinvolving rhesus monkeys have established the SIV model as an important tool for directlyinvestigating cognitive and motor deficits produced by infection and have allowedinvestigators to eliminate many of the variables that confound human studies, such as theeffects of antiviral therapy, education level, age, and socioeconomic status.

The purpose of the present study was to examine the interaction of chronic Δ9-THC withSIV 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 tomeasure any occurrence of behavioral impairments as part of a longitudinal experimentaldesign to examine the effect of Δ9-THC and SIV on host defense. Therefore, assessment ofan animal’s ability to acquire new information (an ability that has been shown to be quitesensitive to disruption by both HIV and Δ9-THC) might help elucidate deficits produced byboth types of insult and also establish an interaction between the two. The specific techniquechosen for measuring learning in this study is commonly referred to as “repeatedacquisition” among behavioral scientists, and it was first described by Boren (1963) as ameans for studying the effects of drugs on learning. Since that time, this technique has beenwidely used in the characterization of drug effects on the acquisition, performance, andretention of complex discriminations in both experimental animals and humans (Thompson& Moerschbaecher, 1979). Identifying preclinical research techniques that can also be usedwith humans is a significant challenge, particularly for relatively complex cognitive abilitiesthat require learning or memory, because few techniques are capable of repeatedly assayingthe 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 learningbecause 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

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behavior (Cohn, Ziriax, Cox, & Cory-Slechta, 1992), (c) sensitive to numerous drugs anddrug 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 inanimals 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 eachsubject’s health status. As SIV infection progressed and subjects met specified criteria foreuthanasia, they were sacrificed, underwent necropsy, and tissues were banked for furtheranalysis. Disease progression in the brain was examined histopathologically and by analysesof viral load and the expression of specific inflammatory cytokines. Cannabinoid receptorexpression in the hippocampus was also determined by Western blot. Hippocampalexpression of cannabinoid receptors was conducted because this brain area is known to beintegrally involved in working memory (Hampson & Deadwyler, 1999, 2000) and has beenshown to be affected by both acute (e.g., Lichtman et al., 1995) and chronic cannabinoidadministration (e.g., Coutts et al., 2001; Landfield et al., 1988).

MethodSubjects

Fifteen domestically bred 4- to 5-year-old, experimentally naïve male rhesus monkeys ofIndian 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. Thesesubjects were housed individually in aluminum cages (BREC, Inc., Bryan, TX) in a roommaintained 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 theirfree-feeding weights on a diet consisting of banana-flavored food pellets (Purina MillsTestDiet, Richmond, IN), standard primate chow (Formula 2050, Harlan Teklad, Madison,WI), fruit, and vitamins. Body weights of the individual subjects in each treatment group areshown in Table 1, and these values are consistent with normative growth curves for rhesusmacaques (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 adlibitum except during behavioral testing.

For behavioral testing, subjects were tested serially in subgroups of four monkeysthroughout the day due to the small number of response panels. This resulted in the subjectsbeing 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 hoursafter the morning injection; however, the various treatments were pseudorandomlydistributed throughout the subgroups to control for the time of day and time after the chronicinjection. Prior to testing, each subject was removed from the colony-room cage andtransported via a macaque restrainer (Primate Products, Inc., Redwood City, CA) toexperimental test cages located in another room. These studies were carried out inaccordance with the Institutional Care and Use Committee of the Louisiana State UniversityHealth Sciences Center and the guidelines of the Committee on Care and Use of LaboratoryAnimal Resources, as adopted and promulgated by the U.S. National Institutes of Health.

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ApparatusSimilar to that portrayed in Thompson and Moerschbaecher (1978), removable responsepanels were attached to the side of each of four experimental test cages. Each aluminumresponse panel contained three translucent response keys aligned horizontally with stimulusprojectors mounted behind them. The in-line stimulus projectors projected colors andgeometric forms onto the key. In addition to these stimuli, there was a single, tricoloredstimulus located above the center stimulus key and a single response key that could beilluminated 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 intothe aperture. Response keys required a minimum force of 0.15 N for activation, and eachcorrect response on the keys produced an audible click of a feedback relay. Recessedfluorescent lights in the ceiling of the room provided overhead lighting. All fourexperimental 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 ProcedureResponding by subjects was stabilized under a multiple-schedule procedure comprised ofthree components: timeout, repeated-acquisition (acquisition), and performance. Thepresentation 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 behavioralresponding was also stabilized before the respective treatments with Δ9-THC and/or SIVcommenced. This was critical not only for ensuring that the behaviors established werestable, but also for establishing a baseline of responding for acquisition and performancebehavior for each animal and each group of animals prior to any treatments. During theacquisition component, all three response keys were illuminated simultaneously with one offive 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 correctkey in the presence of each sequentially illuminated set of geometric symbols (e.g., keyswith 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). Whenthe response sequence was completed, the key lights were turned off and the response keyover 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 thenmaintained 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 apress on the key located over the food pellet aperture. When the subject pressed an incorrectkey (in the example, the left or right key when the square symbols were presented), theincorrect response (error) was followed by a 5-s timeout. During timeouts, the key lightswere off and responses had no programmed consequence. An incorrect response did notreset 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 thiscomponent was changed from cycle to cycle. An example of a typical set of six sequenceswas as follows: LRCRC, CLRLR, LRLCL, RCRLC, CLCRL, RCLCR, with the order of thegeometric symbols always squares, horizontal bars, triangles, vertical bars, and circles. Thesequences were carefully selected to be equivalent in several ways and there wererestrictions on their ordering across sessions (for restrictions, see Winsauer,Moerschbaecher, et al., 2002).

During the performance components of the multiple schedule, the geometric symbols thatidentified each response in the five-response sequence were projected on a greenbackground. The green background on each key served as discriminative stimuli for this

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component. Unlike the acquisition component, the five-response sequence in theperformance 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.), theperformance component was identical to the acquisition component.

All experimental sessions began with a timeout component, which was followedimmediately by an acquisition component and a performance component. On days in whichno injections were administered (baseline days), timeout components were 10 min induration, repeated-acquisition components were 15 min in duration and performancecomponents were 5 min in duration. Due to the relatively long onset period for the effects ofΔ9-THC, on days in which Δ9-THC was administered, timeout components were 30 min induration, whereas the durations for the acquisition and performance components did notchange. The principal dependent measures for both the acquisition and performancecomponents were response rate and the percentage of errors. Responding for each subjectwas considered stable when overall response rates in the acquisition and performancecomponents did not differ from the respective mean rate by ± 20% and the percentage oferrors 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 characterizedby a steady state in terms of within-session error reduction, which was determined by visualinspection of the data and indicated by a distinct decrease in the number of errors and aconcomitant increase in errorless completions of the response sequence.

The overall timeline for the experimental protocol, including inoculation, is depicted inFigure 1. Vehicle and delta-9-THC administration occurred twice daily (Monday throughSunday), whereas behavioral testing occurred over five consecutive days (Monday throughFriday). When blood work and physicals were scheduled, behavioral testing was suspendedfor that day. Behavioral testing and food restriction were also suspended every 3 months for5 days when the subjects were placed in metabolic cages to assess a range of physiologicalmeasures 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 AdministrationFollowing 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-THCin all of the subjects. The acute effects of Δ9-THC were also established in both vehicle- andΔ9-THC-treated subjects throughout the study for the purposes of continuously assessingany within- or between-groups changes in response to drug. The use of a cumulative-dosingprocedure was particularly important because it allowed for the determination of an entiredose-effect curve in a single session, which limited the dose-to-dose variability that mightresult from the progression of the disease if a more traditional acute-dosing procedure wasused. Essentially, because a given experimental session was comprised of two to five cyclesof the multiple schedule, increasing dosages of drugs could be administered prior to the startof each timeout component to obtain the entire dose-effect curve. In general, the dose-effectcurve ranged from one that produced little or no effect to one that substantially reducedresponding. All of the doses were given intramuscularly (i.m.) in the gluteus muscles, andsuccessive 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/kgbefore the second cycle, 0.044 mg/kg before the third cycle, and 0.08 before the fourthcycle, 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 thefirst 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

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(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 ofΔ9-THC were also determined periodically between SIV inoculation and euthanasia (seecriteria in Post-SIV Necropsy section) to assess the effect of this treatment on the magnitudeand duration of tolerance development. Therefore, the number of determinations thatcomprised an individual subject’s dose-effect data varied from subject to subject. If thecumulative dose-effect curves for Δ9-THC were determined in the morning, the chronic doseadministered in the morning was replaced with vehicle. If the cumulative dose-effect curveswere determined in the afternoon, the chronic dose administered in the evening was omitted.

Chronic Δ9-THC AdministrationAfter determining dose-effect curves for Δ9-THC in all of the subjects, they were dividedinto two groups based on their baseline of behavior and response to infectivity.Comparability of baseline behavior was determined by a comparison of each subject’sresponse rate and percentage of errors under the multiple schedule, whereas comparability ofinfectivity 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. twicedaily (8:30 a.m. and 5:30 p.m.) for 28 days. Chronic administration of Δ9-THC was initiatedwith 0.18 mg/kg and then increased to 0.32 mg/kg, the dose administered for the rest of thestudy; 0.18 mg/kg was the initial dose because this was a significantly effective dose for theentire group of subjects prior to chronic treatment. The increase in dose to 0.32 mg/kg wasindividualized for every subject, but it was increased for almost all of the subjects afterapproximately two weeks when responding was no longer affected by 0.18 mg/kg on a dailybasis (i.e., tolerance developed). This was determined simply by administering the chronicdose 30 min prior to the respective behavioral session for each subgroup and noting theamount of responding that occurred by visual inspection. For one subject (RX), the chronicdose 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 PCRAfter 28 days of Δ9-THC or vehicle administration, and a redetermination of the acuteeffects of Δ9-THC, 4 subjects from each chronically treated group were anesthetized withketamine hydrochloride (10 mg/kg, i.m.) and inoculated intravenously with SIVmac251.Subjects were inoculated with 100 times the 50% tissue culture infective dose (TCID50) ofSIVmac251 via the saphenous vein using a 23-gauge catheter. Following inoculation, samplesof both plasma and CSF were collected at 2 weeks postinoculation and then monthlythereafter, as indicated in Figure 1. Viral load in brain tissue was determined in samplescollected from frozen brain (see below).

Viral levels in plasma, CSF, and brain tissue were determined by a quantitative real-timereverse-transcriptase PCR assay (qRT-PCR) that targets a highly conserved region of theSIV genome located within the structural gene gag. Virus particles were isolated from 1 mlplasma or 200 μl CSF by centrifugation at 20,000 g for 60 min, and viral RNA were purifiedwith Trizol reagent (Invitrogen, part of Life Technologies, Carlsbad, CA), according tomanufacturer’s directions. For the isolation of RNA from brain tissues, flash frozen samplesof tissue were placed in RNA-later-ICE solution (Ambion Inc., Austin, TX) overnight andthen homogenized in Trizol reagent. One tenth and one fourth of the total RNA isolatedfrom plasma and CSF samples were added to duplicate qRT-PCR reactions, respectively,whereas approximately 100 ng of RNA prepared from brain tissues were assayed induplicate 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 ofSIV-gag specific forward and reverse primers and 150 nM of FAM-labeled probe. The SIV

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Primers were as follows: Forward, 5′ GCGTCATTTGGTGCATTCAC-3′; Reverse, 5′ TC-CACCACTAGATGTCTCTGCACTAT-3′; and Probe, 5′ 6FAM-TGTTTGCTTCCTCAGTATGTTTCACTTTCTCTT-CTG-TAMRA. Fluorescence wasmeasured with an ABI 7300 RT-PCR detection system (Applied Biosystems) in each of 40cycles, and the threshold cycle (CT) at which fluorescence was greater than background wasdetermined. SIV RNA copy number in each sample was determined by extrapolation from astandard curve generated from serial dilutions of a SIV RNA control template produced invitro, which includes the gag sequences targeted by the primers. Cell-associated viral RNAlevels were normalized by the simultaneous amplification of rhesus GAPDH mRNA levels(Applied Biosystems), expressed as SIV-RNA copies per μg mRNA. The assay has asensitivity 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 ofCSF, and approximately 50 copies SIV/μg mRNA in these studies.

Post-SIV NecropsySubjects meeting at least two of the criteria for euthanasia were necropsied by one of twoautopsy pathologist prosectors. The specific criteria for euthanasia were (a) loss of 25% ofbody weight from the maximum body weight since assignment to the protocol; (b) decreasedserum albumin to less than 3 mg/dl associated with edema; (c) anemia andthrombocytopenia; (d) three days of complete anorexia; or (e) major organ failure or medicalconditions unresponsive to treatment such as respiratory distress, intractable diarrhea, orpersistent vomiting. Evisceration and tissue collection was performed according tostandardized collection procedures for each organ and tissue of interest. Representativepieces from selected tissues were fixed in 10% buffered formalin and subsequently paraffinembedded, sectioned and stained with hematoxylin and eosin. When indicated, Gomorimethanamine silver stain (GMS) was utilized for confirmation of Pneumocystis organismsand Fite’s acid stain was utilized for confirmation of Mycobacterium. For neuropathologicalexamination, the scalp was incised coronally and then reflected to reveal the outercalvarium. A Stryker autopsy saw (Stryker Instruments, Kalamazoo, MI) was used tocircumferentially remove the superior calvarium and its underlying dura mater. The brainwas removed by incision of the tentorium and any adherent cranial nerves. The brain wasthen weighed in its fresh state and the most distal tip of one temporal lobe was retained in10% buffered formalin for tissue fixation. The remainder of the brain was then flash frozenin liquid nitrogen and stored for subsequent analyses of cannabinoid (CB) receptors andcytokine expression. Histopathological analysis on all tissues of interest was performedblindly by both participating autopsy pathologists.

Expression of CB-1 and CB-2 Receptors by Western Blot AnalysisAfter all of the subjects were sacrificed, analysis of CB-1 and CB-2 receptor protein levelsin the hippocampus was conducted as described previously (Winsauer et al., 2011). Forthese analyses, 200 μg of hippocampal tissue was resuspended in a lysis buffer (20 mM TrispH 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 usingMicroRotofor Lysis Kit (Bio-Rad Laboratories, Inc., Hercules, CA) following themanufacturer’s protocol. The protein concentration was determined using the BradfordMethod (Bradford, 1976) and diluted to 1 μg/μl with 1×SDS buffer. Equal amounts ofprotein from each area (50 μg) were separated by sodium dodecyl sulfate-polyacrylamidegel electrophoresis (SDS-PAGE 12%) and transferred to nitrocellulose PDVF membranes(Bio-Rad Laboratories, Inc.). The membranes were subsequently immunoblotted for 2 hr atroom temperature with specific antibodies: a rabbit anti-CB-1 receptor (1:500 dilution) fromEnzo Life Sciences (Plymouth Meeting, PA), a rabbit anti-CB-2 receptor (1:500) fromCayman Chemical (Ann Arbor, MI), and a mouse anti-β-actin (1:2000 dilution) from Santa

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Cruz Biotechnology (Santa Cruz, CA). These antibodies were then followed by specificsecondary antibodies (1:2000 for 90 min) and visualized using ECL Plus (PerkinElmer LifeSciences, Waltham, MA) and a Fuji Film luminescent image analyzer (LAS-1000 Plus, FujiPhoto Film Co. LTD., Tokyo). The identity of the CB1R and CB2R bands were confirmedby their disappearance in samples exposed to preadsorption of the antibody with the controlantibody. 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-2receptor expression was normalized to β-actin values.

Brain Cytokine Content Determination by Luminex-Based Multiplex ImmunoassayTissue 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), prefrontalcortex (PFC), and orbital frontal cortex (OFC). LC was chosen for its density of cannabinoidreceptors (Herkenham et al., 1991). PFC was chosen for its involvement in complexcognitive processes (Silva de Melo et al., 2005), and OFC for its involvement in drug abuseand the inhibitory control of behavior (Goldstein et al., 2007). Luminex immunoassays werecarried out using the Bio-PlexTM Protein Array System (Bio-Rad Laboratories, Inc.)according to the manufacturer’s recommended protocols. Because the levels of cytokines ineach of the three brain areas were very similar, only an average for the three areas ispresented for each vehicle- and Δ9-THC-treated subject.

Data AnalysesThe behavioral data for each session were analyzed in terms of overall response rate (totalresponses/min, excluding timeouts) and overall accuracy, expressed as the percentage oferrors [(incorrect responses/correct + incorrect responses) × 100]. These data were groupedand analyzed statistically depending on the particular treatment period. For example, prior tochronic Δ9-THC, the data for response rate and the percentage of errors were analyzed usinga one-way repeated measures ANOVA (SigmaStat Statistical Software, SPSS, Inc.), withdose serving as the repeated measure. Holm-Sidak tests were then used to make post hoccomparisons to determine Δ9-THC doses that were significantly different from acute salineadministration. To compare the effects of Δ9-THC before and after the 28 days of chronicΔ9-THC or vehicle administration, a two-way repeated measures ANOVA (general linearmodel) 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 interactionsdetermined by a two-way ANOVA were also followed by one-way ANOVA tests andHolm-Sidak post hoc tests to compare the effects of the different doses of Δ9-THC fromsaline administration (control) and to compare the effects of individual doses before andafter chronic Δ9-THC administration. To compare viral load data between chronic treatmentgroups, 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 weretreatment and brain area. A two-way repeated measures ANOVA was also conducted toassess 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 ofcytokine served as a repeated measure. The quantitative analysis of the Western blots wasconducted separately for each receptor subtype and consisted of a one-tailed t test thatcompared Δ9-THC-treated and vehicle-treated values. The analysis was a one-tailed test dueto the assumption that chronic Δ9-THC would only decrease cannabinoid receptorexpression.

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 bycomparing the ED50 values of the dose-effect curves depicted in Figures 4 and 5. These

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ED50 values were determined by linear regression using two or more data points reflectingthe slopes of the descending curve for response rate or the ascending curve for thepercentage of errors. For response rate, the ED50 represented the estimated dose of Δ9-THCthat decreased responding from control levels by 50%. For the percentage of errors, theED50 represented the estimated dose of Δ9-THC that increased the percentage of errorsfrom control levels by 50% (i.e., ED150).

ResultsAcute Effects of Δ9-THC on Behavioral Responding

Figure 2 shows that prior to chronic treatment or SIV inoculation, cumulative doses of0.032–0.32 mg/kg of Δ9-THC produced dose-dependent rate-decreasing effects in theacquisition, 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 ofsaline. Post hoc tests also revealed that the 0.18-mg/kg dose of Δ9-THC significantlydecreased responding in both behavioral components of the task when compared to salineadministration. In contrast to the effects on response rate, the same doses of Δ9-THC hadlittle 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 thateliminated responding.

Acute Effects of Δ9-THC on Behavioral Responding After 28 Days of Chronic Δ9-THCAdministration

Figure 3 shows the acute disruptive effects of Δ9-THC on the rate and accuracy ofresponding after 28 days of either vehicle or Δ9-THC (0.18 – 0.32) administration. In thevehicle-treated group, the acute disruptive effects of Δ9-THC on rate and accuracy in theacquisition and performance components were similar before and after chronicadministration. More specifically, for the three doses of Δ9-THC tested, there were nosignificant main effects of chronic treatment and no significant interactions between chronictreatment and the dosage in either behavioral component. The only significant effect in thisgroup 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 reducedresponse rate compared to saline administration.

In the Δ9-THC-treated group, the acute disruptive effects on rate and accuracy in theacquisition and performance components were substantially different from those obtainedprior to chronic administration, particularly for response rate, where there was a significantmain 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 performancecomponents, F(2, 8) = 16.70, p < .001. Both of these results from the two-way ANOVAtests are indications of the large differences between the disruptive effects of Δ9-THC beforeand after chronic Δ9-THC administration. For example, prior to chronic Δ9-THCadministration, 0.18 mg/kg decreased responding in the acquisition component from 17.4responses per minute under control conditions (acute saline administration) to 2.7 responsesper minute (an 85% reduction). After chronic Δ9-THC administration, a larger dose, 0.32mg/kg, had little or no effect on response rate or errors in this component compared to therespective postchronic control data. Similarly, responding in the performance componentwas substantially reduced by 0.18 and 0.32 mg/kg of Δ9-THC prior to chronic Δ9-THCadministration as compared to saline administration, F(3, 18) = 23.44, p < .001, whereas0.32 mg/kg had no effect on response rate after 28 days of chronic Δ9-THC and wassignificantly different from the effects obtained for that dose prior to chronic administration,F(1, 9) = 9.14, p = .014.

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Although the data for the percentage of errors could not be compared statistically becauseresponding was virtually eliminated in the acquisition components prior to chronic treatmentat 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 responsesper minute, the cutoff for plotting the percentage of errors. This fact alone indicates a clearreduction in sensitivity to the disruptive effects of these doses of Δ9-THC after chronictreatment. With regard to the percentage of errors in the performance components, therewere no significant differences in the acute effects of Δ9-THC before and after chronicadministration of Δ9-THC; however, the number of subjects that were able to respond abovefive responses per minute at the 0.32 mg/kg dose after chronic Δ9-THC administration wasgreater than the number that were able to respond at this dose prior to chronic Δ9-THCadministration (see numbers adjacent to symbols in figure 3).

Acute Effects of Δ9-THC on Behavioral Responding After SIV InoculationFigures 4 (acquisition component) and 5 (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 andnoninfected vehicle-treated groups, the Δ9-THC dose-effect curves overlapped substantiallyat 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 curvesbefore and after SIV inoculation was also evident in the ED50s for response rate. As shownin Table 2, the ED50s were not substantially altered by either vehicle administration or SIVinoculation.

The acute disruptive effects of Δ9-THC on rate and accuracy in both the acquisition andperformance 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 wereadministered prior to chronic treatment. This tenfold shift was also readily apparent in theED50s for both the infected and noninfected subjects (see Table 2). For example, in theacquisition 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 1.21and 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 7. As shown in thesefigures, the Δ9-THC dose-effect curves for all of the noninfected subjects (i.e., left-handpanels) were shifted rightward, which reflects the decreased sensitivity of these subjects tothe rate-decreasing and occasional error-increasing effects of Δ9-THC in both behavioralcomponents 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-THCdose-effect curves were also shifted rightward approximately tenfold for each subject exceptsubject RX; this subject’s sensitivity to the rate-decreasing and error-increasing effects wassimilar in both the acquisition and performance components before and after chronictreatment with Δ9-THC.

Effects of Δ9-THC on Viral Load After SIV InoculationThe three plots in Figure 8 show viral load in plasma (A), CSF (B), and brain (C) for thesubjects inoculated with SIV in the vehicle- and Δ9-THC-treated groups, and verifies that allof the inoculated subjects were successfully infected with SIV, as each subject hadmeasurable levels of viral RNA during the first 6 months of infection. However, there were

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no statistically significant differences in viral load between vehicle- and Δ9-THC-treatedsubjects in either plasma, F(1, 6) = 2.73, p > .05, or CSF, F(1, 6) = 1.87, p > .05, eventhough Δ9-THC-treated subjects tended to have lower geometric mean values for viral loadin plasma and CSF throughout this period of infection. The only significant effect was themain effect of time for CSF viral load, F(6, 26) = 7.27, p < .001, which indicated that CSFviral 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 significantinteractions 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 thetime of necropsy, a two-way ANOVA indicated that there was no significant effect oftreatment, F(1, 6) = 0.601, p > .05, or brain area, F(2, 12) = 0.52, p >.05, and no significantinteraction 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 InoculationWestern-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 vehicleadministration (Figure 9A and Figure 9B). Furthermore, among the four SIV-infectedsubjects that were treated with Δ9-THC, there were a few notable differences across subjectsand receptor subtypes. For example, CB-1 receptor levels were higher for subjects RX andMK than subjects AA and PO, whereas subject AA had higher receptor levels for CB-2 thanCB-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 InoculationTable 3 shows the effects of chronic vehicle or Δ9-THC on the expression of specificcytokines in the brain tissue of SIV-infected subjects. Despite an overall trend indicatingthat 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 vehicleadministration, 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 theeffect of chronic treatment depended on the cytokine. Subsequent Holm-Sidak post hoc teststhen confirmed that there was a significant (p < .05) difference in the effect of chronicvehicle 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 cytokineexpression in the brain were observed in subjects that also had substantial CNShistopathology findings (e.g., subjects BA and TR).

Necropsy FindingsTwo of the four SIV-infected subjects that received vehicle chronically had substantial CNSfindings. In one subject (TR), there was acute necrotizing vasculitis of the medium-sizedvasculature (Figure 10A), along with menin-goencephalitis characterized by a lymphocyticinfiltrate of the cerebral parenchyma and meninges with mild adjacent cerebral edema(Figure 10B). In the other subject (BA), neuropathological findings were characterized by anacute neutrophilic meningoencephalitis with abundant Cytomegalovirus (CMV) inclusionsand adjacent parenchymal necrosis (Figure 10C). By contrast, among the four SIV-infectedmonkeys that received Δ9-THC chronically, none demonstrated substantial CNS findings onroutine histological analysis.

Also, opportunistic infection by Mycobacterium and/or Pneumocystis, confirmed by specialstains, were identified in two of the vehicle-treated subjects (TR and MJ) as well as one ofthe Δ9-THC-treated subjects (MK). Collectively, Pneumocystis pneumonia occurred in three

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cases while Mycobacterium was confirmed in two cases, with evident diffuse disseminationin only one subject (MK).

DiscussionThe main findings from the present study were that 0.032– 0.32 mg/kg of Δ9-THC produceddose-dependent rate-decreasing effects and small, variable error-increasing effects in malerhesus monkeys responding under a complex behavioral procedure with acquisition andperformance components. Given the variability of the error-increasing effects across dosesand individual subjects in both components, these effects were not captured by plotting thegroup means for each dose. When 0.18 – 0.32 mg/kg of Δ9-THC or vehicle was thenadministered chronically over 28 days, tolerance to the disruptive effects of Δ9-THCdeveloped in the Δ9-THC-treated group while there was little evidence of tolerance in thevehicle-treated group. For the Δ9-THC-treated group, this meant that the rate-decreasingeffects in both the acquisition and performance components were largely eliminated andresponding was not significantly different from saline administration; the effects in thevehicle-treated group were similar before and after chronic vehicle administration. Moreimportant, SIV inoculation did not change the respective sensitivity of either of these groupsto Δ9-THC, nor did it affect the type of deficit produced by Δ9-THC administration underthis behavioral procedure (i.e., rate-decreasing effects). A change in the type of deficit mighthave signaled an interaction in areas of the brain not previously suspected to play a role inthe CNS effects of either Δ9-THC or SIV (e.g., see Weed et al., 2004). The single exceptionto the change in sensitivity was an SIV-inoculated subject that did not develop toleranceeven though it was chronically administered Δ9-THC. Another important piece of data fromthe 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 indicatedthat the incidence of significant neuropathology and of opportunistic infections was lower inSIV-infected subjects chronically treated with Δ9-THC than in SIV-infected subjectschronically treated with vehicle. Finally, SIV-infected subjects that were chronically treatedwith Δ9-THC had significantly lower expression of the inflammatory cytokine MCP-1 thanSIV-infected subjects chronically treated with vehicle, with the highest individual cytokinelevels occurring almost universally in the two subjects found to have notable CNSpathology.

Both i.v. and i.m. administration of increasing doses of Δ9-THC has been shown to disruptresponding 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 amanner similar to that observed in humans (Bickel et al., 1989; Kamien et al., 1994). Forexample, Kamien et al. (1994) found that Δ9-THC dose-dependently decreased response ratein eight healthy adult volunteers, but only increased errors marginally in the acquisitioncomponent of the task. These investigators also found that the time course for the effects ofΔ9-THC on the percentage of errors during the acquisition component varied amongsubjects, with peak effects ranging from 90 to 300 min after drug ingestion. Similar to thepresent 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 meanpercentage of correct responses, in nine male rhesus monkeys responding in an incrementalrepeated-acquisition task. However, neither of these studies nor the present study should betaken 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 whichacute 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 producedmarked decreases in the rate of reinforcement, which has been shown to directly contribute

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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 forstudying the effects of drugs on the acquisition behavior of individual subjects. This wasnecessary because many of the earliest studies examining the effects of drugs on learningonly compared groups of subjects (i.e., drug vs. nondrug controls), and this resulted in drugeffects that were generally small in magnitude and of little practical significance (seeThompson & Moerschbaecher, 1979). The same could also be said for the mean effects ofΔ9-THC on percent errors in this study, because the grouped data did not accurately reflectthe variability in the data for this dependent measure. For example, an examination of theindividual subject data in Figures 6 and 7 shows that the percentage of errors was quite highfor some subjects and that the error rates after chronic treatment with Δ9-THC were reducedmore 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 studyalso 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 thedose-effect curves was obtained for response rate and the percentage of errors. The resultsfrom the present study also add to the small amount of existing data in the literatureinvolving either monkeys or humans showing that tolerance develops to the disruptiveeffects of Δ9-THC on transitional behaviors or learning (see Thompson & Moerschbaecher,1979). In nonhuman primates, for example, most of the studies examining the developmentof 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 ourknowledge, have used a learning task. The same is true for the studies involving otheranimal 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 developmentof tolerance to Δ9-THC in the acquisition and performance components of a behavioral taskthat was very similar to the one used in the present study. In humans, these types ofprospective data are difficult to obtain because of a host of uncontrolled variables, includingretrospective 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 canalter the development of tolerance to the rate-decreasing or variable error-increasing effectsof Δ9-THC in monkeys after it has been established or that Δ9-THC’s disruptive effects onbehavior 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 eachcomponent of the behavioral task did not shift after SIV infection in any of the othersubjects in this group, and the magnitude of the shifts remained similar to those of theuninfected subjects that received chronic Δ9-THC (compare Figures 6 and 7). Such effectswould also be consistent with the effects of marijuana use on cognitive function reported byCristiani et al. (2004) in HIV-infected humans. In their study, marijuana use interactedsignificantly with responding on a battery of neuropsychological tests in HIV-infectedindividuals who were symptomatic, but it did not interact significantly with cognitivefunction in uninfected individuals or HIV-infected individuals who were asymptomatic. Inthe present study, three of the four SIV-infected subjects that received vehicle chronicallyreached the symptomatic stage of SIV infection prior to the subjects that received Δ9-THCchronically. Another reason to eliminate SIV infection as a reason for the absence of

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tolerance in the one Δ9-THC-treated subject is that this same subject (RX) did not show anytolerance after receiving the chronic dose for the 28-day period prior to infection and wasconsistently 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 levelsof CB-1 receptor expression in the hippocampus of this entire group (see Figure 9A), whichwould suggest an increased sensitivity for this subject compared to the other Δ9-THC-treatedsubjects in the group. In general, chronic Δ9-THC produced a significant reduction of CB-1and 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 cannabinoidreceptor levels. The fact that CB-2 receptors were significantly reduced in the hippocampusis intriguing because of the recent debate surrounding their relevance and function in theCNS (e.g., see Van Sickle et al., 2005). CB-2 receptors are mostly found in the periphery onstructures such as the spleen, thymus, tonsils, bone marrow, and testes (for a review, seeHowlett et al., 2002), and they are known to be integrally involved in immune function asthey are found on immune cells (Cabral & Griffin-Thomas, 2009; Klein, Friedman, &Specter, 1998). Recently, however, CB-2 receptor mRNA has been detected in total RNAfrom cultured microglia isolated from the cerebral cortex of neonatal rat brain (Carlisle,Marciano-Cabral, Staab, Ludwick, & Cabral, 2002), but these receptors are not thought tohave a functional role in the CNS (e.g., Yao et al., 2009). A limitation of our measures is thefact that expression was determined in tissue extracts that did not allow for thedifferentiation 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 immunologicaleffects, specific or nonspecific agonists could serve as potential pharmacotherapeutics. Oneof the particularly appealing aspects of CB-2 agonists as pharmacotherapeutics is that theymight be able to produce therapeutic effects without producing the psychotropic CNS effectscharacteristic 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 tosuggest that the cannabinoids such as Δ9-THC might have the capacity to lower plasma viralload in SIV-infected subjects. Although the effects on viral load were not significant up to 6months after SIV inoculation, there was a downward trend during the later monthspostinfection, and these data would support existing in vitro data indicating that thesynthetic cannabinoid WIN 55,212–2 can potently inhibit HIV-1 expression in aconcentration 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’santiviral activity as a selective CB-2 receptor antagonist, SR144528, blocked its antiviraleffect in microglial cells; a similar antagonism was not obtained with the CB-1 receptorantagonist SR141716A.

Among the difficulties in ascertaining the impact of certain drugs on HIV-infected humansis that the drugs must often be tested in conjunction with therapeutic drugs such as proteaseinhibitors or nucleoside reverse transcriptase inhibitors (e.g., Bredt et al., 2002; Gorter,Seefried, & Volberding, 1992). In the present study, no other drugs were administeredchronically 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 thechronic dose tested even though they were clearly tolerant to the behaviorally disruptiveeffects of this dose. For example, in the current study, none of the chronic Δ9-THC-treatedsubjects had significant neuropathological findings at the time of necropsy compared to twoof the four vehicle-treated subjects. Additionally, only one of the four Δ9-THC-treated

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subjects was found to harbor an opportunistic infection by histopathological analysiscompared to three of four vehicle-treated subjects. The organisms identified were generallycomparable to those described in human autopsies of AIDS patients. Specifically, in a largeretrospective review of 565 adult AIDS autopsies, Klatt, Nichols, and Noguchi (1994)reported a 54% incidence of Pneumocystis carinii pneumonia, 32% incidence ofMyocobacterium spp., and 7% incidence of CMV encephalitis. A similar pattern ofinfections was documented in the current primate study, with Pneumocystis being the mostcommonly 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-THCwould also seem to set cannabinoids apart from other drugs of abuse such as alcohol, whichhas clearly been shown to increase viral replication (Poonia et al., 2006) and decrease thelife span of SIV-infected monkeys (Bagby, Zhang, Purcell, Didier, & Nelson, 2006). Opiateshave 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, seeNoel, Rivera-Amill, Buch, & Kumar, 2008). Lastly, in vitro studies with CNS stimulantssuch methamphetamine (Liang et al., 2008) and cocaine (Peterson et al., 1993) haveindicated that these drugs can enhance infection of macrophages by HIV-1; however, thesedata have not been extended to in vivo experimental data involving SIV. Data from thepresent study also contrast with reports suggesting that the cannabinoids can decreaseimmunity. For example, Zhu et al. (2000) found that Δ9-THC can inhibit antitumorimmunity by a CB-2 receptor-mediated, cytokine-dependent pathway in immunocompetentmice. This effect was hypothesized to have been the result of Δ9-THC-induced increases inIL-1 and tumor growth factor beta (TGF-β) and decreases in IL-2 and IFN-γ release. Thesedata were also consistent with data from Newton, Klein, and Friedman (1994) showing thatΔ9-THC can alter the development of cell-mediated immunity to primary infection fromagents such as L. pneumophila, which depends on the production of type-1 cytokines such asIL-2 and IFN-γ. Together, results such as these have led to the hypothesis that Δ9-THCgenerally decreases type-1 cytokines and increases type-2 cytokines to produce its overallimmunosuppressive effects (Klein et al., 1998). This hypothesis does not fit the cytokinedata obtained from brain in the present study, however. In the present study, with theexception of IL-12/23 and IL-4, Δ9-THC-treated subjects tended to have lower levels of thecytokines 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 animportant immunomodulatory effect of the cannabinoids on SIV remains to be investigated.

In summary, chronic administration of Δ9-THC produced tolerance to its rate-decreasingeffects in both the learning and performance components of a complex behavioral task whencompared to chronic administration of vehicle. Following SIV inoculation, the disruptiveeffects of Δ9-THC were similar to those obtained prior to SIV inoculation in both thevehicle- and Δ9-THC-treated groups. These data support the notion that there may be littleinteraction between the disruptive effects of Δ9-THC and SIV infection, or the disease statecreated by SIV infection, during the early stages of infection. Though the current study islimited by small numbers, the notable difference in neuropathology, and in opportunisticinfection rates, between vehicle- and Δ9-THC-treated SIV-infected subjects warrants furtherinvestigation. 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 inflammatorycytokines, but it did decrease CB-1 and CB-2 levels in the hippocampus.

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AcknowledgmentsThis work was supported by USPHS Grants DA 020419 (P.E.M.), DA 019625 (P.J.W.), and P20-RR-018766(C.M.F.). The authors would also like to sincerely thank the Drug Supply & Analytical Services Branch of NIDAfor supplying the Δ9-THC, Dr. Ted G. Weyand for his expertise in nonhuman primate neuroanatomy, and Ms.Jaime A. Hubbell for her expert technical assistance.

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Figure 1.Diagram showing the timeline of manipulations for the subjects in the four treatment groups.

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Figure 2.Δ9-THC dose-effect curves for response rate and the percentage of errors in each behavioralcomponent of a complex operant task established in rhesus monkeys prior to chronic THCor vehicle administration and SIV infection (n = 15). The unfilled symbols represent datafrom the acquisition components, whereas the filled symbols represent data from theperformance components. The data points and vertical lines above “C” (i.e., control) and inthe dose-effect curves represent a grand mean and standard error of the mean (SEM), as allof the individual subjects received multiple determinations of saline and each dose of Δ9-THC. Asterisks indicate significant differences from control injections; the percentage oferrors was not analyzed or plotted when response rates fell below 5 responses/min in eithercomponent. Numerical values in parentheses and adjacent to a data point indicate thenumber of subjects represented by that point when it differed from the total number ofsubjects for the group.

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Figure 3.Δ9-THC dose-effect curves for response rate and the percentage of errors in each behavioralcomponent of a complex operant task established in both the vehicle- (n = 7) and Δ9-THC-treated (n = 8) groups before and after 28 days of chronic treatment. The unfilled symbolsrepresent data from the acquisition components, whereas the filled symbols represent datafrom the performance components; symbols with crosses represent data collected after 28days of chronic treatment. The data points and vertical lines above “C” and in the dose-effect curves represent a grand mean and standard error of the mean (SEM), as all of theindividual subjects received multiple determinations of saline and each dose of Δ9-THC.The percentage of errors was not analyzed or plotted when response rates fell below 5responses/min in either component. Numerical values in parentheses and adjacent to a datapoint indicate the number of subjects represented by that point when it differed from thetotal number of subjects for the group. Asterisks indicate significant differences fromcontrol, whereas pound signs indicated significant differences between pre- and postchronicΔ9-THC determinations.

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Figure 4.Δ9-THC dose-effect curves for response rate and the percentage of errors in the acquisitioncomponents of a complex operant task established in both the vehicle- (n = 7) and Δ9-THC-treated (n = 8) groups after SIV inoculation. The unfilled symbols represent data obtainedprior to chronic vehicle or THC administration, whereas the unfilled symbols with crossesrepresent data obtained after 28 days of chronic treatment and SIV inoculation. The chronicdose for all of the subjects except RX was 0.32 mg/kg. For other details, see legend inFigure 3. Numerical values in parentheses were omitted in many instances for the purposesof clarity.

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Figure 5.Δ9-THC dose-effect curves for response rate and the percentage of errors in the performancecomponents of a complex operant task established in both the vehicle- (n = 7) and Δ9-THC-treated (n = 8) groups after SIV inoculation. The filled symbols represent data obtained priorto chronic vehicle or Δ9-THC administration, whereas the filled symbols with crossesrepresent data obtained after 28 days of chronic treatment and SIV inoculation. For otherdetails, see legend in Figure 3. Numerical values in parentheses were omitted in manyinstances for the purposes of clarity.

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Figure 6.Δ9-THC dose-effect curves for response rate and the percentage of errors established in theindividual subjects from the noninfected and SIV-infected treatment groups. These data onlydepict the effects in the acquisition components of the behavioral task. The solid linesrepresent data obtained from each subject before Δ9-THC administration, whereas thedashed lines represent data obtained from that same subject after chronic Δ9-THCadministration.

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Figure 7.Δ9-THC dose-effect curves for response rate and the percentage of errors established in theindividual subjects from the noninfected and SIV-infected treatment groups. These data onlydepict the effects in the performance components of the behavioral task. The solid linesrepresent data obtained from each subject before Δ9-THC administration, whereas thedashed lines represent data obtained from that same subject after chronic Δ9-THCadministration.

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Figure 8.Viral loads in plasma (A), CSF (B), and brain tissue (C) for vehicle-treated and Δ9-THC-treated rhesus macaques following SIV inoculation. Unfilled symbols indicate viral loads forindividual subjects in the vehicle-treated group, whereas fill symbols indicate viral loads forindividual subjects in the Δ9-THC-treated group. The lines represent the geometric meansfor viral load in each group. Assay limit of detection was 50 copies SIV/ml of plasma and100 copies SIV/ml of CSF. Samples below the limit of detection (negative samples) wereassigned a value for statistical comparisons. The lower case letters above the data points inpanel B indicate significant differences in viral load among the various time points after SIVinoculation. Viral loads in panel C were determined in tissue from three areas of the brain:lateral cerebellum (LC), prefrontal cortex (PFC), and orbital frontal cortex (OFC).

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Figure 9.Effects of chronic administration of vehicle (n = 3) or Δ9-THC (n = 4) on CB-1 and CB-2receptor protein expression in the hippocampus of SIV-infected rhesus macaques. The bandsin 9A were normalized to β-actin, an internal control for the overall amount of protein. Thequantification of the bands are shown in 9B and expressed as a percentage of receptorexpression in vehicle-treated subjects (control). Asterisks indicate significant differencesfrom control.

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Figure 10.Slides of the CNS findings in subjects TR (A and B) and BA (C).

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Table 1

Body Weights of Individual Subjects in Each Treatment Group Prior to Chronic Δ9-THC or VehicleAdministration (−1 Month) and 7 Months After SIV Inoculation

Subject Treatment Body weight (−1 month) Body weight (7 months)

RG (A1R067) Veh/−SIV 10.24 10.90

DE (A1R035) Veh/−SIV 10.46 12.14

TE (A1R045) Veh/−SIV 10.12 9.94

RJ (A1R039) THC/−SIV 10.6 11.08

RO (A1R078) THC/−SIV 8.67 10.76

FR (A2R116) THC/−SIV 8.51 11.78

CH (A1R071) THC/−SIV 12.1 10.62

TR (A1R047) Veh/+SIV 9.24 9.54*

MJ (A1R058) Veh/+SIV 10.1 10.82

BA (A2L046) Veh/+SIV 9.92 10.82*

JA (A2R065) Veh/+SIV 8.49 9.66

PO (A2L094) THC/+SIV 9.58 11.16

RX (A1R005) THC/+SIV 10.38 11.64

MK (A1R044) THC/+SIV 9.63 9.20

AA (A1R022) THC/+SIV 8.71 9.08

Note. Veh/−SIV = vehicle/SIV uninfected; Veh/+SIV = vehicle/SIV infected; THC/−SIV = 9-THC/SIV uninfected; THC/+SIV = 9-THC/SIVinfected.

*Last recorded weights for these subjects, which did not reach the 7-mo post-SIV time point.


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Table 2

Mean Effective Doses (i.e., ED50s) for Decreasing Response Rate by 50% Before and After ChronicAdministration of Δ9-THC in Male Rhesus Monkeys

Task/Group Prechronic ED50 (mg/kg) Postchronic ED50 (mg/kg)

Acquisition component

Veh/−SIV 0.09 0.09

Veh/+SIV 0.12 0.15

THC/−SIV 0.14 1.21

THC/+SIV 0.14 1.34

Performance component

Veh/−SIV 0.22 0.15

Veh/+SIV 0.32 0.26

THC/−SIV 0.21 2.22

THC/+SIV 0.14 2.42

Note. Values generated from the dose-effect curves depicted in figures 4 and 5. Italicized value represents an estimation of the ED50 based on anextrapolation of the regression line generated for the downward slope of the curve.


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Tabl

e 3

Cyt

okin

e V

alue

s Obt

aine

d Fr

om S

IV-I

nfec

ted

Subj

ects

Tha

t Rec

eive

d Ei

ther

Veh

icle

or Δ

9 -TH

C C

hron

ical

ly

Tre

atm

ent

Subj

ect

IL-6

MC

P–1

IL-8

IL-1

2/23

IL-1β

IFN

-γG

M-C

SFIL

-18

IL-4

TN

F-α

Veh

/SIV

BA

12.3

131

9.09

74.0

80.

600.

060.

910.

0312

.06

0.10

4.08

TR18

3.01

515.

5462

.20

0.25

1.08

2.73

1.10

3.48

0.05

6.84

MJ

1.41

39.1

92.

590.

470.

000.

210.

050.

810.

02—

JA0.

013.

192.

970.

23—

0.15

0.02

0.61

0.04

—

THC

/SIV

RX

0.07

8.02

***

3.73

0.26

0.00

0.21

0.02

0.76

0.04

—

AA

0.38

8.15

3.36

0.29

0.00

0.20

0.02

0.71

0.09

—

MK

0.37

23.4

98.

140.

380.

010.

780.

021.

520.

084.

60

PO0.

053.

439.

900.

44—

0.33

0.02

0.67

0.07

—

Not

e. V

alue

s rep

rese

nt a

vera

ge e

xpre

ssio

n fr

om th

ree

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author manuscript nih public access rhesus macaques ......in addition to assessing the effects of...

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