Constitutional mechanisms of vulnerability and resilience to nicotine ...

Molecular Psychiatry (2009) 14, 653–667 & 2009 Nature Publishing Group All rights reserved 1359-4184/09 $32.00 www.nature.com/mp

FEATURE REVIEW

Constitutional mechanisms of vulnerability and resilience to nicotine dependence N Hiroi1,2 and D Scott2 1

Department of Psychiatry and Behavioral Sciences, Laboratory of Molecular Psychobiology, Albert Einstein College of Medicine, Bronx, NY, USA and 2Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA The core nature of nicotine dependence is evident in wide variations in how individuals become and remain smokers. Individuals with pre-existing behavioral traits are more likely to develop nicotine dependence and experience difficulty when attempting to quit. Many molecular factors likely contribute to individual variations in the development of nicotine dependence and behavioral traits in complex manners. However, the identification of such molecules has been hampered by the phenotypic complexity of nicotine dependence and the complex ways molecules affect elements of nicotine dependence. We hypothesize that nicotine dependence is, in part, a result of interactions between nicotine and pre-existing behavioral traits. This perspective suggests that the identification of the molecular bases of such pre-existing behavioral traits will contribute to the development of effective methods for reducing smoking dependence and for helping smokers to quit. Molecular Psychiatry (2009) 14, 653–667; doi:10.1038/mp.2009.16; published online 24 February 2009 Keywords: smoking; addiction; knockout mice; translational model; comorbidity; genetic susceptibility

Introduction According to a recent estimate, 1.3 billion individuals or one-fifth of the global population smokes. Tobacco smoking poses a grave health hazard, with half of the continuing smokers being expected to die prematurely due to tobacco-related diseases.1 Cigarette smoking is one of the most persistent types of substance dependence, comparable to cocaine dependence. A survey of 10 343 daily smokers and 107 daily cocaine users aged 12 years or older revealed that 75% of smokers and 72% of daily cocaine users try to cut down on their use. However, 80% of smokers and 66% of daily cocaine users feel unable to cut down on their use, despite their efforts. The majority of daily smokers (85%) and daily cocaine users (63%) feel they are dependent, and 37% of daily smokers and 49% of daily cocaine users feel sick when they stop or cut down on the use of the drug.2 Antidepressants and some forms of nicotine replacement therapy increase tobacco cessation rates.3,4 However, the majority of quitters relapse, despite the use of these aids or even a combination of these aids. More effective tobacco cessation treatments depend Correspondence: Dr N Hiroi, Department of Psychiatry and Behavioral Sciences, Laboratory of Molecular Psychobiology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA. E-mail: [email protected] Received 22 September 2008; revised 2 January 2009; accepted 22 January 2009; published online 24 February 2009

upon a more complete understanding of the mechanisms underlying dependence. Although chemicals other than nicotine might contribute to the development of continued smoking,5 evidence suggests that nicotine is a major determinant of dependence. When switched to denicotinized cigarettes, smokers consume fewer denicotinized cigarettes within 1 week.6 Smoking denicotinized cigarettes causes smokers to experience withdrawal symptoms.7 Conversely, nicotine replacement therapy reduces withdrawal symptoms and the urge to smoke in smokers during abstinence.8 Nicotine replacement also increases the success of cessation efforts.4 Varenicline, a partial nicotinic receptor agonist,9 increases the success rates of long-term smoking cessation efforts.10 Nicotine alone is sufficient to sustain self-administration behavior in humans.11 In this review, we discuss the difficulty in defining nicotine dependence, individual variation in smoking and pre-existing traits for smoking. We also discuss potential molecular correlates of individual susceptibility to nicotine dependence in humans and experimental animals. We propose a hypothetical framework to better understand nicotine dependence and discuss its implications for future research directions.

Many definitions of nicotine dependence The clinical definition of nicotine dependence depends on the measure that is used. The Fagerstro¨m Test for Nicotine Dependence (FTND) and the Diagnostic and Statistical Manual (DSM)-III or

Constitutional mechanisms of nicotine dependence N Hiroi and D Scott

654

DSM-IV-Revised are commonly used for studies of nicotine dependence. Both measures define nicotine dependence in terms of behavioral and clinical manifestations. The FTND is a revised version of the earlier Fagerstro¨m Tolerance Questionnaire. As the name indicates, this scale assumes that physical dependence, including withdrawal and tolerance, motivates compulsive smoking. It assesses the degree of dependence on a continuous scale.12 The DSM focuses on salient behavioral and physiological features of smoking. These features include (1) impaired control over use, (2) use greater than intended, (3) withdrawal, (4) use despite harm and problems and (5) tolerance. DSM criteria not readily applicable to nicotine dependence are (6) large amounts of time spent obtaining, using and recovering from the substance and (7) forgoing other activities to use the substance.13 Each item of the DSM is judged to be present or absent, and overall dependence is categorically judged to be present or absent if a smoker meets three or more of these criteria at any time during a 12-month period. This diagnosis is useful for reliably determining the prevalence of smoking. However, a shortcoming of the DSM-based diagnosis is that it does not take into account the gradual development of smoking. Even when a smoker is nearing the threshold of a diagnostic cutoff, he would be judged as non-dependent. Moreover, a single diagnosis does not reveal the combinations of the multidimensional symptoms that are present. At different stages of smoking, smokers might exhibit distinct sets of characteristics or more of one particular symptom. Grouping smokers into a single ‘dependent’ group ignores the potential heterogeneity among these individuals. Not surprisingly, the FTND and DSM do not correlate well. The FTND defines smoking on a continuous dimension and has no clear-cut point that divides dependent and non-dependent individuals. Cutoffs can be arbitrarily set to divide nondependent and dependent smokers or to define low, medium or high levels of dependence. However, no matter how a cutoff is set, the FTND-defined nicotine dependence does not agree well with nicotine dependence defined by the DSM-III-R-based scale.14 The FTND is a better predictor of smoking cessation than the DSM-III-R.15,16 Whether nicotine dependence should be categorically defined as a state or a continuous process and whether nicotine dependence involves a single process or multiple processes are matters of debate.17 The Wisconsin Inventory of Smoking Dependence Motives (WISDM-68) was developed to comprehensively capture the multidimensional reasons for smoking on a continuous scale.18 This inventory includes the following potential motives for smoking: a strong emotional attachment to smoking and cigarettes (that is, affiliative attachment); smoking without awareness or intention (that is, automaticity); smoking among all options despite constraints or negative consequences (that is, behavioral choice-

Molecular Psychiatry

melioration); smoking to enhance cognitive functioning (that is, cognitive enhancement); frequent urge, inability to ignore urge to smoke and intensified and intolerable nature of urge to smoke during abstinence (that is, craving); strong impact of smoking-associated cues on urge to smoke and smoking (that is, cue exposure-associative processes); a sense of loss of volitional control over smoking (that is, loss of control); the ability of smoking to improve mood, lift a ‘down’ mood, relieve irritability and increase the ability to cope with stress (that is, negative reinforcement); the desire to smoke to experience pleasure or to enhance an already positive feeling or experience (that is, positive reinforcement); social stimuli or contexts that either model or invite smoking (that is, social-environmental goads); the need to experience the sensory and gustatory effects of smoking (that is, taste and sensory properties); the need to smoke increasing amounts over time or the ability to smoke large amounts without acute toxicity (that is, tolerance); and use of cigarettes to control body weight or appetite (that is, weight control). This test is likely to uncover distinct underlying motives for various aspects of smoking. Variation in how much one smokes is correlated with tolerance, loss of control, craving, automaticity, behavioral choice-melioration, affiliative attachment, cognitive enhancement, cue exposure-associative processes and negative reinforcement; the number of cigarettes smoked is less correlated with positive reinforcement, weight control, taste and sensory properties and social-environmental goads.18 Relapse also can be predicted by some of these motives. Smokers subjectively feel that they relapse because they automatically smoke without thinking about it (that is, automaticity), they smoke to enhance cognitive function (that is, cognitive enhancement), they smoke to alleviate stress or withdrawal (that is, negative reinforcement) and their social environments are conducive to smoking (that is, social-environmental goads).18 In a follow-up study, tolerance, automaticity, social-environmental goads and craving were found to be predictive of abstinence.16 Many behavioral and physiological features are potentially related to the mechanisms underlying nicotine dependence. The symptomatic complexity challenges the assumption that a single mechanism governs the many aspects of nicotine dependence. This raises the question as to how to model ‘dependence’ as a whole and whether such an attempt is a reasonable endeavor. In addition, imposing an artificial division between dependent and nondependent individuals is conceptually limiting. Smoking is more appropriately characterized by the degree of dependence on a continuous, multidimensional scale.

Myth of the average smoker The interindividual variation in nicotine intake is large. Among smokers aged 18 or older in the United


States, the majority (64%) consume between 5 and 24 cigarettes per day. A sizable population of smokers (23%) smoke only on some days or smoke 1–4 cigarettes per day. Heavy smokers who smoke at least 25 cigarettes per day make up 12% of the total population of smokers.19 These different levels of smoking represent a crosssection of how individuals’ smoking habits change over a lifetime. Most smokers initiate smoking during adolescence, and their smoking habits change through distinct trajectories into adulthood. Although there is inconsistency among studies regarding how many trajectories exist, prospective studies have identified four primary trajectories: (1) persistent low-level smoking, (2) early initiation of smoking with rapid progression to heavy, persistent smoking, (3) early initiation of smoking with slow progression to moderate smoking and eventual cessation, (4) later initiation of smoking with progression to moderate to heavy smoking.20–36 Those who become persistent, heavy smokers comprise approximately one-third of those who have ever tried cigarettes. Like adolescent smokers, adult smokers show various trajectories. Among college students examined over a course of 4 years, light smokers quit (45%), became occasional (35%) or daily smokers (20%). The majority of those who were daily smokers at baseline quit or reduced smoking (54%). The remaining individuals became low-level smokers (6%) or increased their daily smoking (27%).37 Among adults over 24 years old who have smoked for at least 5 years, 37% of daily low-level smokers (pfive cigarettes per day) quit, 36% retain the same level of smoking, 21% increase their smoking to more than five cigarettes per day and 6% become occasional smokers over a period of 2 years.38 Light or intermittent smokers are often in transition to either quitting or becoming regular smokers. However, a subpopulation of intermittent and light smokers remains at this level for more than 20 years without changing their smoking status.39 Even after smoking tens of thousands of cigarettes over 20 years, these smokers, dubbed ‘chippers’, do not experience craving, mood disturbance, reduced arousal, or sleep disturbance.40 Chippers feel the urge to smoke only in settings where they usually smoke, such as in settings related to relaxation, socializing, eating and drinking alcohol; they do not feel the urge to smoke or experience positive feelings upon smoking outside of their usual smoking settings.41–43 The intensity and time course of withdrawal symptoms following quitting differs from individual to individual. Some individuals experience intense withdrawal immediately following the onset of abstinence, with the symptoms gradually or quickly waning. Others experience a rebound in withdrawal symptoms later.44,45 Lapses are highly correlated with the individual time course of withdrawal intensity,46 and lapsers have higher levels of withdrawal symptoms (for example, negative affect) than abstainers.44

There is also heterogeneity in nicotine dependence among smokers. During adolescence, different smoking trajectories are correlated with different levels of nicotine dependence. Adolescents whose smoking rates rapidly escalate tend to develop stronger nicotine dependence, with 95% becoming dependent (as defined by the International Statistical Classification of Diseases and Related Health Problems or ICD). In contrast, only 12% of non-progressing, low-intensity adolescent smokers become nicotine dependent, whereas 79% of both slow and moderate smoking escalators become dependent.30 Similarly, by early adulthood, 51% of early stable smokers meet the FTND-defined criteria for nicotine dependence, compared to 26% of late stable smokers and 5% of low-level smokers.47 However, individual smokers with different trajectories during adolescence might eventually reach similar levels of nicotine dependence by the time they reach adulthood.35 Individual smokers markedly differ in the trajectory of smoking, rate of smoking, relapse timing, withdrawal and nicotine dependence. The mechanisms of nicotine dependence need to explain the reasons for this variation.

655

Pre-existing conditions for smoking progression The individual variation in nicotine dependence raises the question as to what may be unique about those who are more vulnerable to nicotine dependence. One way of viewing this variation is that it simply reflects the amount of nicotine exposure. That is, the more one is exposed to nicotine, the more likely they will develop dependence. According to this explanation, any person who is exposed to a sufficient amount of nicotine will develop dependence. However, this view cannot adequately explain why chippers maintain smoking without developing dependence and why only one-third of novice light smokers increase their level of smoking. Evidence suggests that those susceptible to heavy smoking are not simply individuals who happened to be exposed to large amounts of nicotine. Longitudinal studies have shown that individuals with higher levels of novelty/sensation seeking tend to have a greater susceptibility to smoking and nicotine dependence.33,48–55 Novelty seeking is ‘a heritable tendency toward intense exhilaration or excitement in response to novel stimuli or cues for potential rewards or potential relief of punishment, leading to frequent exploratory activity in pursuit of potential rewards’.56 Sensation seeking is ‘a trait by which an individual seeks varied, novel, complex and intense sensations and experiences and is willing to take physical, social, legal and financial risks for the sake of such experiences’.57 These two scales are highly correlated.58 If a cigarette is perceived as a novel object during adolescence, then novelty/sensation seeking could easily manifest itself as initiation of smoking. However, because novelty seeking might not necessaMolecular Psychiatry


656

rily be a predictor for sustained smoking,55 this behavioral trait may not influence all aspects of nicotine dependence. Novelty seeking predicts the onset of disruptive behaviors, such as oppositional defiant disorder and conduct disorder. These behavioral traits also are associated with susceptibility to nicotine dependence.52 The presence of conduct disorder in childhood and adolescence is a strong predictor of daily smoking and DSM-defined nicotine dependence.55,59–61 Such traits are likely to contribute to poor choices despite negative future consequences (that is, behavioral choice-melioration).18 Novelty seeking also predicts the future incidence of attention-deficit hyperactivity disorder (ADHD).52 Longitudinal studies have shown that ADHD predicts progression to regular smoking and nicotine dependence.59,62–67 ADHD symptoms of inattention68,69 and hyperactivity/impulsivity60 are associated with smoking and nicotine dependence, although these two classes of symptoms might contribute to nicotine dependence during different developmental periods.70 Because nicotine improves attention in smokers with ADHD71 and cognitive/behavioral inhibition in non-smoking adolescents with ADHD,72–76 negative reinforcement might underlie nicotine dependence in smokers with ADHD.68 Pre-existing conditions that may lead a person to smoke not only include motivational and behavioral traits, but also include more pathological conditions. Although smoking withdrawal might cause anxiety and depression-like symptoms, the opposite can also be true. The presence of depressive disorders and some forms of anxiety disorders may predict the likelihood that a person will initiate smoking and develop dependence.77,78 However, it is unclear if there is a causal relationship between depressed mood or anxiety and various aspects of nicotine dependence.79

Genetic origin of variation in nicotine dependence and pre-existing traits Multiple factors likely contribute to individual variation in pre-existing behavioral traits and susceptibility and resilience to nicotine dependence. Environmental factors are thought to contribute to variation in nicotine dependence. However, the precise identification of the environmental factors is difficult, and such factors might not exert as robust an influence as genetic factors.80 Twin studies have consistently shown that heritable factors account for a substantial amount of the variance in susceptibility to smoking initiation, quantity, persistence, regular use, dependence and cessation.81–87 However, heritability estimates vary depending upon the particular aspect of smoking and the definition of dependence. Different genetic factors appear to affect smoking initiation, regular use and nicotine dependence (as defined by the FTND) as well as DSM-IV-defined nicotine withdrawal.80,88 Molecular Psychiatry

Although it is clear that there is a genetic influence on nicotine dependence, the exact genes that contribute to dependence have not been clearly delineated. Genome-wide linkage analyses have implicated almost all chromosomes in various aspects of smoking. However, the involvement of each locus has not been consistently demonstrated. Several factors are thought to contribute to this problem. First, studies have used different criteria, including the Fagerstro¨m Tolerance Questionnaire, number of cigarettes smoked per day, maximum number of cigarettes smoked in any 24-h period, age at which the first cigarette was smoked, FTND, cessation, frequency, withdrawal severity and DSM. These different measures have led to the identification of linkages to different chromosomes within the same population sample. For example, linkage was found on chromosome 5 when smoking quantity was used as a criterion, chromosome 6 with FTND, chromosome 7 with a DSM-based continuous scale, chromosome 8 with a dichotomous DSM classification, chromosome 16 with cessation and chromosome 19 with smoking frequency.89 Second, different population bases show different linkage loci even when the same measures are used. Linkage with FTND scales are located on chromosomes 9, 10, 11 and 13 in an AfricanAmerican sample but on chromosomes 4 and 9 in a sample from Americans of European origin.90,91 A genome-wide association study found that alleles in the neurexin 1 and the b3 nicotinic receptor genes may be associated with FTND-defined nicotine dependence.92 However, another genome-wide association study, examining far more SNPs, identified a different set of genes that were associated with both nicotine and other substance dependence.93 By contrast, a number of genes have been found to be consistently associated with smoking cessation in three different sample populations.94 Some of these genes encode proteins that are involved in cell adhesion and cell signaling. Association studies examining individual candidate genes have uncovered variants of many monoamine-related genes that might contribute to smoking initiation, persistence, cessation, consumption and therapeutic response (see refs. 95–99 for reviews). Associations between specific genes and variation in behavioral traits have also been examined. Polymorphisms in the dopamine D4 receptor have been implicated in novelty/sensation seeking, and polymorphisms in the serotonin transporter have been linked to anxiety-related traits.100 Although some studies do not support the association between D4 receptor alleles and novelty/sensation seeking, the association is more consistent in subjects under 35 years old.101 Like genes associated with nicotine dependence, variants in monoamine-related genes have been reported to be associated with susceptibility to ADHD. These genes include the dopamine transporter, dopamine D4 receptor, tryptophan hydroxylase 2, phenylethanolamine N-methyltransferase, adrenergic b2 receptor and monoamine


oxidase A (MAOA).102 The serotonin receptor 2A, adrenergic a2A receptor, adrenergic a2C receptor, phenylethanolamine N-methyltransferase, catecholO-methyltransferase, serotonin transporter and MAOA have been reported to be associated with conduct disorder, oppositional defiant disorder or antisocial behavior.103–105 In some cases, human association studies are difficult to replicate. Many reasons are likely to contribute to this problem, but the following are some of the potential reasons relevant to the gene side of association. First, the impact of each gene on nicotine dependence or novelty/sensation seeking is thought to be small. A meta-analysis could potentially reveal an overall trend in a large number of studies but might overestimate the impact of a gene allele on smoking and associated behavioral traits. This is because meta-analyses are affected by publication bias; more studies with positive associations are published than those with no association and more significant measures are reported than those that are less or not significant even within individual studies.106 Second, reported gene alleles might not be the genuine source of phenotypic variation. Rather, allelic association with a phenotype could reflect the impact of other gene alleles that are linked to the reported gene alleles. Third, the phenotypic expression of a single gene allele may be influenced by alleles in other genes that differ in different ethnic groups. A problem at the phenotypic side of correlation is that the scales used to categorize phenotypes might not match genetic influences perfectly. For example, categorizing nicotine dependence and pre-existing behavioral traits using self-reported questionnaires may not fit well with the way single genes influence behavior. If a specific gene affects only a specific element/aspect of nicotine dependence or preexisting behavioral traits, the overall association between gene polymorphisms and traits could fail to reach significance. Another complicating factor is that variance of each motive for smoking is large and thus each smoker might have a unique set of motives for smoking.18 Moreover, various subscales of novelty/sensation seeking do not necessarily correlate with each other, and thus what is globally defined as novelty/sensation seeking might contain more than one psychological process.107 In mice, behaviors in various behavioral tasks that involve novel elements do not necessarily co-vary with genetic background, suggesting a complex influence of genetic factors on behaviors in response to novel stimuli.108,109 Different elements of nicotine dependence and behavioral traits might have non-identical, albeit partially overlapping, genetic bases. The reproducibility of human association studies might be improved if they focused on associations between single genes and distinct elements of nicotine dependence and behavioral traits.

Phenotypic refinement: translational models of nicotine dependence

657

Experimental animals provide a complementary approach to precisely characterize genetic mechanisms of nicotine dependence independently of the subjective assessment of smokers. The ultimate value of rodent models lies in their ability to predict nicotine dependence in humans, but the translation of rodent data into human studies and vice versa must be done cautiously. Caution must be used when translating animal data to humans, particularly in terms of highly subjective processes such as craving, urge and pleasure, as there are no unequivocal, objective ways to measure these psychological processes in rodents. Another limitation of rodent models of nicotine dependence is that they are not designed to mimic the entire process of nicotine dependence. Ironically, this limitation may be advantageous in identifying behavioral elements that might be affected by molecules. Models that focus on specific, distinct aspects of nicotine dependence are advantageous when testing underlying mechanisms. A single molecule may not contribute equally to all elements of nicotine dependence, and distinct molecules may affect different elements of nicotine dependence. Feature-specific models are less likely to be contaminated by mechanistically heterogeneous processes and thus will allow us to tease apart distinct mechanisms. Three paradigms are widely used to determine the role of molecules in nicotine dependence using knockout (KO) mice: self-administration, conditioned place preference and withdrawal. Because mice cannot reliably or robustly establish intravenous self-administration, the oral route has been used to evaluate how much nicotine a mouse consumes. Nicotine consumption has a clear counterpart in smokers. Each smoker has a unique optimal blood nicotine concentration.110–112 The amount of nicotine taken in during smoking is the best predictor of relapse in humans.15 ‘Tolerance’, as defined in the WISDM-68 inventory, essentially characterizes how heavily one consumes nicotine.18 Cue reactivity or cue exposure-associative processes have been examined using the placeconditioning paradigm in mice. This Pavlovian conditioning procedure assesses an animal’s approach, termed conditioned place preference (CPP), toward sensory cues that are associated with a substance that has dependence potential.113–115 Cue reactivity is a prominent element of nicotine dependence in humans. Non-nicotine cues that are associated with smoking (for example, smoking paraphernalia and smoking-association sensory cues and context) contribute to relapse in ex-smokers and to persistent smoking in smokers.116,117 Cue reactivity correlates with the amount of smoking during early, developing stages of nicotine dependence, as well as in late stages of dependence.18 Cue reactivity is established through Pavlovian conditioning.118 When Molecular Psychiatry


658

neutral cues are paired with smoking under Pavlovian experimental conditions, new sensory cues acquire the ability to induce urge within only a few trials.119 Individuals differ in the way they react to these cues during relapses.120 In humans, withdrawal symptoms of nicotine dependence are divided into two major classes: somatic and affective withdrawal. During abstinence, smokers experience somatic symptoms, including insomnia, difficulty concentrating, restlessness, decreased heart rate, increased appetite and weight gain.121 Affective withdrawal includes feelings of depression, heightened anxiety, increased irritability or anger and frustration.122,123 Smokers report that avoidance of withdrawal is one of the subjective motives for heavy smoking and relapse (that is, negative reinforcement).18 Because a reliable method to demonstrate affective withdrawal signs has not been developed in mice until recently,124,125 somatic signs have been used widely to evaluate withdrawal in mice. Somatic withdrawal symptoms in mice include paw tremor, head shaking, scratching, body tremor, teeth chatter, ptosis, backing and jumping. Other possible contributors to smoking are difficult to model in experimental animals. For example, it is not feasible to establish that a mouse considers nicotine to be a ‘friend’ (affiliative attachment) or makes decisions in light of future negative consequences (behavioral choice-melioration). The latter is not a simple choice between a drug and other reinforcers but reflects a complex cognitive process that compares the immediate value of smoking with future reinforcers in the presence of other reinforcers and constraints on cigarettes. Determining whether animals feel that they have lost control over their behavior (loss of control) or whether they act without being aware (automaticity) is not feasible. Similarly, although craving may be an underlying motive, it is not possible to unequivocally attribute the cause of behavioral alterations to this subjective feeling in experimental animals. A systematic comparison between preclinical and clinical studies is difficult for several reasons. Rodent models focus on specific elements of nicotine dependence, whereas human association studies use more global definitions of nicotine dependence. A future challenge is to increase the translationability of animal and human studies by focusing on specific elements of human nicotine dependence, as exemplified in the WISDM-68. For example, examining the association between subjective feelings and activities of specific brain regions during exposure to smokingrelated cues in humans with specific genetic alleles would provide a human equivalent of rodent cue reactivity, as measured in CPP and withdrawalassociated conditioned place aversion (CPA). Moreover, a better understanding of the functional consequences of gene alleles (for example, expression and activity levels of proteins) in humans would be essential for comparing mouse KO studies to human studies.


Multiple molecules for distinct aspects of nicotine dependence Complex behaviors, including dependence-related behaviors, are thought to be influenced by multiple genes. Rodent quantitative trait loci for complex behavioral traits generally suggest that the average percentage of total phenotypic variance that is explained by each sequence variant is 5% or less.126 Such a small effect is difficult—if not impossible—to detect. The constitutive, complete deletion of genes using KO mice provides a unique opportunity to assess the maximum potential impact of a single gene on behavior. Another advantage of constitutive genetic manipulation is that it is expected to induce developmental and compensatory alterations. From the susceptibility perspective, secondary alterations originating from a single gene deletion do not limit interpretation and should not be considered a contaminant, because any molecular alteration would be expected to have some effects on related molecules and developmental processes in the human brains, as well as in the mouse brains.127–130 Developmental alterations cannot be recapitulated by pharmacological means that are applied to adult or even adolescent rodents. Evidence suggests that both shared and distinct molecular bases exist for elements of nicotine dependence. Both nicotine CPP and somatic withdrawal are reduced in mice that are defective for preproenkephalin131 or the m-opioid receptor.132 Other molecules influence some but not all models of nicotine dependence. Genetic deletion of the cannabinoid CB1 receptor or adenosine A2A receptor attenuates nicotine CPP but not somatic withdrawal signs.133,134 Mice deficient for the nicotinic acetylcholine receptor subtype b2 do not show withdrawal-induced CPA, but are normal in somatic withdrawal signs; a5 subunit KO mice show the opposite pattern of phenotypes.124 As the impact of single genes on elements of nicotine dependence would be difficult to detect in a model that incorporates many aspects of dependence symptoms, rodent models that focus on specific elements of nicotine dependence likely are the most effective models for understanding the genetic determinants of nicotine dependence. Distinct molecular mechanisms might also exist for different phases of exposure to nicotine. Mice deficient for the transcription factor FosB are impaired in behavioral alterations that are induced by repeated or prolonged exposure to nicotine in voluntary nicotine intake, nicotine CPP and nicotine-induced motor suppression, but these mice respond normally to acute single nicotine exposure.109 Concomitant with the behavioral effects of FosB deletion, FosB proteins increase in the brain of wild-type mice following repeated injections of nicotine, but not after a single, acute nicotine exposure.109


Gene variation is likely to influence nicotine dependence through many molecular pathways. First, molecules may influence nicotine dependence through direct involvement in signaling cascades activated by nicotine (Figure 1, circles). The nicotinic acetylcholine receptor is one such example (Figure 1, green circle).124,135–137 Alternatively, even if they are not part of a cascade that is directly activated by nicotine, other molecules still could influence nicotine dependence either by influencing the molecular cascades activated by nicotine (Figure 1, triangle) or independently of the nicotine-activated cascades (Figure 1, star). Specific examples of these factors are discussed below.

The pleiotropic action of these genes raises the question of whether molecules exist that are selectively involved in the development of nicotine dependence. Lack of behavioral selectivity does not necessarily indicate that the dependence phenotype in a KO mouse is a contaminant of some general behavioral abnormality. Instead, such lack of behavioral selectivity could provide a means to explore a causal relationship between pre-existing behavioral traits and substance dependence. In more general terms, it is difficult to assume that the organisms have evolved, during their evolutionary history, to express molecules that are selectively designed to respond to addictive substances.

Pleiotropy of nicotine dependence and pre-existing behavioral traits

Indirect influence of molecular variation on nicotine dependence

Why is there an association between nicotine dependence and pre-existing traits? One plausible biological explanation is that some molecules commonly affect both nicotine dependence and pre-existing behavioral traits.138 This phenomenon, called pleiotropy, could be one of the underlying mechanisms of nicotine dependence. Although human association studies have not provided genes that clearly exert pleiotropic effects on nicotine dependence and preexisting traits, animal studies have provided a few examples. FosB is induced along the mesencephalic dopaminergic systems and other limbic brain regions by nicotine and other addictive substances in rodents (see Figure 1, red circle).109,139 FosB is also induced in limbic and other brain regions by external stimuli that evoke stress and alter affect in rodents.140,141 FosB KO mice are impaired in nicotine intake and CPP, and also are abnormal in behavioral traits related to tasks in which stress levels are considered high.109 Another transcription factor, cAMP-response element binding protein (CREB), has been implicated in behaviors relevant to learning, depression and anxiety, as well as nicotine dependence.142–144 These transcription factors could have a common influence on elements of nicotine dependence and pre-existing behavioral traits.

Constitutive levels of molecules and their activities that are not regulated by nicotine nonetheless could influence smoking and relapses (Figure 1, triangle or star). Nicotine at physiological concentrations does not reliably alter the activity of MAOA, but the constitutive level of MAOA nevertheless might affect smoking in humans and behavioral responses to nicotine in mice. The activity levels of MAOA vary widely among individuals. In cultured skin fibroblasts, individual responses range from extremely low, almost undetectable, levels to levels 30-fold higher.145–149 Differences in MAOA activity of up to seven-fold have been noted in postmortem tissues of the human frontal cortex.149 These individual differences in MAOA activity exist at ages before smoking usually starts.145,149,150 The genetic origin for variance in MAOA activities is poorly understood. High levels of MAOA activities are associated with either a T allele, compared to a C allele, at position 1460 in exon 14 or with either 3.5 and 4 repeats, compared to other repeat numbers, at the variable number tandem repeat in the promoter region of the human MAOA gene.146,151–153 However, the impact of these polymorphisms on MAOA activity is weak and no known single alleles or their haplotypes adequately account for the large interindividual differences in basal MAOA activity.146,149,151–153 A PET study also demonstrated that MAOA alleles are not strongly correlated with MAOA activity in vivo.154 The weak impact of genetic alleles on MAOA activities might be one of the reasons why association studies of human MAOA and nicotine dependence have not been consistent. Some studies have shown a positive association between the high-activity alleles and an increased risk of nicotine dependence,155–157 but other studies have failed to confirm this association.158,159 These studies have many procedural differences, including the gender, age and other characteristics of the sample population and the definition of dependence, which make direct comparisons difficult.

Figure 1 Possible effects of molecular variation on the development of nicotine dependence. The degree of nicotine dependence may be determined by molecular variation in the cascades that are activated directly (green circle) or indirectly (gray and red circles) by nicotine, by molecular variation that indirectly affects nicotine-regulated molecular cascades (triangle), or by variation in molecules that influence the degree of dependence independently of these cascades (star).

659



660

Constitutive Maoa-deficient mice are impaired in nicotine intake and nicotine CPP.160 This effect cannot be generalized to the isoenzyme MAOB in mice, as constitutive Maob-deficient mice exhibit normal nicotine intake.161 Although species differences should be taken into account in generalizing this finding to humans,162 constitutively high and low MAOA activities might confer susceptibility and resilience, respectively, to levels of nicotine intake and cue reactivity in smokers. Although it still is possible that some unidentified alleles within MAOA or in other genes influence MAOA activities, non-genetic, environmental factors also might alter MAOA activities. Tobacco smoke contains chemicals that inhibit MAOA,163–166 and brain MAOA levels are reduced in smokers.167 Simultaneous, irreversible inhibition of both MAOA and MAOB by non-selective MAOA/B inhibitors (for example, tranylcypromine or phenelzine) increases nicotine self-administration in rats.168,169 Clorgyline, an irreversible MAOA inhibitor, also promotes nicotine self-administration in rats.169,170 These studies in rats suggest that MAO activities have an inhibitory effect on nicotine intake. However, this possibility is inconsistent with the report that the MAOA inhibitor moclobemide does not increase smoking and rather has some subjective beneficial effects on reducing smoking.171 Similarly, none of the MAOB inhibitors tested so far lead to increased smoking; rather, they result in reduced smoking and increased cessation rates.172–174 These studies with Maoa KO mice and pharmacological inhibition of MAOA in rats should not be compared directly. First, the developmental impact of constitutive deletion of MAOA expression on the behavioral effects of nicotine likely occurs through processes different from acute or semi-chronic pharmacological inhibition of MAOA during adulthood. In fact, the constitutive inactivation of MAOA and the pharmacological inhibition of MAOA by clorgyline induce many different and even opposite effects on various behaviors in mice. For example, acoustic startle response is reduced in Maoa KO mice, compared to wild-type mice, but is either unaffected or slightly potentiated by clorgyline in a dosedependent manner in mice.175 Second, many MAO inhibitors lack selectivity and exert a myriad of effects. It is unclear what actions of these drugs influence nicotine-regulated behaviors in rats. The non-selective MAOA/B inhibitor tranylcypromine inhibits CYP2A6, the principle enzyme responsible for metabolizing nicotine into cotinine.176 Tranylcypromine and clorgyline inhibit monoamine uptake in various brain regions.177–180 This action of these inhibitors is likely to contribute to the potentiation of nicotine self-administration, as a dopamine transporter inhibitor has been shown to enhance nicotineinduced behavior.181 Clorgyline also binds to the s-opioid receptor.182–184 This is problematic because the s-opioid receptor is involved in some behavioral actions of nicotine.185


Constitutional basis for nicotine dependence Exposure to nicotine does not necessarily result in smoking. Even after smoking has become established, individuals exhibit varying degrees of nicotine dependence. Individuals who develop dependence are not necessarily those who happened to be exposed to a large amount of nicotine for a long period of time; those who develop nicotine dependence often exhibit pre-existing traits. Human and mouse studies suggest that molecular variation provides a basis for both pre-existing behavioral traits and susceptibility and resilience to elements of nicotine dependence. One way to understand the complex picture of nicotine dependence is to hypothesize that nicotine acts to direct pre-existing behavioral traits toward nicotine or to provide some beneficial effects for the host (Figure 2). According to this constitutional view, nicotine dependence is determined in part by preexisting molecular variation that provides a basis for quantitative variance in behavioral traits. However, pre-existing traits are unlikely to be all-or-none determinants for nicotine dependence; it is more probable that they probabilistically increase the chances of developing nicotine dependence. Pre-existing traits may confer susceptibility through various modalities. Smoking may become a target of some pre-existing behavioral traits (see Figure 2, redirected target mode). Compared to other age groups, adolescents tend to seek novel sensations and cigarettes and smoking might be perceived as novel. A pre-existing automatic and stereotypical behavioral pattern also might be directed toward smoking. A possible gene that contributes to behavioral choice-melioration, a condition in which smokers are unwilling to give up cigarettes even when faced with negative consequences (for example, illness), is an allele linked to the dopamine D2 receptor. Healthy volunteers with the A1 allele are impaired in their general ability to learn from negative consequences.186 The A1 allele linked to the dopamine D2 receptor gene is associated with current smoking, as well.96 In other instances, nicotine may be perceived as therapeutic for an individual’s pre-existing motivational or affective defects (see Figure 2, defect repair mode). Nicotine may induce this effect by normalizing negative affective states. Alternatively, nicotine may induce some subjective effects that counteract negative affective states. Affective abnormalities such as depressive symptoms, high levels of anxiety and altered stress reactivity exist before smoking starts in a subpopulation of smokers.77 The ability of antidepressants to reduce smoking and aid smoking cessation187 is consistent with this mode. Alternatively, molecular variation may independently influence pre-existing behavioral traits and nicotine dependence (see Figure 2, independent influence).79 The plasticity-based dependence model posits that addictive substances induce behavioral dependence (that is, addiction) because they cause pathologically


661

Figure 2 Three possible modes of the influence of molecular variation on nicotine dependence. Molecular variation, which involves altered levels or function of a molecule, is caused by genetic variants or non-genetic factors such as developmental anomalies or environmental factors. Molecular variation could increase tendencies toward novelty seeking affiliative attachment, loss of control, cue reactivity, decision-making deficits and automaticity. After exposure, nicotine could serve as a new target for these traits (re-directed target mode). Alternatively, molecular variation could set a basal tone of affect (for example, negative affect, depression, anxiety and stress vulnerability). Nicotine then could provide a ‘therapeutic’ or ‘repair’ function and may potentiate the intensity of negative reinforcement (defect repair mode). Molecular variation also could independently influence behavioral traits and susceptibility or resilience to nicotine dependence (shared, independent influence).

persistent molecular and cellular alterations that, in a normal form, serve to establish long-term associative memories.188 Rodent studies have provided an ample body of evidence that addictive substances, including nicotine, modulate many molecules that normally subserve learning-related events in the rodent brain.188–190 The plasticity-based hypothesis of addiction/dependence provides a plausible explanation as to why nicotine consolidates cue reactivity or promotes self-administration, but it does not explain why such processes widely differ among individuals. The ultimate evidence would be to demonstrate that such plastic alterations actually occur in the brains of heavy smokers, but not in chippers or nonsmokers, as a causal event for the development of nicotine dependence. Needless to say, it is technically difficult to demonstrate drug-induced molecular alterations as a causal event in humans. Demonstration of altered levels or activities of molecules in the postmortem brains of current and former smokers, compared to non-smokers, could suggest that such alterations are induced by smoking and are longlasting. However, equally plausible is an alternative interpretation that pre-existing molecular levels or activities increase the probability of initiating smoking. Levels or activities of molecules that are different in smokers than in former smokers and non-smokers could be equally interpreted as a suggestion that former smokers were able to quit because they lacked

the pre-existing molecular substrates for addiction or that nicotine only transiently induces these molecular alterations in former smokers. Functional imaging might provide an alternative, reliable way to obtain correlative evidence that altered responsiveness of regions in the brain predates or follows the initiation of drug exposure in both humans and experimental animals.191 Setting the dividing line between smokers and non-smokers is another difficult issue in determining the molecular correlates of nicotine dependence in humans. Artificially drawing a line between smokers and non-smokers would create a barrier to revealing the underlying mechanisms. ‘Smokers’ may include heterogeneous samples, if distinct molecular substrates exist for heavy smokers, moderate smokers and chippers. Similarly, it is important to consider that non-smokers might include a combination of individuals with and without susceptibility factors. Nicotine dependence develops only if nicotine is available and one initiates smoking. Even if one is burdened with many susceptibility factors, nicotine dependence will not develop unless smoking is initiated. ‘Non-smokers’ might be heterogeneous and may not serve as a reliable control. Unless initial exposure to nicotine is equal among smokers and non-smokers, the molecular basis for susceptibility to nicotine dependence will be difficult to identify. This point is well illustrated by the seminal study Molecular Psychiatry


662

that demonstrated that the genetic contribution to tobacco consumption was more prominently revealed when historical social restrictions in Sweden changed to allow more women to smoke.192 One possible difficulty associated with the constitutional hypothesis is that cue reactivity or cue exposure-associative processes occur only after exposure to nicotine and cues, and therefore, these processes cannot be a pre-existing trait. However, pre-existing molecular variation could potentially influence the response to nicotine, the general capability to form cue association or both. Consistent with this possibility, smokers with the A1 allele of the dopamine D2 receptor exhibit stronger cue-induced cravings than those without the allele, even though these two genotype groups are indistinguishable in the number of cigarettes smoked per day, years of smoking and levels of nicotine dependence.193 This study suggests that pre-existing molecular variation could selectively affect the capability of cue reactivity without altering the direct effects of nicotine. Moreover, inbred rat strains that differ in susceptibility to behavioral alterations induced by nicotine exhibit differences in basal levels of many molecules in the brain, suggesting that pre-existing molecular variation could alter the behavioral effects of nicotine.194–196 Alternatively, both pre-existing molecular variation and induced alterations might determine the ultimate degree of dependence. Individual variations in the susceptibility to dependence and pre-existing traits are not unique to nicotine dependence and are also seen with stimulants, opiates and alcohol dependence.138,197–200 However, any given molecule could exert different effects in response to diverse addictive substances. For example, the constitutive absence of FosB or CREB renders mice more sensitive to cocaine, but less sensitive to nicotine in the place-conditioning paradigm.109,143,201,202 A future challenge is to evaluate how each molecule contributes to pre-existing traits and the susceptibility and resilience to substance dependence. Such an endeavor will hopefully result in the identification of appropriate molecular targets for medication to more effectively prevent substance dependence disorders.

Acknowledgments This article is dedicated to the memory of T Klein, who inspired the first author’s research direction. We thank Drs TB Baker and ME Piper for sharing unpublished data and Drs Justin Cho and Soh Agatsuma for their critical comments on an early draft of this article. The preparation of this article was supported by Grants R01 DA013232 and R01 DA024330 from the National Institute on Drug Abuse (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse or the National Institutes of Health. Molecular Psychiatry

References 1 World Health Organization. Tobacco: Deadly in any Form and Disguise. World Health Organization, WHO Press: Geneva, Switzerland, 2006. 2 Centers for Disease Control and Prevention. Symptoms of substance dependence associated with use of cigarettes, alcohol, and illicit drugs—United Sates, 1991–1992. MMWR 1995; 44: 831–839. 3 Hughes J, Stead L, Lancaster T. Antidepressants for smoking cessation. Cochrane Database Syst Rev 2004; 4: CD000031. 4 Stead LF, Perera R, Bullen C, Mant D, Lancaster T. Nicotine replacement therapy for smoking cessation. Cochrane Database Syst Rev 2008; 1: CD000146. 5 Rose JE. Nicotine and nonnicotine factors in cigarette addiction. Psychopharmacology (Berlin) 2006; 184: 274–285. 6 Donny EC, Houtsmuller E, Stitzer ML. Smoking in the absence of nicotine: behavioral, subjective and physiological effects over 11 days. Addiction 2007; 102: 324–334. 7 Buchhalter AR, Acosta MC, Evans SE, Breland AB, Eissenberg T. Tobacco abstinence symptom suppression: the role played by the smoking-related stimuli that are delivered by denicotinized cigarettes. Addiction 2005; 100: 550–559. 8 Rohsenow DJ, Monti PM, Hutchison KE, Swift RM, MacKinnon SV, Sirota AD et al. High-dose transdermal nicotine and naltrexone: effects on nicotine withdrawal, urges, smoking, and effects of smoking. Exp Clin Psychopharmacol 2007; 15: 81–92. 9 Rollema H, Coe JW, Chambers LK, Hurst RS, Stahl SM, Williams KE. Rationale, pharmacology and clinical efficacy of partial agonists of alpha4beta2 nACh receptors for smoking cessation. Trends Pharmacol Sci 2007; 28: 316–325. 10 Cahill K, Stead LF, Lancaster T. Nicotine receptor partial agonists for smoking cessation. Cochrane Database Syst Rev 2008; 3: CD006103. 11 Harvey DM, Yasar S, Heishman SJ, Panlilio LV, Henningfield JE, Goldberg SR. Nicotine serves as an effective reinforcer of intravenous drug-taking behavior in human cigarette smokers. Psychopharmacology (Berlin) 2004; 175: 134–142. 12 Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerstrom test for nicotine dependence: a revision of the Fagerstrom tolerance questionnaire. Br J Addict 1991; 86: 1119–1127. 13 Hughes JR, Helzer JE, Lindberg SA. Prevalence of DSM/ICDdefined nicotine dependence. Drug Alcohol Depend 2006; 85: 91–102. 14 Moolchan ET, Radzius A, Epstein DH, Uhl G, Gorelick DA, Cadet JL et al. The Fagerstrom test for nicotine dependence and the Diagnostic Interview Schedule: do they diagnose the same smokers? Addict Behav 2002; 27: 101–113. 15 Breslau N, Johnson EO. Predicting smoking cessation and major depression in nicotine-dependent smokers. Am J Public Health 2000; 90: 1122–1127. 16 Piper ME, McCarthy DE, Bolt DM, Smith SS, Lerman C, Benowitz N et al. Assessing dimensions of nicotine dependence: an evaluation of the Nicotine Dependence Syndrome Scale (NDSS) and the Wisconsin Inventory of Smoking Dependence Motives (WISDM). Nicotine Tob Res 2008; 10: 1009–1020. 17 Tiffany ST, Conklin CA, Shiffman S, Clayton RR. What can dependence theories tell us about assessing the emergence of tobacco dependence? Addiction 2004; 99(Suppl 1): 78–86. 18 Piper ME, Piasecki TM, Federman EB, Bolt DM, Smith SS, Fiore MC et al. A multiple motives approach to tobacco dependence: the Wisconsin Inventory of Smoking Dependence Motives (WISDM-68). J Consul Clin Psychol 2004; 72: 139–154. 19 Centers for Disease Control and Prevention. Cigarette Smoking Among Adults-United States, 2004. MMWR 2005; 54: 1121–1124. 20 Chassin L, Presson CC, Pitts SC, Sherman SJ. The natural history of cigarette smoking from adolescence to adulthood in a midwestern community sample: multiple trajectories and their psychosocial correlates. Health Psychol 2000; 19: 223–231. 21 Colder CR, Mehta P, Balanda K, Campbell RT, Mayhew KP, Stanton WR et al. Identifying trajectories of adolescent smoking:


22 23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

an application of latent growth mixture modeling. Health Psychol 2001; 20: 127–135. Soldz S, Cui X. Pathways through adolescent smoking: a 7-year longitudinal grouping analysis. Health Psychol 2002; 21: 495–504. White HR, Pandina RJ, Chen PH. Developmental trajectories of cigarette use from early adolescence into young adulthood. Drug Alcohol Depend 2002; 65: 167–178. White HR, Nagin D, Replogle E, Stouthamer-Loeber M. Racial differences in trajectories of cigarette use. Drug Alcohol Depend 2004; 76: 219–227. Audrain-McGovern J, Rodriguez D, Tercyak KP, Cuevas J, Rodgers K, Patterson F. Identifying and characterizing adolescent smoking trajectories. Cancer Epidemiol Biomarkers Prev 2004; 13: 2023– 2034. Orlando M, Tucker JS, Ellickson PL, Klein DJ. Developmental trajectories of cigarette smoking and their correlates from early adolescence to young adulthood. J Consult Clin Psychol 2004; 72: 400–410. Stanton WR, Flay BR, Colder CR, Mehta P. Identifying and predicting adolescent smokers’ developmental trajectories. Nicotine Tob Res 2004; 6: 843–852. Vitaro F, Wanner B, Brendgen M, Gosselin C, Gendreau PL. Differential contribution of parents and friends to smoking trajectories during adolescence. Addict Behav 2004; 29: 831–835. Abroms L, Simons-Morton B, Haynie DL, Chen R. Psychosocial predictors of smoking trajectories during middle and high school. Addiction 2005; 100: 852–861. Karp I, O’loughlin J, Paradis G, Hanley J, Difranza J. Smoking trajectories of adolescent novice smokers in a longitudinal study of tobacco use. Ann Epidemiol 2005; 15: 445–452. Brook JS, Pahl K, Ning Y. Peer and parental influences on longitudinal trajectories of smoking among African Americans and Puerto Ricans. Nicotine Tob Res 2006; 8: 639–651. Bernat DH, Erickson DJ, Widome R, Perry CL, Forster JL. Adolescent smoking trajectories: results from a population-based cohort study. J Adolesc Health 2008; 43: 334–340. Brook DW, Brook JS, Zhang C, Whiteman M, Cohen P, Finch SJ. Developmental trajectories of cigarette smoking from adolescence to the early thirties: personality and behavioral risk factors. Nicotine Tob Res 2008; 10: 1283–1291. Chassin L, Presson C, Seo DC, Sherman SJ, Macy J, Wirth RJ et al. Multiple trajectories of cigarette smoking and the intergenerational transmission of smoking: a multigenerational, longitudinal study of a midwestern community sample. Health Psychol 2008; 27: 819–828. Lessov-Schlaggar CN, Hops H, Brigham J, Hudmon KS, Andrews JA, Tildesley E et al. Adolescent smoking trajectories and nicotine dependence. Nicotine Tob Res 2008; 10: 341–351. Costello DM, Dierker LC, Jones BL, Rose JS. Trajectories of smoking from adolescence to early adulthood and their psychosocial risk factors. Health Psychol 2008; 27: 811–818. Kenford SL, Wetter DW, Welsch SK, Smith SS, Fiore MC, Baker TB. Progression of college-age cigarette samplers: what influences outcome. Addict Behav 2005; 30: 285–294. Zhu SH, Sun J, Hawkins S, Pierce J, Cummins S. A population study of low-rate smokers: quitting history and instability over time. Health Psychol 2003; 22: 245–252. Shiffman S, Paty JA, Kassel JD, Gnys M, Zettler-Segal M. Smoking behavior and smoking history of tobacco chippers. Exp Clin Psychopharmacol 1994; 2: 126–142. Shiffman S, Paty JA, Gnys M, Kassel JD, Elash C. Nicotine withdrawal in chippers and regular smokers: subjective and cognitive effects. Health Psychol 1995; 14: 301–309. Davies GM, Willner P, Morgan MJ. Smoking-related cues elicit craving in tobacco ‘chippers’: a replication and validation of the two-factor structure of the Questionnaire of Smoking Urges. Psychopharmacology (Berlin) 2000; 152: 334–342. Sayette MA, Wertz JM, Martin CS, Cohn JF, Perrott MA, Hobel J. Effects of smoking opportunity on cue-elicited urge: a facial coding analysis. Exp Clin Psychopharmacol 2003; 11: 218–227. Shiffman S, Paty J. Smoking patterns and dependence: contrasting chippers and heavy smokers. J Abnorm Psychol 2006; 115: 509–523.

44 Piasecki TM, Jorenby DE, Smith SS, Fiore MC, Baker TB. Smoking withdrawal dynamics: I. Abstinence distress in lapsers and abstainers. J Abnorm Psychol 2003; 112: 3–13. 45 McCarthy DE, Piasecki TM, Fiore MC, Baker TB. Life before and after quitting smoking: an electronic diary study. J Abnorm Psychol 2006; 115: 454–466. 46 Piasecki TM, Fiore MC, Baker TB. Profiles in discouragement: two studies of variability in the time course of smoking withdrawal symptoms. J Abnorm Psychol 1998; 107: 238–251. 47 Riggs NR, Chou CP, Li C, Pentz MA. Adolescent to emerging adulthood smoking trajectories: when do smoking trajectories diverge, and do they predict early adulthood nicotine dependence? Nicotine Tob Res 2007; 9: 1147–1154. 48 Lipkus IM, Barefoot JC, Williams RB, Siegler IC. Personality measures as predictors of smoking initiation and cessation in the UNC Alumni Heart Study. Health Psychol 1994; 13: 149–155. 49 Sher KJ, Bartholow BD, Wood MD. Personality and substance use disorders: a prospective study. J Consult Clin Psychol 2000; 68: 818–829. 50 Audrain-McGovern J, Rodriguez D, Patel V, Faith MS, Rodgers K, Cuevas J. How do psychological factors influence adolescent smoking progression? The evidence for indirect effects through tobacco advertising receptivity. Pediatrics 2006; 117: 1216–1225. 51 Masse LC, Tremblay RE. Behavior of boys in kindergarten and the onset of substance use during adolescence. Arch Gen Psychiatry 1997; 54: 62–68. 52 Griesler PC, Hu MC, Schaffran C, Kandel DB. Comorbidity of psychiatric disorders and nicotine dependence among adolescents: findings from a prospective, longitudinal study. J Am Acad Child Adolesc Psychiatry 2008; 47: 1340–1350. 53 Difranza JR, Savageau JA, Fletcher K, Pbert L, O’loughlin J, McNeill AD et al. Susceptibility to nicotine dependence: the development and assessment of nicotine dependence in Youth 2 study. Pediatrics 2007; 120: e974–e983. 54 Difranza JR, Savageau JA, Fletcher K, Pbert L, O’loughlin J, McNeill AD et al. Susceptibility to nicotine dependence: the development and assessment of nicotine dependence in youth 2 study. Pediatrics 2007; 120: e974–e983. 55 Hu MC, Muthen B, Schaffran C, Griesler PC, Kandel DB. Developmental trajectories of criteria of nicotine dependence in adolescence. Drug Alcohol Depend 2008; 98: 94–104. 56 Cloninger CR. Neurogenetic adaptive mechanisms in alcoholism. Science 1987; 236: 410–416. 57 Zuckerman M, Kuhlman DM. Personality and risk-taking: common biosocial factors. J Pers 2000; 68: 999–1029. 58 Zuckerman M, Cloninger CR. Relationships between Cloninger’s, Zuckerman’s and Eysenck’s dimensions of personality. Person Indiv Diff 1996; 21: 283–285. 59 Galera C, Fombonne E, Chastang JF, Bouvard M. Childhood hyperactivity-inattention symptoms and smoking in adolescence. Drug Alcohol Depend 2005; 78: 101–108. 60 Kollins SH, McClernon FJ, Fuemmeler BF. Association between smoking and attention-deficit/hyperactivity disorder symptoms in a population-based sample of young adults. Arch Gen Psychiatry 2005; 62: 1142–1147. 61 Fergusson DM, Horwood LJ. Early conduct problems and later life opportunities. J Child Psychol Psychiatry 1998; 39: 1097–1108. 62 Barkley RA, Fischer M, Edelbrock CS, Smallish L. The adolescent outcome of hyperactive children diagnosed by research criteria: I. An 8-year prospective follow-up study. J Am Acad Child Adolesc Psychiatry 1990; 29: 546–557. 63 Milberger S, Biederman J, Faraone SV, Chen L, Jones J. ADHD is associated with early initiation of cigarette smoking in children and adolescents. J Am Acad Child Adolesc Psychiatry 1997; 36: 37–44. 64 Lambert NM, Hartsough CS. Prospective study of tobacco smoking and substance dependencies among samples of ADHD and non-ADHD participants. J Learn Disabil 1998; 31: 533–544. 65 Molina BS, Pelham Jr WE. Childhood predictors of adolescent substance use in a longitudinal study of children with ADHD. J Abnorm Psychol 2003; 112: 497–507. 66 Rohde P, Kahler CW, Lewinsohn PM, Brown RA. Psychiatric disorders, familial factors, and cigarette smoking: II. Associations

663



664 67

68

69

70

71

72

73

74

75

76

77 78

79

80

81

82

83

84

85

86

with progression to daily smoking. Nicotine Tob Res 2004; 6: 119–132. Biederman J, Monuteaux MC, Mick E, Spencer T, Wilens TE, Silva JM et al. Young adult outcome of attention deficit hyperactivity disorder: a controlled 10-year follow-up study. Psychol Med 2006; 36: 167–179. Lerman C, Audrain J, Tercyak K, Hawk Jr LW, Bush A, Crystal-Mansour S et al. Attention-deficit hyperactivity disorder (ADHD) symptoms and smoking patterns among participants in a smoking-cessation program. Nicotine Tob Res 2001; 3: 353–359. Tercyak KP, Lerman C, Audrain J. Association of attentiondeficit/hyperactivity disorder symptoms with levels of cigarette smoking in a community sample of adolescents. J Am Acad Child Adolesc Psychiatry 2002; 41: 799–805. Rodriguez D, Tercyak KP, Audrain-McGovern J. Effects of inattention and hyperactivity/impulsivity symptoms on development of nicotine dependence from mid adolescence to young adulthood. J Pediatr Psychol 2008; 33: 563–575. Gehricke JG, Whalen CK, Jamner LD, Wigal TL, Steinhoff K. The reinforcing effects of nicotine and stimulant medication in the everyday lives of adult smokers with ADHD: a preliminary examination. Nicotine Tob Res 2006; 8: 37–47. Conners CK, Levin ED, Sparrow E, Hinton SC, Erhardt D, Meck WH et al. Nicotine and attention in adult attention deficit hyperactivity disorder (ADHD). Psychopharmacol Bull 1996; 32: 67–73. Potter AS, Newhouse PA. Effects of acute nicotine administration on behavioral inhibition in adolescents with attention-deficit/ hyperactivity disorder. Psychopharmacology (Berl) 2004; 176: 182–194. Potter AS, Newhouse PA. Acute nicotine improves cognitive deficits in young adults with attention-deficit/hyperactivity disorder. Pharmacol Biochem Behav 2008; 88: 407–417. Levin ED, Conners CK, Sparrow E, Hinton SC, Erhardt D, Meck WH et al. Nicotine effects on adults with attention-deficit/ hyperactivity disorder. Psychopharmacology (Berl) 1996; 123: 55–63. Levin ED, Conners CK, Silva D, Canu W, March J. Effects of chronic nicotine and methylphenidate in adults with attention deficit/hyperactivity disorder. Exp Clin Psychopharmacol 2001; 9: 83–90. Breslau N, Novak SP, Kessler RC. Psychiatric disorders and stages of smoking. Biol Psychiatry 2004; 55: 69–76. Di Franza JR, Savageau JA, Fletcher K, Pbert L, O’loughlin J, McNeill AD et al. Susceptibility to nicotine dependence: the development and assessment of nicotine dependence in youth 2 study. Pediatrics 2007; 120: e974–e983. Kendler KS, Neale MC, MacLean CJ, Heath AC, Eaves LJ, Kessler RC. Smoking and major depression. A causal analysis. Arch Gen Psychiatry 1993; 50: 36–43. Maes HH, Sullivan PF, Bulik CM, Neale MC, Prescott CA, Eaves LJ et al. A twin study of genetic and environmental influences on tobacco initiation, regular tobacco use and nicotine dependence. Psychol Med 2004; 34: 1251–1261. Li MD, Cheng R, Ma JZ, Swan GE. A meta-analysis of estimated genetic and environmental effects on smoking behavior in male and female adult twins. Addiction 2003; 98: 23–31. Xian H, Scherrer JF, Madden PA, Lyons MJ, Tsuang M, True WR et al. The heritability of failed smoking cessation and nicotine withdrawal in twins who smoked and attempted to quit. Nicotine Tob Res 2003; 5: 245–254. Madden PA, Heath AC, Pedersen NL, Kaprio J, Koskenvuo MJ, Martin NG. The genetics of smoking persistence in men and women: a multicultural study. Behav Genet 1999; 29: 423–431. True WR, Heath AC, Scherrer JF, Waterman B, Goldberg J, Lin N et al. Genetic and environmental contributions to smoking. Addiction 1997; 92: 1277–1287. Heath AC, Martin NG. Genetic models for the natural history of smoking: evidence for a genetic influence on smoking persistence. Addict Behav 1993; 18: 19–34. Carmelli D, Swan GE, Robinette D, Fabsitz R. Genetic influence on smoking—a study of male twins. N Engl J Med 1992; 327: 829–833.


87 Broms U, Silventoinen K, Madden PA, Heath AC, Kaprio J. Genetic architecture of smoking behavior: a study of Finnish adult twins. Twin Res Hum Genet 2006; 9: 64–72. 88 Pergadia ML, Heath AC, Martin NG, Madden PA. Genetic analyses of DSM-IV nicotine withdrawal in adult twins. Psychol Med 2006; 36: 963–972. 89 Swan GE, Hops H, Wilhelmsen KC, Lessov-Schlaggar CN, Cheng LS, Hudmon KS et al. A genome-wide screen for nicotine dependence susceptibility loci. Am J Med Genet B Neuropsychiatr Genet 2006; 141: 354–360. 90 Li MD, Payne TJ, Ma JZ, Lou XY, Zhang D, Dupont RT et al. A genomewide search finds major susceptibility loci for nicotine dependence on chromosome 10 in African Americans. Am J Hum Genet 2006; 79: 745–751. 91 Li MD, Ma JZ, Payne TJ, Lou XY, Zhang D, Dupont RT et al. Genome-wide linkage scan for nicotine dependence in European Americans and its converging results with African Americans in the Mid-South Tobacco Family sample. Mol Psychiatry 2008; 13: 407–416. 92 Bierut LJ, Madden PA, Breslau N, Johnson EO, Hatsukami D, Pomerleau OF et al. Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum Mol Genet 2007; 16: 24–35. 93 Uhl GR, Liu QR, Drgon T, Johnson C, Walther D, Rose JE. Molecular genetics of nicotine dependence and abstinence: whole genome association using 520 000 SNPs. BMC Genet 2007; 8: 10. 94 Uhl GR, Liu QR, Drgon T, Johnson C, Walther D, Rose JE et al. Molecular genetics of successful smoking cessation: convergent genome-wide association study results. Arch Gen Psychiatry 2008; 65: 683–693. 95 Arinami T, Ishiguro H, Onaivi ES. Polymorphisms in genes involved in neurotransmission in relation to smoking. Eur J Pharmacol 2000; 410: 215–226. 96 Li MD, Ma JZ, Beuten J. Progress in searching for susceptibility loci and genes for smoking-related behaviour. Clin Genet 2004; 66: 382–392. 97 Berrettini WH, Lerman CE. Pharmacotherapy and pharmacogenetics of nicotine dependence. Am J Psychiatry 2005; 162: 1441–1451. 98 Schnoll RA, Johnson TA, Lerman C. Genetics and smoking behavior. Curr Psychiatry Rep 2007; 9: 349–357. 99 Lessov CN, Swan GE, Ring HZ, Khroyan TV, Lerman C. Genetics and drug use as a complex phenotype. Subst Use Misuse 2004; 39: 1515–1569. 100 Ebstein RP. The molecular genetic architecture of human personality: beyond self-report questionnaires. Mol Psychiatry 2006; 11: 427–445. 101 Lusher JM, Chandler C, Ball D. Dopamine D4 receptor gene (DRD4) is associated with Novelty Seeking (NS) and substance abuse: the saga continues. Mol Psychiatry 2001; 6: 497–499. 102 Brookes K, Xu X, Chen W, Zhou K, Neale B, Lowe N et al. The analysis of 51 genes in DSM-IV combined type attention deficit hyperactivity disorder: association signals in DRD4, DAT1 and 16 other genes. Mol Psychiatry 2006; 11: 934–953. 103 Thapar A, Langley K, Fowler T, Rice F, Turic D, Whittinger N et al. Catechol O-methyltransferase gene variant and birth weight predict early-onset antisocial behavior in children with attentiondeficit/hyperactivity disorder. Arch Gen Psychiatry 2005; 62: 1275–1278. 104 Caspi A, Langley K, Milne B, Moffitt TE, O’Donovan M, Owen MJ et al. A replicated molecular genetic basis for subtyping antisocial behavior in children with attentiondeficit/hyperactivity disorder. Arch Gen Psychiatry 2008; 65: 203–210. 105 Malmberg K, Wargelius HL, Lichtenstein P, Oreland L, Larsson JO. ADHD and disruptive behavior scores—associations with MAO-A and 5-HTT genes and with platelet MAO-B activity in adolescents. BMC Psychiatry 2008; 8: 28. 106 Williamson PR, Gamble C, Altman DG, Hutton JL. Outcome selection bias in meta-analysis. Stat Methods Med Res 2005; 14: 515–524. 107 Herbst JH, Zonderman AB, McCrae RR, Costa Jr PT. Do the dimensions of the temperament and character inventory map a


108

109

110

111 112

113 114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

simple genetic architecture? Evidence from molecular genetics and factor analysis. Am J Psychiatry 2000; 157: 1285–1290. Kliethermes CL, Crabbe JC. Genetic independence of mouse measures of some aspects of novelty seeking. Proc Natl Acad Sci USA 2006; 103: 5018–5023. Zhu H, Lee M, Agatsuma S, Hiroi N. Pleiotropic impact of constitutive fosB inactivation on nicotine-induced behavioral alterations and stress-related traits in mice. Hum Mol Genet 2007; 16: 820–836. Gourlay SG, Benowitz NL. Arteriovenous differences in plasma concentration of nicotine and catecholamines and related cardiovascular effects after smoking, nicotine nasal spray, and intravenous nicotine. Clin Pharmacol Ther 1997; 62: 453–463. Benowitz NL. Nicotine addiction. Prim Care 1999; 26: 611–631. Rose JE, Behm FM, Westman EC, Coleman RE. Arterial nicotine kinetics during cigarette smoking and intravenous nicotine administration: implications for addiction. Drug Alcohol Depend 1999; 56: 99–107. White NM, Hiroi N. Amphetamine cue preference and the neurobiology of drug-seeking. Semin Neurosci 1993; 5: 329–336. Bardo MT, Rowlett JK, Harris MJ. Conditioned place preference using opiate and stimulant drugs: a meta-analysis. Neurosci Biobehav Rev 1995; 19: 39–51. Bardo MT, Bevins RA. Conditioned place preference: what does it add to our preclinical understanding of drug reward? Psychopharmacology (Berl) 2000; 153: 31–43. Droungas A, Ehrman RN, Childress AR, O’Brien CP. Effect of smoking cues and cigarette availability on craving and smoking behavior. Addict Behav 1995; 20: 657–673. Shiffman S, Paty JA, Gnys M, Kassel JA, Hickcox M. First lapses to smoking: within-subjects analysis of real-time reports. J Consult Clin Psychol 1996; 64: 366–379. Hogarth L, Duka T. Human nicotine conditioning requires explicit contingency knowledge: is addictive behaviour cognitively mediated? Psychopharmacology (Berl) 2006; 184: 553–566. Lazev AB, Herzog TA, Brandon TH. Classical conditions of environmental cues to cigarette smoking. Exp Clin Psychopharmacol 1999; 7: 56–63. Shiffman S, Gnys M, Richards TJ, Paty JA, Hickcox M, Kassel JD. Temptations to smoke after quitting: a comparison of lapsers and maintainers. Health Psychol 1996; 15: 455–461. American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text revision (DSM-IVTR). Washington, DC, American Psychiatric Association, 2000. Hughes JR, Gust SW, Skoog K, Keenan RM, Fenwick JW. Symptoms of tobacco withdrawal. A replication and extension. Arch Gen Psychiatry 1991; 48: 52–59. Piasecki TM, Niaura R, Shadel WG, Abrams D, Goldstein M, Fiore MC et al. Smoking withdrawal dynamics in unaided quitters. J Abnorm Psychol 2000; 109: 74–86. Jackson KJ, Martin BR, Changeux JP, Damaj MI. Differential role of nicotinic acetylcholine receptor subunits in physical and affective nicotine withdrawal signs. J Pharmacol Exp Ther 2008; 325: 302–312. Merritt LL, Martin BR, Walters C, Lichtman AH, Damaj MI. The endogenous cannabinoid system modulates nicotine reward and dependence. J Pharmacol Exp Ther 2008; 326: 483–492. Flint J, Valdar W, Shifman S, Mott R. Strategies for mapping and cloning quantitative trait genes in rodents. Nat Rev Genet 2005; 6: 271–286. Matsumoto M, Straub RE, Marenco S, Nicodemus KK, Matsumoto S, Fujikawa A et al. The evolutionarily conserved G proteincoupled receptor SREB2/GPR85 influences brain size, behavior, and vulnerability to schizophrenia. Proc Natl Acad Sci USA 2008; 105: 6133–6138. Papaleo F, Crawley JN, Song J, Lipska BK, Pickel J, Weinberger DR et al. Genetic dissection of the role of catechol-O-methyltransferase in cognition and stress reactivity in mice. J Neurosci 2008; 28: 8709–8723. Lai WS, Xu B, Westphal KG, Paterlini M, Olivier B, Pavlidis P et al. Akt1 deficiency affects neuronal morphology and predisposes to abnormalities in prefrontal cortex functioning. Proc Natl Acad Sci USA 2006; 103: 16906–16911.

130 Mickey BJ, Ducci F, Hodgkinson CA, Langenecker SA, Goldman D, Zubieta JK. Monoamine oxidase A genotype predicts human serotonin 1A receptor availability in vivo. J Neurosci 2008; 28: 11354–11359. 131 Berrendero F, Mendizabal V, Robledo P, Galeote L, Bilkei-Gorzo A, Zimmer A et al. Nicotine-induced antinociception, rewarding effects, and physical dependence are decreased in mice lacking the preproenkephalin gene. J Neurosci 2005; 25: 1103–1112. 132 Berrendero F, Kieffer BL, Maldonado R. Attenuation of nicotineinduced antinociception, rewarding effects, and dependence in mu-opioid receptor knock-out mice. J Neurosci 2002; 22: 10935–10940. 133 Castane A, Valjent E, Ledent C, Parmentier M, Maldonado R, Valverde O. Lack of CB1 cannabinoid receptors modifies nicotine behavioural responses, but not nicotine abstinence. Neuropharmacology 2002; 43: 857–867. 134 Castane A, Soria G, Ledent C, Maldonado R, Valverde O. Attenuation of nicotine-induced rewarding effects in A(2A) knockout mice. Neuropharmacology 2006; 51: 631–640. 135 Picciotto MR, Zoli M, Rimondini R, Lena C, Marubio LM, Pich EM et al. Acetylcholine receptors containing the beta2 subunit are involved in the reinforcing properties of nicotine. Nature 1998; 391: 173–177. 136 Tapper AR, McKinney SL, Nashmi R, Schwarz J, Deshpande P, Labarca C et al. Nicotine activation of alpha4* receptors: sufficient for reward, tolerance, and sensitization. Science 2004; 306: 1029–1032. 137 Maskos U, Molles BE, Pons S, Besson M, Guiard BP, Guilloux JP et al. Nicotine reinforcement and cognition restored by targeted expression of nicotinic receptors. Nature 2005; 436: 103–107. 138 Hiroi N, Agatsuma S. Genetic susceptibility to substance dependence. Mol Psychiatry 2005; 10: 336–344. 139 Chao J, Nestler EJ. Molecular neurobiology of drug addiction. Annu Rev Med 2004; 55: 113–132. 140 Hiroi N, Marek GJ, Brown JR, Ye H, Saudou F, Vaidya VA et al. Essential role of the fosB gene in molecular, cellular, and behavioral actions of chronic electroconvulsive seizures. J Neurosci 1998; 18: 6952–6962. 141 Perrotti LI, Hadeishi Y, Ulery PG, Barrot M, Monteggia L, Duman RS et al. Induction of delta FosB in reward-related brain structures after chronic stress. J Neurosci 2004; 24: 10594–10602. 142 Carlezon Jr WA, Duman RS, Nestler EJ. The many faces of CREB. Trends Neurosci 2005; 28: 436–445. 143 Walters CL, Cleck JN, Kuo YC, Blendy JA. Mu-opioid receptor and CREB activation are required for nicotine reward. Neuron 2005; 46: 933–943. 144 Ray R, Jepson C, Wileyto P, Patterson F, Strasser AA, Rukstalis M et al. CREB1 haplotypes and the relative reinforcing value of nicotine. Mol Psychiatry 2007; 12: 615–617. 145 Edelstein SB, Castiglione CM, Breakfield XO. Monoamine oxidase activity in normal and Lesch-Nyhan fibroblasts. J Neurochem 1978; 31: 1247–1254. 146 Hotamisligil GS, Breakefield XO. Human monoamine oxidase A gene determines levels of enzyme activity. Am J Hum Genet 1991; 49: 383–392. 147 Tivol EA, Shalish C, Schuback DE, Hsu YP, Breakefield XO. Mutational analysis of the human MAOA gene. Am J Med Genet 1996; 67: 92–97. 148 Schuback DE, Mulligan EL, Sims KB, Tivol EA, Greenberg BD, Chang SF et al. Screen for MAOA mutations in target human groups. Am J Med Genet 1999; 88: 25–28. 149 Balciuniene J, Emilsson L, Oreland L, Pettersson U, Jazin E. Investigation of the functional effect of monoamine oxidase polymorphisms in human brain. Hum Genet 2002; 110: 1–7. 150 Castro Costa MR, Edelstein SB, Castiglione CM, Chao H, Breakefield XO. Properties of monoamine oxidase in control and Lesch-Nyhan fibroblasts. Biochem Genet 1980; 18: 577–590. 151 Sabol SZ, Hu S, Hamer D. A functional polymorphism in the monoamine oxidase A gene promoter. Hum Genet 1998; 103: 273–279. 152 Deckert J, Catalano M, Syagailo YV, Bosi M, Okladnova O, DiBella D et al. Excess of high activity monoamine oxidase A gene promoter alleles in female patients with panic disorder. Hum Mol Genet 1999; 8: 621–624.

665



666

153 Denney RM, Koch H, Craig IW. Association between monoamine oxidase A activity in human male skin fibroblasts and genotype of the MAOA promoter-associated variable number tandem repeat. Hum Genet 1999; 105: 542–551. 154 Fowler JS, Alia-Klein N, Kriplani A, Logan J, Williams B, Zhu W et al. Evidence that brain MAO A activity does not correspond to MAO A genotype in healthy male subjects. Biol Psychiatry 2007; 62: 355–358. 155 McKinney EF, Walton RT, Yudkin P, Fuller A, Haldar NA, Mant D et al. Association between polymorphisms in dopamine metabolic enzymes and tobacco consumption in smokers. Pharmacogenetics 2000; 10: 483–491. 156 Ito H, Hamajima N, Matsuo K, Okuma K, Sato S, Ueda R et al. Monoamine oxidase polymorphisms and smoking behaviour in Japanese. Pharmacogenetics 2003; 13: 73–79. 157 Jin Y, Chen D, Hu Y, Guo S, Sun H, Lu A et al. Association between monoamine oxidase gene polymorphisms and smoking behaviour in Chinese males. Int J Neuropsychopharmacol 2005; 9: 557–564. 158 Johnstone EC, Clark TG, Griffiths SE, Murphy MF, Walton RT. Polymorphisms in dopamine metabolic enzymes and tobacco consumption in smokers: seeking confirmation of the association in a follow-up study. Pharmacogenetics 2002; 12: 585–587. 159 Huang S, Cook DG, Hinks LJ, Chen XH, Ye S, Gilg JA et al. CYP2A6, MAOA, DBH, DRD4, and 5HT2A genotypes, smoking behaviour and cotinine levels in 1518 UK adolescents. Pharmacogenet Genomics 2005; 15: 839–850. 160 Agatsuma S, Lee M, Zhu H, Chen K, Shih JC, Seif I et al. Monoamine oxidase A knockout mice exhibit impaired nicotine preference but normal responses to novel stimuli. Hum Mol Genet 2006; 15: 2721–2731. 161 Lee M, Chen K, Shih JC, Hiroi N. MAO-B knockout mice exhibit deficient habituation of locomotor activity but normal nicotine intake. Genes Brain Behav 2004; 3: 216–227. 162 Shih JC, Chen K, Ridd MJ. Monoamine oxidase: from genes to behavior. Annu Rev Neurosci 1999; 22: 197–217. 163 Carr LA, Basham JK. Effects of tobacco smoke constituents on MPTP-induced toxicity and monoamine oxidase activity in the mouse brain. Life Sci 1991; 48: 1173–1177. 164 Khalil AA, Steyn S, Castagnoli Jr N. Isolation and characterization of a monoamine oxidase inhibitor from tobacco leaves. Chem Res Toxicol 2000; 13: 31–35. 165 Hauptmann N, Shih JC. 2-Naphthylamine, a compound found in cigarette smoke, decreases both monoamine oxidase A and B catalytic activity. Life Sci 2001; 68: 1231–1241. 166 Herraiz T, Chaparro C. Human monoamine oxidase is inhibited by tobacco smoke: beta-carboline alkaloids act as potent and reversible inhibitors. Biochem Biophys Res Commun 2005; 326: 378–386. 167 Fowler JS, Volkow ND, Wang GJ, Pappas N, Logan J, Shea C et al. Brain monoamine oxidase A inhibition in cigarette smokers. Proc Natl Acad Sci USA 1996; 93: 14065–14069. 168 Guillem K, Vouillac C, Azar MR, Parsons LH, Koob GF, Cador M et al. Monoamine oxidase inhibition dramatically increases the motivation to self-administer nicotine in rats. J Neurosci 2005; 25: 8593–8600. 169 Villegier AS, Salomon L, Granon S, Changeux JP, Belluzzi JD, Leslie FM et al. Monoamine oxidase inhibitors allow locomotor and rewarding responses to nicotine. Neuropsychopharmacology 2006; 31: 1704–1713. 170 Guillem K, Vouillac C, Azar MR, Parsons LH, Koob GF, Cador M et al. Monoamine oxidase A rather than monoamine oxidase B inhibition increases nicotine reinforcement in rats. Eur J Neurosci 2006; 24: 3532–3540. 171 Berlin I, Said S, Spreux-Varoquaux O, Launay JM, Olivares R, Millet V et al. A reversible monoamine oxidase A inhibitor (moclobemide) facilitates smoking cessation and abstinence in heavy, dependent smokers. Clin Pharmacol Ther 1995; 58: 444–452. 172 Houtsmuller EJ, Thornton JA, Stitzer ML. Effects of selegiline (Ldeprenyl) during smoking and short-term abstinence. Psychopharmacology (Berl) 2002; 163: 213–220. 173 George TP, Vessicchio JC, Termine A, Jatlow PI, Kosten TR, O’Malley SS. A preliminary placebo-controlled trial of selegiline


174

175

176

177

178

179

180

181

182

183

184

185

186

187 188

189

190

191

192

193

hydrochloride for smoking cessation. Biol Psychiatry 2003; 53: 136–143. Biberman R, Neumann R, Katzir I, Gerber Y. A randomized controlled trial of oral selegiline plus nicotine skin patch compared with placebo plus nicotine skin patch for smoking cessation. Addiction 2003; 98: 1403–1407. Popova NK, Vishnivetskaya GB, Ivanova EA, Skrinskaya JA, Seif I. Altered behavior and alcohol tolerance in transgenic mice lacking MAO A: a comparison with effects of MAO A inhibitor clorgyline. Pharmacol Biochem Behav 2000; 67: 719–727. Zhang W, Kilicarslan T, Tyndale RF, Sellers EM. Evaluation of methoxsalen, tranylcypromine, and tryptamine as specific and selective CYP2A6 inhibitors in vitro. Drug Metab Dispos 2001; 29: 897–902. Azzaro AJ, Demarest KT. Inhibitory effects of type A and type B monoamine oxidase inhibitors on synaptosomal accumulation of [3H]dopamine: a reflection of antidepressant potency. Biochem Pharmacol 1982; 31: 2195–2197. Lai JC, Leung TK, Guest JF, Lim L, Davison AN. The monoamine oxidase inhibitors clorgyline and L-deprenyl also affect the uptake of dopamine, noradrenaline and serotonin by rat brain synaptosomal preparations. Biochem Pharmacol 1980; 29: 2763–2767. Moron JA, Perez V, Fernandez-Alvarez E, Marco JL, Unzeta M. ‘In vitro’ effect of some 5-hydroxy-indolalkylamine derivatives on monoamine uptake system. J Neural Transm Suppl 1998; 52: 343–349. Tekes K, Magyar K. Effect of MAO inhibitors on the high-affinity reuptake of biogenic amines in rat subcortical regions. Neurobiology (Bp) 2000; 8: 257–264. Janhunen S, Mielikainen P, Paldanius P, Tuominen RK, Ahtee L, Kaakkola S. The effect of nicotine in combination with various dopaminergic drugs on nigrostriatal dopamine in rats. Naunyn Schmiedebergs Arch Pharmacol 2005; 371: 480–491. Itzhak Y, Kassim CO. Clorgyline displays high affinity for sigma binding sites in C57BL/6 mouse brain. Eur J Pharmacol 1990; 176: 107–108. Itzhak Y, Stein I, Zhang SH, Kassim CO, Cristante D. Binding of sigma-ligands to C57BL/6 mouse brain membranes: effects of monoamine oxidase inhibitors and subcellular distribution studies suggest the existence of sigma-receptor subtypes. J Pharmacol Exp Ther 1991; 257: 141–148. Seth P, Fei YJ, Li HW, Huang W, Leibach FH, Ganapathy V. Cloning and functional characterization of a sigma receptor from rat brain. J Neurochem 1998; 70: 922–931. Horan B, Gardner EL, Dewey SL, Brodie JD, Ashby Jr CR. The selective sigma(1) receptor agonist, 1-(3,4-dimethoxyphenethyl)4-(phenylpropyl)piperazine (SA4503), blocks the acquisition of the conditioned place preference response to ( )-nicotine in rats. Eur J Pharmacol 2001; 426: R1–R2. Klein TA, Neumann J, Reuter M, Hennig J, von Cramon DY, Ullsperger M. Genetically determined differences in learning from errors. Science 2007; 318: 1642–1645. Hughes JR, Stead LF, Lancaster T. Antidepressants for smoking cessation. Cochrane Database Syst Rev 2007; 1: CD000031. Hyman SE, Malenka RC, Nestler EJ. Neural mechanisms of addiction: the role of reward-related learning and memory. Annu Rev Neurosci 2006; 29: 565–598. Brunzell DH, Russell DS, Picciotto MR. In vivo nicotine treatment regulates mesocorticolimbic CREB and ERK signaling in C57Bl/6J mice. J Neurochem 2003; 84: 1431–1441. Steiner RC, Heath CJ, Picciotto MR. Nicotine-induced phosphorylation of ERK in mouse primary cortical neurons: evidence for involvement of glutamatergic signaling and CaMKII. J Neurochem 2007; 103: 666–678. Dalley JW, Fryer TD, Brichard L, Robinson ES, Theobald DE, Laane K et al. Nucleus accumbens D2/3 receptors predict trait impulsivity and cocaine reinforcement. Science 2007; 315: 1267–1270. Kendler KS, Thornton LM, Pedersen NL. Tobacco consumption in Swedish twins reared apart and reared together. Arch Gen Psychiatry 2000; 57: 886–892. Erblich J, Lerman C, Self DW, Diaz GA, Bovbjerg DH. Effects of dopamine D2 receptor (DRD2) and transporter (SLC6A3)


194

195

196

197

polymorphisms on smoking cue-induced cigarette craving among African-American smokers. Mol Psychiatry 2005; 10: 407–414. Horan B, Smith M, Gardner EL, Lepore M, Ashby Jr CR. Nicotine produces conditioned place preference in Lewis, but not Fischer 344 rats. Synapse 1997; 26: 93–94. Haile CN, Hiroi N, Nestler EJ, Kosten TA. Differential behavioral responses to cocaine are associated with dynamics of mesolimbic dopamine proteins in Lewis and Fischer 344 rats. Synapse 2001; 41: 179–190. Brower VG, Fu Y, Matta SG, Sharp BM. Rat strain differences in nicotine self-administration using an unlimited access paradigm. Brain Res 2002; 930: 12–20. Piazza PV, Le Moal ML. Pathophysiological basis of vulnerability to drug abuse: role of an interaction between stress, glucocorticoids, and dopaminergic neurons. Annu Rev Pharmacol Toxicol 1996; 36: 359–378.

198 Kreek MJ, Nielsen DA, Butelman ER, LaForge KS. Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nat Neurosci 2005; 8: 1450–1457. 199 Paulus MP. Decision-making dysfunctions in psychiatry—altered homeostatic processing? Science 2007; 318: 602–606. 200 Goldman D, Oroszi G, Ducci F. The genetics of addictions: uncovering the genes. Nat Rev Genet 2005; 6: 521–532. 201 Hiroi N, Brown JR, Haile CN, Ye H, Greenberg ME, Nestler EJ. FosB mutant mice: loss of chronic cocaine induction of Fos-related proteins and heightened sensitivity to cocaine’s psychomotor and rewarding effects. Proc Natl Acad Sci USA 1997; 94: 10397–10402. 202 Walters CL, Blendy JA. Different requirements for cAMP response element binding protein in positive and negative reinforcing properties of drugs of abuse. J Neurosci 2001; 21: 9438–9444.

667
