Abstract
Twin and family studies reveal a significant genetic contribution to the risk of smoking initiation and progression (SI/P), nicotine dependence (ND), and smoking cessation (SC). Further, numerous genes have been implicated in these smoking-related behaviors, especially for ND. However, no study has presented a comprehensive and systematic view of the genetic factors associated with these important smoking-related phenotypes. By reviewing the literature on these behaviors, we identified 16, 99, and 75 genes that have been associated with SI/P, ND, and SC, respectively. We then determined whether these genes were enriched in pathways important in the neuronal and brain functions underlying addiction. We identified 9, 21, and 13 pathways enriched in the genes associated with SI/P, ND, and SC, respectively. Among these pathways, four were common to all of the three phenotypes, that is, calcium signaling, cAMP-mediated signaling, dopamine receptor signaling, and G-protein-coupled receptor signaling. Further, we found that serotonin receptor signaling and tryptophan metabolism pathways were shared by SI/P and ND, tight junction signaling pathway was shared by SI/P and SC, and gap junction, neurotrophin/TRK signaling, synaptic long-term potentiation, and tyrosine metabolism were shared between ND and SC. Together, these findings show significant genetic overlap among these three related phenotypes. Although identification of susceptibility genes for smoking-related behaviors is still in an early stage, the approach used in this study has the potential to overcome the hurdles caused by factors such as genetic heterogeneity and small sample size, and thus should yield greater insights into the genetic mechanisms underlying these complex phenotypes.
Similar content being viewed by others
INTRODUCTION
Cigarette smoking is the most common form of tobacco use (Smith and Fiore, 1999), and is one of the most significant sources of morbidity and death worldwide (Murray, 2006). In the United States, more than 20% of adults are current smokers (CDC, 2007), and cigarette smoking is responsible for ∼438 000 premature deaths and an estimated economic cost of $167 billion annually (CDC, 2005b). In addition, about 20% of high school students (CDC, 2006) and 8% of middle school students (CDC, 2005a) in the United States smoke. Moreover, every day, about 4000 teenagers in the United States initiate cigarette smoking, and more than 1000 of them may become daily cigarette smokers (SAMHSA, 2006). Although a large fraction of smokers try to quit (CDC, 2007), available treatments are effective for only a fraction of them (Hughes et al, 2007; Lerman et al, 2007). Thus, developing therapeutic approaches that can help smokers achieve and sustain abstinence from smoking, as well as methods that can prevent people, especially youths, from starting to smoke, remain a huge challenge in public health.
Cigarette smoking is a complex behavior that includes a number of stages such as initiation, experimentation, regular use, dependence, cessation, and relapse (Ho and Tyndale, 2007; Malaiyandi et al, 2005; Mayhew et al, 2000). Although the initiation of tobacco use, the progression from initial use to smoking dependence, and the ability to quit smoking are undoubtedly affected by various environmental factors, twin, family, and adoption studies have provided strong evidence that genetics has a substantial role in the etiology of these phenotypes (Goode et al, 2003; Lerman and Berrettini, 2003; Lerman et al, 2007; Osler et al, 2001). Earlier studies revealed a considerable genetic contribution to the risk of smoking initiation (SI) (Hardie et al, 2006; Kendler et al, 1999; Li et al, 2003; Mayhew et al, 2000; Morley et al, 2007; Vink et al, 2004), nicotine dependence (ND) (Kendler et al, 1999; Lessov et al, 2004a; Maes et al, 2004; Malaiyandi et al, 2005; Sullivan and Kendler, 1999; True et al, 1999), as well as smoking cessation (SC) (Hamilton et al, 2006; Heath et al, 1999; Morley et al, 2007; Xian et al, 2003).
Nicotine is the main psychoactive ingredient in cigarettes and evokes its physiological effects by stimulating the mesolimbic brain reward system by binding with nicotinic acetylcholine receptors (nAChRs). So far, the majority of candidate gene-based association studies have focused on those genes that may predispose to addictive behavior by virtue of their effects on key neurotransmitter pathways (for example, dopamine and serotonin) and genes that may affect response to nicotine (for example, nAChRs and nicotine metabolism) (Ho and Tyndale, 2007). However, genetic studies have indicated that, for complex behaviors such as cigarette smoking, the individual differences can be attributed to hundreds of genes and their variants. Genes involved in different biological functions may act in concert to account for the risk of vulnerability to smoking behavior, with each gene having a moderate effect (Hall et al, 2002; Lessov et al, 2004b; Tyndale, 2003). Polymorphisms in related genes may cooperate in an additive or synergistic manner and modify the risk of smoking rather than act as sole determinants. Consistent with this belief, more and more genes have been found to be associated with smoking behavior over past decades, especially during the past few years. Whereas some plausible candidate genes (for example, nAChRs and dopamine signaling) have been reported and the findings have been partially replicated, numerous genes involved in other biological processes and pathways also have been associated with different smoking behaviors. This is especially true as the genome-wide association (GWA) study is being commonly used in genetic studies of complex traits such as smoking, and the underlying genetic factors can now be investigated in a high throughput and more comprehensive approach. In this situation, a systematic approach that is able to reveal the biochemical processes underlying the genes associated with smoking behaviors will not only help us understand the relations of these genes but also provide further evidence of the validity of the individual gene-based association studies.
In this study, we searched the literature to identify genes purportedly associated with SI/progression (P), ND, and SC. We then examined whether these genes are enriched in biochemical pathways important in neuronal and brain function.
MATERIALS AND METHODS
Identification of Smoking-Related Genes
Contemporary genetic association studies of smoking behaviors are focused primarily on SI, progression to smoking dependence, ND (assessed by various measures or scales such as DSM-IV, Fagerstrom Test for Nicotine Dependence, Fagerstrom Tolerance Questionnaire, and/or smoking quantity, etc.), or SC. Only limited studies have been conducted on SI and progression to ND, and considering the potential overlap of these two highly related behaviors, we combined them into the single category of SI/P.
The list of candidate genes for the three smoking-related phenotypes was created by searching all human genetics association studies deposited in PUBMED (http://www.ncbi.nlm.nih.gov/pubmed/). Similar to Sullivan et al (2004), we queried the item ‘(Smoking [MeSH] OR Tobacco Use Disorder [MeSH]) AND (Polymorphism [MeSH] OR Genotype [MeSH] OR Alleles [MeSH]) NOT (Neoplasms [MeSH])’, and a total of 1790 hits was retrieved by September 2008. The abstracts of these articles were reviewed and the association studies of any of the three smoking-related behaviors were selected. From the selected publications, we narrowed our selection by focusing on those reporting a significant association of one or more genes with any of the three phenotypes. To reduce the number of false-positive findings, the studies reporting negative or insignificant associations were not included, although it is likely that some of the genes analyzed in these studies might be associated with the phenotypes that we were interested in. The full reports of the selected publications were reviewed to ensure that the conclusions were supported by the content. From these studies, genes reported to be associated with each phenotype were selected for the current study.
The results from several GWA studies were included. In the work of Bierut et al (2007), 35 of 31 960 SNPs were identified with p-values <0.0001, and several genes were suggested to be associated with ND, including neurexin 1 (NRXN1), vacuolar sorting protein (VPS13A), transient receptor potential channel (TRPC7), as well as a classic candidate gene related to smoking, neuronal nicotinic cholinergic receptor β3 (CHRNB3). All the genes nominated in this study were included in our list for ND. In another large-scale candidate gene-based association study, Saccone et al (2007) analyzed 3713 SNPs corresponding to more than 300 candidate genes. The top five SNPs with the smallest false discovery rate (FDR) values (ranging from 0.056 to 0.166) corresponded with neuronal nicotinic cholinergic receptor α3 (CHRNA3), α5 (CHRNA5), and CHRNB3. In the work of Uhl et al (2007), allele frequencies in nicotine-dependent and control individuals were compared for 520 000 SNPs, and 32 genes were suggested to be potentially associated with ND; all of them were included in the ND-related gene list. In a recent study on SC, Uhl et al (2008) performed GWA studies on three independent samples to identify genes facilitating SC success with bupropion hydrochloride vs nicotine replacement therapy. Various genes involved in cell adhesion, transcription regulation, transportation, and signaling transduction were suggested to be candidates contributing to successful SC. From this study, we included those genes that showed significant association with SC in all the three samples (eight genes) or in two samples with at least two nominally significant SNPs in each sample (55 genes).
Identification of Enriched Biochemical Pathways
By literature search, we collected a list of genes associated with each smoking-related phenotype. To get a better understanding of the underlying biological mechanisms, multiple bioinformatics tools were used to identify the significantly enriched pathways involved in the smoking phenotypes. The available pathway analysis tools can be classified into three categories (Tarca et al, 2009): (1) Over-representation analysis (ORA), which compares the genes of interest with genes in predefined pathways and identifies the pathways that include a statistically higher number of genes in the list of interest as overrepresented; (2) functional class scoring (FCS), which compares the genes in chosen pathways with the entire list of genes sorted by certain criteria and identifies the pathways showing statistically significant correlation with the phenotypes under study; and (3) impact analysis, which is similar to ORA, but also considers the connections of genes in the pathways. The FCS approach, for example, GSEA (Subramanian et al, 2005), is not feasible for the current analysis as it requires gene expression measurements. The following is a brief description of the four pathway analysis tools used in the current study.
Ingenuity pathway analysis
The core of Ingenuity pathway analysis (IPA) (http://www.ingenuity.com/) is the Ingenuity Pathways Knowledge Base (IPKB), a manually curated knowledge database consisting of function, interaction, and other information of genes/proteins. On the basis of such information, IPA is able to perform analysis on global canonical pathways, dynamically generated biological networks, and global functions from a list of genes. Currently, the IPKB includes 81 canonical metabolic pathways involved in various metabolism processes such as energy metabolism, metabolism of amino acids, and complex carbohydrates. It also includes 202 signaling pathways, such as those related to neurotransmitter signaling, intracellular and secondary signaling, and nuclear receptor signaling.
In our analysis, the gene symbol and the corresponding GenBank accession numbers of genes associated with each smoking phenotype were uploaded into the IPA and compared against the genes in each canonical pathway included in the IPKB. All the pathways with one or more genes overlapping the candidate genes were extracted. A significance value was assigned by the program to measure the chance that the genes of interest participate in a given extracted pathway. Briefly, the p-value for a given pathway was calculated by considering: (1) the number of input genes that could be mapped to this pathway in the IPKB, denoted by m; (2) the number of genes involved in this pathway, denoted by M; (3) the total number of input genes that could be mapped to the IPKB, denoted by n; and (4) the total number of known genes included in the IPKB, denoted by N. Then the p-value was calculated using the right-tailed Fisher's exact test (which is identical to the hypergeometric distribution in this case):
where C(M, k), C(N−M, n−k), and C(N, n) are binomial coefficients. In general, a p-value <0.05 indicates a statistically significant, non-random association.
As many pathways were examined, multiple comparison correction for the individually calculated p-values was necessary to permit reliable statistical inferences. The output p-values for the pathways associated with each smoking phenotype were analyzed separately using the MATLAB Bioinformatics Toolbox (The Mathworks, Natick, MA), which calculated the FDR by the method of Benjamini and Hochberg (1995).
The database for annotation, visualization, and integrated discovery
The database for annotation, visualization, and integrated discovery (DAVID) (http://david.abcc.ncifcrf.gov) (Hosack et al, 2003; Huang da et al, 2009) is a bioinformatics resource consisting of an integrated biological knowledge database and analytic tools aimed at extracting biological themes from gene/protein lists systematically. Compared with other tools, DAVID can provide an integrated and expanded back-end annotation database, multiple modular enrichment algorithms, and exploratory ability in an integrated data-mining environment. In our analysis, the input genes were analyzed using different text- and pathway-mining tools including gene functional classification, functional annotation chart or clustering, and functional annotation table. Pathway analysis was performed using its Functional Annotation Tool based on the Kyoto Encyclopedia of Genes and Genomes (KEGG; www.genome.jp/kegg) and Biocarta (www.biocarta.com) pathway databases. The enrichment of given pathways in the gene list was measured by EASE score, a modified Fisher exact test p-value. The program also performed p-value correction based on the method of Benjamini and Hochberg (1995).
GeneTrail
GeneTrail (Keller et al, 2008) (http://genetrail.bioinf.uni-sb.de) is web-based bioinformatics tool providing the statistical evaluation of gene/protein lists with respect to enrichment of functional categories. It can perform a wide variety of biological categories and pathway analysis based on multiple databases such as KEGG and Gene Ontology (GO; http://www.geneontology.org/). The GeneTrail analysis tool used in this work was the ‘Over-representation Analysis’ module, which compared the gene list with a reference set of genes and identified the overrepresented pathways.
Onto Pathway-Express
Onto Pathway-Express (http://vortex.cs.wayne.edu/ontoexpress/) is a pathway analysis tool based on the KEGG pathway database. Different from other tools such as IPA and DAVID, this bioinformatics tool integrates the pathway topology and the position information of each gene in the pathway into its enrichment analysis. By implementing an impact factor analysis, Onto Pathway-Express incorporates both the probabilistic component and the gene interactions into pathway identification (Draghici et al, 2007). Briefly, on the basis of the input gene list, Onto Pathway-Express calculates a perturbation factor for each gene by taking into account its expression level and the perturbation of genes downstream from it in each selected pathway. The impact factor of the entire pathway includes a probabilistic term that takes into consideration the proportion of differentially regulated genes in the pathway and the perturbation factors of all genes in the pathway. The output pathways are assigned significance levels according to their impact factors, and the FDR values are computed by the method of Benjamini and Hochberg (1995).
Of the four tools, IPA, DAVID, and GeneTrail use the ORA approach. However, the pathway databases underlying these tools are different, for example, a proprietary database is included in IPA, whereas public databases are adopted by DAVID (KEGG and Biocarta) and GeneTrail (KEGG). Although Onto Pathway-Express performs its analysis on the basis of the KEGG database, the analysis algorithm is different from other methods. With these methods, we expect to obtain a relatively comprehensive evaluation of the pathways associated with the genes important to each smoking-related phenotype.
RESULTS
Identification of Genes Reported to be Associated with Each Smoking Behavior
By searching PUBMED, we extracted publications on the genetic association studies related to tobacco smoking. In the current work, we focused only on the studies related to one of the three phenotypes: SI/P, ND, and SC. For each phenotype, only the publications reporting a significant association of a gene(s) with this phenotype were collected; those by the original authors reporting a negative result or insignificant association were not included. A detailed list of all genes reported to be associated with each of the three phenotypes is provided in Table 1.
For SI/P, 16 genes were identified in 15 studies, all of which were performed at individual gene level. Among them are five nAChR subunit genes, that is, CHRNA3, CHRNA5, CHRNA6, CHRNB3, and CHRNB4; dopamine receptor D2 (DRD2) and D4 (DRD4); and one serotonin receptor (HTR6). The genes encoding transporters of dopamine (DAT1 or SLC6A3) and serotonin (5-HTT or SLC6A4) were also included. The other genes were those involving the functions related to nicotine or neurotransmitter metabolism/synthesis such as COMT, CYP2A6, and TPH1; signal transduction (for example, PTEN and RHOA); or immune response (for example, IL8).
Regarding ND, there were 76 publications, including 73 studies focused on either single or a few genes. In these papers, 63 genes were reported to be significantly associated with ND by the original authors. The other three studies were either on a genome-wide scale (Bierut et al, 2007; Uhl et al, 2007) or on hundreds of candidate genes (Saccone et al, 2007), and they nominated a total of 41 genes. Collectively, 99 unique genes are included in the final list. The most prominent genes were those encoding acetylcholine receptors (CHRM1, CHRM5, CHRNA4, CHRNA5, and CHRNB2), dopamine receptors (DRD1, DRD2, DRD3, and DRD4), GABA receptors (GABRA2, GABRB2, GABARAP, and GABRA4), serotonin receptors (HTR1F and HTR2A), as well as proteins involved in nicotine or neurotransmitter metabolism/synthesis (for example, CYP2A6, DBH, MAOA, and TPH1).
For SC, 63 genes were nominated by a GWA study (Uhl et al, 2008) and 12 by 23 candidate gene-based association studies. These genes were involved in various biological functions, such as dopamine receptor signaling (DRD2, DRD4, and SLC6A3), glutamate receptor signaling (GRIK1, GRIK2, GRIN2A, and SLC1A2), and calcium signaling (for example, CACNA2D3, CACNB2, CDH13, and ITPR2).
Among the genes associated with the three smoking phenotypes, five were included in all the three lists, that is, COMT, CYP2A6, DRD2, DRD4, and SLC6A3. Another six genes, that is, CHRNA3, CHRNA5, CHRNB3, PTEN, SLC6A4, and TPH1, were associated with SI/P and ND. Ten genes, that is, A2BP1, ARRB2, CDH13, CHRNB2, CSMD1, CYP2B6, DBH, OPRM1, PRKG1, and PTPRD, were associated with ND and SC.
Enriched Biological Pathways Associated with Each Smoking-Related Phenotype
On the basis of the genes related to each smoking phenotype, enriched biochemical pathways were identified by IPA and other bioinformatics tools. For SI/P, the 16 genes (Table 1) were overrepresented in 9 pathways defined in the IPA database (p<0.05; Table 2). For five of these pathways (calcium signaling, dopamine receptor signaling, serotonin receptor signaling, cAMP-mediated signaling, and G-protein-coupled receptor signaling), the corresponding FDR values were <0.05. For the other pathways (tryptophan metabolism, tight junction signaling, IL-8 signaling, and integrin signaling), they had slightly higher FDR values (0.085–0.116).
The IPA assigned 51 of the 99 genes associated with ND to 21 overrepresented pathways (p<0.05; Table 3). Fourteen of these pathways (for example, dopamine receptor signaling, cAMP-mediated signaling, G-protein-coupled receptor signaling, and serotonin receptor signaling) had an FDR<0.05, and the other pathways (for example, fatty acid metabolism and synaptic long-term potentiation (LTP)) had an FDR<0.14.
For SC, 13 pathways were found to be enriched in 18 of the 75 genes associated with this phenotype (p<0.05; Table 4). Four of the pathways (dopamine receptor signaling, glutamate receptor signaling, cAMP-mediated signaling, and calcium signaling) had an FDR<0.05, and the remaining pathways (for example, synaptic LTP, G-protein-coupled receptor signaling, and synaptic long-term depression (LTD)) had an FDR ranging from 0.082 to 0.18.
Of the pathways enriched in the genes associated with each smoking phenotype, four, that is, calcium signaling, cAMP-mediated signaling, dopamine receptor signaling, and G-protein-coupled receptor signaling, were associated with all three smoking behaviors (Table 5). Two other enriched pathways (that is, serotonin receptor signaling and tryptophan metabolism) were shared by SI/P and ND, and three enriched pathways (neurotrophin/TRK signaling, synaptic LTP, and tyrosine metabolism) were shared by ND and SC.
The enrichment of these pathways in multiple smoking phenotypes was consistent with the fact that synaptic transmission-related biological processes, such as nicotine-nAChR and dopamine signaling, were the key biochemical components underlying different smoking-related behaviors. This also implies that the genes involved in these three smoking phenotypes indeed overlap highly. On the basis of these biochemical relationships, we present in Figure 1 a schematic representation of the major pathways associated with the three phenotypes.
Schematic representation of the genes and major pathways involved in smoking initiation/progression (SI/P), smoking dependence, or smoking cessation (SC). Genetic studies have indicated that tobacco smoking is a complex disorder. On the basis of the genes associated with SI/P, ND, and SC, we identified various enriched pathways corresponding to each phenotype of interest. These pathways were then connected on the basis of their biological relations. Owing to the overlap of many pathways among these three phenotypes, for the sake of simplicity, all identified pathways are shown together.
DISCUSSION
Over recent decades, much has been learnt from animal or cell models about the molecular mechanisms underlying nicotine treatment. Numerous genes and pathways have been found to have a role, either directly or indirectly, in these important smoking-related phenotypes. However, it is less clear whether the same sets of genes and pathways are involved in tobacco dependence of humans. Epidemiological studies have shown that genetic factors are responsible for a significant portion of the risk for SI, ND, and SC (Hamilton et al, 2006; Lerman and Berrettini, 2003; Li et al, 2003; Mayhew et al, 2000; Sullivan and Kendler, 1999). Moreover, significant genetic overlaps have been identified among these three phenotypes (Ho and Tyndale, 2007; Kendler et al, 1999; Maes et al, 2004). Identifying vulnerability genes for the three phenotypes, especially the biochemical pathways associated with them, will not only provide a systematic overview of the genetic factors underlying different smoking behaviors but is also helpful in guiding selection of potentially important genes for further analysis. With a thorough review of the genes contributing to the genetic risk of smoking behaviors, and a systematic search for gene networks using various pathway analysis tools, herein, we provide a comprehensive view of the biochemical pathways involved in the three major smoking phenotypes (see Figure 1 for details).
Although candidate gene-based association studies have provided much of our knowledge about factors contributing to smoking behaviors, a systematic approach, as reported in this study, has significant advantages. For complex disorders such as tobacco smoking, the presence of genetic heterogeneity and multiple interacting genes, each with a small to moderate effect, are considered to be the major hurdle in genetic association studies (Ho and Tyndale, 2007; Lessov-Schlaggar et al, 2008). Numerous genetic factors have been implicated, but in many cases, these findings cannot be replicated in independent studies. At the same time, because of resource limitations, a significant proportion of reported genetic studies might not have sufficient sample size or enough replication samples to reduce the rate of false-positive associations evoked by multiple testing. This is especially true for GWA studies, in which tens of thousands of SNPs can be analyzed simultaneously. A pathway approach, which takes account of the biochemical relevance of genes identified from association studies, not only can be more robust to potential false positives caused by factors such as low density of markers, small sample sizes, different ethnicities, and heterogeneity within and between samples but also may yield a more comprehensive view of the genetic mechanism underlying smoking behaviors. Moreover, although in candidate gene-based association studies, the selection of targets may be focused on some specific biological processes or pathways, the results from GWA studies seem to be more diverse. In such cases, pathway analysis becomes more necessary to detect the main biological themes from the genes involved in different functions. For example, in a recently reported GWA study, Vink et al (2009) identified 302 genes associated with SI and current smoking, but no gene involved in classic targets, such as dopamine receptor signaling or nAChRs, was detected. Instead, they identified genes related to glutamate receptor signaling, tyrosine kinase signaling, and cell-adhesion proteins. In our analysis based on genes other than those reported by Vink et al, glutamate receptor signaling was enriched among the genes associated with SC, and TRK signaling was enriched in both ND and SC (see Tables 3 and 4, and Figure 1). With the increased interest in conducting GWA studies for smoking behavior and other complex traits, a pathway approach will become more useful.
However, there are several limitations of this study. First, our pathway analysis results depend entirely on genes reported to be associated with each smoking phenotype of interest. Given that identification of susceptibility genes for each smoking phenotype is an ongoing process, the pathways identified in this report should be treated in the same way. Therefore, the pathways identified here are only some of the pathways that may be involved in the regulation of the three phenotypes. This is especially true for SI/P and SC, as significantly more genetic studies have been conducted on ND compared with the other smoking phenotypes. Second, we adopted the conclusions drawn by the original authors of each study in our pathway analysis. This means that some of our conclusions may be biased by some of those original reports because of their small sample size, the presence of heterogeneity, or absence of correction for multiple testing. Initially, we tried to apply a general standard to all those reported studies but had to give it up because different research groups conducted those studies over different time periods. It was challenging to redraw a conclusion from those studies reported by other researchers. However, we do not think this will affect our results greatly, as we have included as many reports as we could get from the literature. Third, for the sake of simplicity and increasing the number of genes included in each smoking phenotype, we classified more than 100 reports on smoking-related behaviors from different ethnic populations into three broad categories, that is, SI/P, ND, and SC. This is certain to bring a heterogeneity issue to the three phenotypes of interest, especially for SI/P and ND. Fourth, the direction of association is an important issue. For example, some variations may be associated with a protective effect against SI or ND, whereas others may increase the risk of such tendencies. Considering the fact that the direction of association depends on genetic variants under investigation for a given phenotype, we did not consider it in our current analyses. Because at this stage we are more interested in the genes and pathways potentially associated with smoking behaviors, focusing on the genes without considering the association directions will not create a serious problem. In addition, to simplify the analysis and reduce the number of false-positive genes, we did not include publications reporting negative or insignificant results. However, we realize that some genes from these studies may be among the factors associated with the smoking behaviors of interest. The fact that they were not found to be associated is likely attributable to other factors such as the small sample size or the presence of heterogeneity in their samples.
Although there are some limitations to this study, some interesting findings emerged, which probably never would have been identified in any single genetic study, including GWA, in one or a few samples. For example, we found that calcium signaling, dopamine receptor signaling, and cAMP-mediated signaling are the main pathways enriched in all three smoking phenotypes. The most prominent calcium signaling-related genes associated with each phenotype were nACh receptors. By mediating intracellular Ca2+ concentration, these ligand-gated cation channels have an important role in regulating various neuronal activities, including neurotransmitter release (Marshall et al, 1997; Wonnacott, 1997). Transcription factors, such as CREBs (cAMP responsive element-binding proteins), are crucial for conversion of events at cell membranes into alterations in gene expression. Regulation of the activity of CREB by drugs of abuse or stress has a profound effect on an animal's responsiveness to emotional stimuli (Carlezon et al, 2005; Conti and Blendy, 2004). The CREB function in the neurons is normally regulated by glutamatergic and dopaminergic inputs (Dudman et al, 2003).
The mesolimbic dopamine pathway is believed to be one of the central pathways underlying addiction to various drugs of abuse (Nestler, 2005). Genes included in this pathway are among the major targets of association study for ND. Although this pathway is enriched in all the three smoking-related phenotypes, the genes associated with each smoking phenotype are different. For SI/P, the genes reported in literature, such as COMT, DRD2, DRD4, and SLC6A3, were shared by ND and SC. For SC, two genes, FREQ and PPP2R2B, were uniquely detected. The FREQ protein (also known as neuronal calcium sensor 1, NCS1), a member of the neuronal calcium sensor family, has been implicated in the regulation of a wide range of neuronal functions such as membrane traffic, cell survival, ion channels, and receptor signaling (Burgoyne, 2007). In mammalian cells, FREQ may couple the dopamine and calcium signaling pathways by direct interaction with DRD2, implying an important role in the regulation of dopaminergic signaling in normal and diseased brain (Kabbani et al, 2002). The interaction between variants of DRD2 and FREQ significantly impacts the efficacy of nicotine replacement therapy (Dahl et al, 2006). PPP2R2B encodes a brain-specific regulatory subunit of protein phosphatase 2A (PP2A) and gives rise to multiple splice variants in neurons (Dagda et al, 2003; Schmidt et al, 2002). The product of this gene is suggested to be localized in the outer mitochondrial membrane and involved in neuronal survival regulation through the mitochondrial fission/fusion balance (Dagda et al, 2008). A CAG-repeat expansion in a non-coding region of this gene is responsible for the neurodegenerative disorder, spinocerebellar ataxia type 12 (SCA12) (Holmes et al, 1999). Although the dopamine receptor pathway has an important role in all three smoking phenotypes, it is possible that different parts of this pathway are involved in each smoking behavior, with SI/P and ND having greater similarity than SC. Given the importance of this pathway in the development of drug addiction, more genes need to be verified to obtain a more specific picture of its role underlying each phenotype.
Serotonin modulates dopamine release and has been implicated in nicotine reinforcement. Earlier study has shown that serotonin concentrations are increased by nicotine administration and decreased during withdrawal. Serotonin receptor signaling was enriched in the genes associated with SI/P and ND, but not in those associated with SC, in our analysis. In several recent studies designed to investigate the association between genes from the serotonin receptor signaling pathway and SC, no positive result was obtained (Brody et al, 2005; David et al, 2007b, 2008a; Munafo et al, 2006; O'Gara et al, 2008). Similar to the serotonin receptor signaling pathway, tryptophan metabolism, the pathway involved in the biological synthesis of serotonin, is enriched in the genes associated with SI/P, but not in those associated with SC. Consistent with this result, to date, the clinical effects of serotonergic-based drugs in SC are largely negative (Fletcher et al, 2008). Although more studies are needed, these results suggest that the genetic variants in serotonin receptor signaling and tryptophan metabolism pathways may be less important in SC.
Glutamate receptor signaling was found to be enriched in the genes associated with SC, but not in those associated with the other two phenotypes. In a recent GWA study (Vink et al, 2009), multiple genes from the glutamate receptor signaling pathway were suggested to be associated with SI and current smoking. Similarly, the glutamate receptor signaling-related genes associated with SC were also identified by a GWA study (Uhl et al, 2008). The genes in this pathway that are associated with SC include GRIK1, GRIK2, GRIN2A, and SLC1A2, whereas GRIN2A, GRIN2B, GRIK2, and GRM8 were associated with SI and current smoking (Vink et al, 2009). Another gene, GRM7, was suggested to be associated with ND in an earlier GWA study (Uhl et al, 2007). Taken together, these results suggest that glutamate receptor signaling is involved all three phenotypes of interest. In addition, till now, most of the genes from this pathway were identified by the GWA studies, showing the great potential of the GWA study in identifying genetic variants related to smoking behavior.
Our analysis indicates that the LTP pathway was enriched in genes associated with ND and SC, and the LTD pathway was enriched in genes associated with SC. Repeated exposure of neurons to nicotine eventually leads to the modulation of the functioning of the neural circuits in which the neurons operate. LTP and LTD are thought to be critical mechanisms that contribute to such modifications in neuronal plasticity (Kauer, 2004; Saal et al, 2003; Thomas and Malenka, 2003). In the development of ND, the LTP and LTD pathways may be essential for the neurons to form new synapses and eliminate some unnecessary ones to adapt to a new environment. In the process of SC, these pathways may be invoked to interrupt some neuron connections formed in the development of nicotine addiction in order to help the reward circuit return to normal. Until now, only a few genes related to LTP and LTD have been identified in the association studies. Considering the importance of these pathways in ND development and SC, other genes associated with these processes represent potential targets for future studies of these phenotypes.
In a recent study, five pathways were suggested to be associated with addiction to cocaine, alcohol, opioids, and nicotine in humans (Li et al, 2008). These pathways are gap junctions, GnRH signaling, LTP, MAPK signaling, and neuroactive ligand–receptor interaction. As shown in Table 5, three of the pathways (gap junction, LTP, and MAPK signaling) were enriched in genes associated with ND or SC. Although another pathway, neuroactive ligand-receptor interaction, was identified for all three smoking phenotypes by either Onto Pathway-Express or DAVID analysis in our work, it was not included in the current report because several more specific pathways, such as calcium signaling, dopamine receptor signaling, and serotonin receptor signaling, were also identified and reported herein. Our results provide further evidence that nicotine may share some biological mechanisms with other substances in addiction conditions. However, we also identified multiple specific pathways related to smoking behavior, suggesting that the mechanisms underlying nicotine addiction are complex and may be different in certain ways from those associated with addiction to other drugs.
The significantly overrepresented pathways suggest a view of neuronal responses in different conditions of nicotine–neuron interaction (Figure 1). On binding by nicotine, the nAChRs open and cause the influx of Ca2+ and Na+ into the presynaptic neuron, which evokes depolarization of the neuron, as well as activation of the Ca2+ signaling cascade. The Ca2+ signaling cascade is directly related to the presynaptic release of neurotransmitters, including dopamine, serotonin, GABA, and glutamate, in different neurons. The neurotransmitters interact with their specific receptors, provoking a series of signaling pathways, such as cAMP-mediated signaling and PKC signaling. With the regulation of these pathways, various physiological processes such as neuronal excitability and energy metabolism may be mediated. Variations in some of these genes may change the efficiency or function of the pathways and, eventually, the psychopathological phenotype. Although a significant number of genes associated with these pathways have been identified, our understanding of the genetic determinants of smoking is still in its early stages (Munafo and Johnstone, 2008). It can be expected that as more genetic factors are determined, more detailed pathways and more comprehensive understanding of the mechanisms of human smoking behavior will be obtained.
References
Agrawal A, Pergadia ML, Saccone SF, Hinrichs AL, Lessov-Schlaggar CN, Saccone NL et al (2008). Gamma-aminobutyric acid receptor genes and nicotine dependence: evidence for association from a case–control study. Addiction 103: 1027–1038.
Anney RJ, Lotfi-Miri M, Olsson CA, Reid SC, Hemphill SA, Patton GC (2007). Variation in the gene coding for the M5 muscarinic receptor (CHRM5) influences cigarette dose but is not associated with dependence to drugs of addiction: evidence from a prospective population based cohort study of young adults. BMC Genet 8: 46.
Audrain-McGovern J, Al Koudsi N, Rodriguez D, Wileyto EP, Shields PG, Tyndale RF (2007). The role of CYP2A6 in the emergence of nicotine dependence in adolescents. Pediatrics 119: e264–e274.
Audrain-McGovern J, Lerman C, Wileyto EP, Rodriguez D, Shields PG (2004). Interacting effects of genetic predisposition and depression on adolescent smoking progression. Am J Psychiatry 161: 1224–1230.
Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B 57: 289–300.
Berrettini W, Yuan X, Tozzi F, Song K, Francks C, Chilcoat H et al (2008). Alpha-5/alpha-3 nicotinic receptor subunit alleles increase risk for heavy smoking. Mol Psychiatry 13: 368–373.
Berrettini WH, Wileyto EP, Epstein L, Restine S, Hawk L, Shields P et al (2007). Catechol-O-methyltransferase (COMT) gene variants predict response to bupropion therapy for tobacco dependence. Biol Psychiatry 61: 111–118.
Beuten J, Ma JZ, Lou XY, Payne TJ, Li MD (2007a). Association analysis of the protein phosphatase 1 regulatory subunit 1B (PPP1R1B) gene with nicotine dependence in European- and African-American smokers. Am J Med Genet B Neuropsychiatr Genet 144B: 285–290.
Beuten J, Ma JZ, Payne TJ, Dupont RT, Crews KM, Somes G et al (2005a). Single- and multilocus allelic variants within the GABA(B) receptor subunit 2 (GABAB2) gene are significantly associated with nicotine dependence. Am J Hum Genet 76: 859–864.
Beuten J, Ma JZ, Payne TJ, Dupont RT, Lou XY, Crews KM et al (2007b). Association of specific haplotypes of neurotrophic tyrosine kinase receptor 2 gene (NTRK2) with vulnerability to nicotine dependence in African-Americans and European-Americans. Biol Psychiatry 61: 48–55.
Beuten J, Ma JZ, Payne TJ, Dupont RT, Quezada P, Huang W et al (2005b). Significant association of BDNF haplotypes in European-American male smokers but not in European-American female or African-American smokers. Am J Med Genet B Neuropsychiatr Genet 139B: 73–80.
Beuten J, Payne TJ, Ma JZ, Li MD (2006). Significant association of catechol-O-methyltransferase (COMT) haplotypes with nicotine dependence in male and female smokers of two ethnic populations. Neuropsychopharmacology 31: 675–684.
Bierut LJ, Madden PA, Breslau N, Johnson EO, Hatsukami D, Pomerleau OF et al (2007). Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum Mol Genet 16: 24–35.
Brody CL, Hamer DH, Haaga DA (2005). Depression vulnerability, cigarette smoking, and the serotonin transporter gene. Addict Behav 30: 557–566.
Burgoyne RD (2007). Neuronal calcium sensor proteins: generating diversity in neuronal Ca2+ signalling. Nat Rev Neurosci 8: 182–193.
Cannon DS, Baker TB, Piper ME, Scholand MB, Lawrence DL, Drayna DT et al (2005). Associations between phenylthiocarbamide gene polymorphisms and cigarette smoking. Nicotine Tob Res 7: 853–858.
Caporaso NE, Lerman C, Audrain J, Boyd NR, Main D, Issaq HJ et al (2001). Nicotine metabolism and CYP2D6 phenotype in smokers. Cancer Epidemiol Biomarkers Prev 10: 261–263.
Carlezon Jr WA, Duman RS, Nestler EJ (2005). The many faces of CREB. Trends Neurosci 28: 436–445.
CDC (2005a). Tobacco use, access, and exposure to tobacco in media among middle and high school students—United States, 2004. Morbid Mortal Wkly Rep 54: 297–301.
CDC (2005b). Annual smoking-attributable mortality, years of potential life lost, and economic costs—United States, 1997–2001. Morbid Mortal Wkly Rep 54: 625–628.
CDC (2006). Cigarette use among high school students—United States, 1991–2005. Morbid Mortal Wkly Rep 55: 724–726.
CDC (2007). Cigarette smoking among adults—United States, 2006. Morbid Mortal Wkly Rep 56: 1157–1161.
Chen X, Che Y, Zhang L, Putman AH, Damaj I, Martin BR et al (2007). RhoA, encoding a Rho GTPase, is associated with smoking initiation. Genes Brain Behav 6: 689–697.
Chen X, Williamson VS, An SS, Hettema JM, Aggen SH, Neale MC et al (2008). Cannabinoid receptor 1 gene association with nicotine dependence. Arch Gen Psychiatry 65: 816–824.
Chen X, Wu B, Kendler KS (2004). Association study of the Epac gene and tobacco smoking and nicotine dependence. Am J Med Genet B Neuropsychiatr Genet 129B: 116–119.
Colilla S, Lerman C, Shields PG, Jepson C, Rukstalis M, Berlin J et al (2005). Association of catechol-O-methyltransferase with smoking cessation in two independent studies of women. Pharmacogenet Genomics 15: 393–398.
Comings DE, Ferry L, Bradshaw-Robinson S, Burchette R, Chiu C, Muhleman D (1996). The dopamine D2 receptor (DRD2) gene: a genetic risk factor in smoking. Pharmacogenetics 6: 73–79.
Comings DE, Wu S, Gonzalez N, Iacono WG, McGue M, Peters WW et al (2001). Cholecystokinin (CCK) gene as a possible risk factor for smoking: a replication in two independent samples. Mol Genet Metab 73: 349–353.
Conti AC, Blendy JA (2004). Regulation of antidepressant activity by cAMP response element binding proteins. Mol Neurobiol 30: 143–155.
Conti DV, Lee W, Li D, Liu J, Van Den Berg D, Thomas PD et al (2008). Nicotinic acetylcholine receptor {beta}2 subunit gene implicated in a systems-based candidate gene study of smoking cessation. Hum Mol Genet 17: 2834–2848.
Costa-Mallen P, Costa LG, Checkoway H (2005). Genotype combinations for monoamine oxidase-B intron 13 polymorphism and dopamine D2 receptor TaqIB polymorphism are associated with ever-smoking status among men. Neurosci Lett 385: 158–162.
Dagda RK, Merrill RA, Cribbs JT, Chen Y, Hell JW, Usachev YM et al (2008). The spinocerebellar ataxia 12 gene product and protein phosphatase 2A regulatory subunit Bbeta2 antagonizes neuronal survival by promoting mitochondrial fission. J Biol Chem 283: 36241–36248.
Dagda RK, Zaucha JA, Wadzinski BE, Strack S (2003). A developmentally regulated, neuron-specific splice variant of the variable subunit Bbeta targets protein phosphatase 2A to mitochondria and modulates apoptosis. J Biol Chem 278: 24976–24985.
Dahl JP, Jepson C, Levenson R, Wileyto EP, Patterson F, Berrettini WH et al (2006). Interaction between variation in the D2 dopamine receptor (DRD2) and the neuronal calcium sensor-1 (FREQ) genes in predicting response to nicotine replacement therapy for tobacco dependence. Pharmacogenomics J 6: 194–199.
David SP, Brown RA, Papandonatos GD, Kahler CW, Lloyd-Richardson EE, Munafo MR et al (2007a). Pharmacogenetic clinical trial of sustained-release bupropion for smoking cessation. Nicotine Tob Res 9: 821–833.
David SP, Johnstone EC, Murphy MF, Aveyard P, Guo B, Lerman C et al (2008a). Genetic variation in the serotonin pathway and smoking cessation with nicotine replacement therapy: new data from the Patch in Practice trial and pooled analyses. Drug Alcohol Depend 98: 77–85.
David SP, Munafo MR, Murphy MF, Proctor M, Walton RT, Johnstone EC (2008b). Genetic variation in the dopamine D4 receptor (DRD4) gene and smoking cessation: follow-up of a randomised clinical trial of transdermal nicotine patch. Pharmacogenomics J 8: 122–128.
David SP, Munafo MR, Murphy MF, Walton RT, Johnstone EC (2007b). The serotonin transporter 5-HTTLPR polymorphism and treatment response to nicotine patch: follow-up of a randomized controlled trial. Nicotine Tob Res 9: 225–231.
De Luca V, Wong AH, Muller DJ, Wong GW, Tyndale RF, Kennedy JL (2004). Evidence of association between smoking and alpha7 nicotinic receptor subunit gene in schizophrenia patients. Neuropsychopharmacology 29: 1522–1526.
do Prado-Lima PA, Chatkin JM, Taufer M, Oliveira G, Silveira E, Neto CA et al (2004). Polymorphism of 5HT2A serotonin receptor gene is implicated in smoking addiction. Am J Med Genet B Neuropsychiatr Genet 128B: 90–93.
Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C et al (2007). A systems biology approach for pathway level analysis. Genome Res 17: 1537–1545.
Dudman JT, Eaton ME, Rajadhyaksha A, Macias W, Taher M, Barczak A et al (2003). Dopamine D1 receptors mediate CREB phosphorylation via phosphorylation of the NMDA receptor at Ser897-NR1. J Neurochem 87: 922–934.
Ehringer MA, Clegg HV, Collins AC, Corley RP, Crowley T, Hewitt JK et al (2007). Association of the neuronal nicotinic receptor beta2 subunit gene (CHRNB2) with subjective responses to alcohol and nicotine. Am J Med Genet B Neuropsychiatr Genet 144B: 596–604.
Erblich J, Lerman C, Self DW, Diaz GA, Bovbjerg DH (2005). Effects of dopamine D2 receptor (DRD2) and transporter (SLC6A3) polymorphisms on smoking cue-induced cigarette craving among African-American smokers. Mol Psychiatry 10: 407–414.
Feng Y, Niu T, Xing H, Xu X, Chen C, Peng S et al (2004). A common haplotype of the nicotine acetylcholine receptor alpha 4 subunit gene is associated with vulnerability to nicotine addiction in men. Am J Hum Genet 75: 112–121.
Fletcher PJ, Le AD, Higgins GA (2008). Serotonin receptors as potential targets for modulation of nicotine use and dependence. Prog Brain Res 172: 361–383.
Gambier N, Batt AM, Marie B, Pfister M, Siest G, Visvikis-Siest S (2005). Association of CYP2A6*1B genetic variant with the amount of smoking in French adults from the Stanislas cohort. Pharmacogenomics J 5: 271–275.
Gelernter J, Panhuysen C, Weiss R, Brady K, Poling J, Krauthammer M et al (2007). Genomewide linkage scan for nicotine dependence: identification of a chromosome 5 risk locus. Biol Psychiatry 61: 119–126.
Gelernter J, Yu Y, Weiss R, Brady K, Panhuysen C, Yang BZ et al (2006). Haplotype spanning TTC12 and ANKK1, flanked by the DRD2 and NCAM1 loci, is strongly associated to nicotine dependence in two distinct American populations. Hum Mol Genet 15: 3498–3507.
Gerra G, Garofano L, Zaimovic A, Moi G, Branchi B, Bussandri M et al (2005). Association of the serotonin transporter promoter polymorphism with smoking behavior among adolescents. Am J Med Genet B Neuropsychiatr Genet 135B: 73–78.
Goode EL, Badzioch MD, Kim H, Gagnon F, Rozek LS, Edwards KL et al (2003). Multiple genome-wide analyses of smoking behavior in the Framingham Heart Study. BMC Genet 4 (Suppl 1): S102.
Guo S, Chen da F, Zhou DF, Sun HQ, Wu GY, Haile CN et al (2007). Association of functional catechol-O-methyl transferase (COMT) Val108Met polymorphism with smoking severity and age of smoking initiation in Chinese male smokers. Psychopharmacology (Berl) 190: 449–456.
Hall W, Madden P, Lynskey M (2002). The genetics of tobacco use: methods, findings and policy implications. Tob Control 11: 119–124.
Hamilton AS, Lessov-Schlaggar CN, Cockburn MG, Unger JB, Cozen W, Mack TM (2006). Gender differences in determinants of smoking initiation and persistence in California twins. Cancer Epidemiol Biomarkers Prev 15: 1189–1197.
Han DH, Joe KH, Na C, Lee YS (2008). Effect of genetic polymorphisms on smoking cessation: a trial of bupropion in Korean male smokers. Psychiatr Genet 18: 11–16.
Hardie TL, Moss HB, Lynch KG (2006). Genetic correlations between smoking initiation and smoking behaviors in a twin sample. Addict Behav 31: 2030–2037.
Heath AC, Kirk KM, Meyer JM, Martin NG (1999). Genetic and social determinants of initiation and age at onset of smoking in Australian twins. Behav Genet 29: 395–407.
Ho MK, Tyndale RF (2007). Overview of the pharmacogenomics of cigarette smoking. Pharmacogenomics J 7: 81–98.
Holmes SE, O'Hearn EE, McInnis MG, Gorelick-Feldman DA, Kleiderlein JJ, Callahan C et al (1999). Expansion of a novel CAG trinucleotide repeat in the 5′ region of PPP2R2B is associated with SCA12. Nat Genet 23: 391–392.
Hosack DA, Dennis Jr G, Sherman BT, Lane HC, Lempicki RA (2003). Identifying biological themes within lists of genes with EASE. Genome Biol 4: R70.
Howard LA, Ahluwalia JS, Lin SK, Sellers EM, Tyndale RF (2003). CYP2E1*1D regulatory polymorphism: association with alcohol and nicotine dependence. Pharmacogenetics 13: 321–328.
Huang da W, Sherman BT, Lempicki RA (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44–57.
Huang W, Ma JZ, Payne TJ, Beuten J, Dupont RT, Li MD (2008a). Significant association of DRD1 with nicotine dependence. Hum Genet 123: 133–140.
Huang W, Payne TJ, Ma JZ, Beuten J, Dupont RT, Inohara N et al (2009). Significant association of ANKK1 and detection of a functional polymorphism with nicotine dependence in an African-American sample. Neuropsychopharmacology 34: 319–330.
Huang W, Payne TJ, Ma JZ, Li MD (2008b). A functional polymorphism, rs6280, in DRD3 is significantly associated with nicotine dependence in European-American smokers. Am J Med Genet B Neuropsychiatr Genet 147B: 1109–1115.
Hubacek J, Pitha J, Skodova Z, Poledne R (2002). Is the CD14 receptor gene a marker for smoking dependence? Med Sci Monit 8: BR172–BR174.
Hughes JR, Stead LF, Lancaster T (2007). Antidepressants for smoking cessation. Cochrane Database Syst Rev Issue 1. Art. No. CD000031.
Ito H, Matsuo K, Hamajima N, Okuma K, Saito T, Tajima K (2005). Significant association of interleukin 8 -251T/A polymorphism with smoking behavior in a Japanese population. J Hum Genet 50: 567–573.
Johnstone EC, Elliot KM, David SP, Murphy MF, Walton RT, Munafo MR (2007). Association of COMT Val108/158Met genotype with smoking cessation in a nicotine replacement therapy randomized trial. Cancer Epidemiol Biomarkers Prev 16: 1065–1069.
Johnstone EC, Yudkin PL, Hey K, Roberts SJ, Welch SJ, Murphy MF et al (2004). Genetic variation in dopaminergic pathways and short-term effectiveness of the nicotine patch. Pharmacogenetics 14: 83–90.
Kabbani N, Negyessy L, Lin R, Goldman-Rakic P, Levenson R (2002). Interaction with neuronal calcium sensor NCS-1 mediates desensitization of the D2 dopamine receptor. J Neurosci 22: 8476–8486.
Kauer JA (2004). Learning mechanisms in addiction: synaptic plasticity in the ventral tegmental area as a result of exposure to drugs of abuse. Annu Rev Physiol 66: 447–475.
Keller A, Backes C, Al-Awadhi M, Gerasch A, Kuntzer J, Kohlbacher O et al (2008). GeneTrail Express: a web-based pipeline for the statistical evaluation of microarray experiments. BMC Bioinformatics 9: 552.
Kendler KS, Neale MC, Sullivan P, Corey LA, Gardner CO, Prescott CA (1999). A population-based twin study in women of smoking initiation and nicotine dependence. Psychol Med 29: 299–308.
Kremer I, Bachner-Melman R, Reshef A, Broude L, Nemanov L, Gritsenko I et al (2005). Association of the serotonin transporter gene with smoking behavior. Am J Psychiatry 162: 924–930.
Kubota T, Nakajima-Taniguchi C, Fukuda T, Funamoto M, Maeda M, Tange E et al (2006). CYP2A6 polymorphisms are associated with nicotine dependence and influence withdrawal symptoms in smoking cessation. Pharmacogenomics J 6: 115–119.
Lang UE, Sander T, Lohoff FW, Hellweg R, Bajbouj M, Winterer G et al (2007). Association of the met66 allele of brain-derived neurotrophic factor (BDNF) with smoking. Psychopharmacology (Berl) 190: 433–439.
Laucht M, Becker K, Frank J, Schmidt MH, Esser G, Treutlein J et al (2008). Genetic variation in dopamine pathways differentially associated with smoking progression in adolescence. J Am Acad Child Adolesc Psychiatry 47: 673–681.
Lee AM, Jepson C, Hoffmann E, Epstein L, Hawk LW, Lerman C et al (2007). CYP2B6 genotype alters abstinence rates in a bupropion smoking cessation trial. Biol Psychiatry 62: 635–641.
Lerer E, Kanyas K, Karni O, Ebstein RP, Lerer B (2006). Why do young women smoke? II. Role of traumatic life experience, psychological characteristics and serotonergic genes. Mol Psychiatry 11: 771–781.
Lerman C, Berrettini W (2003). Elucidating the role of genetic factors in smoking behavior and nicotine dependence. Am J Med Genet B Neuropsychiatr Genet 118B: 48–54.
Lerman C, Caporaso NE, Audrain J, Main D, Bowman ED, Lockshin B et al (1999). Evidence suggesting the role of specific genetic factors in cigarette smoking. Health Psychol 18: 14–20.
Lerman C, Caporaso NE, Bush A, Zheng YL, Audrain J, Main D et al (2001). Tryptophan hydroxylase gene variant and smoking behavior. Am J Med Genet 105: 518–520.
Lerman C, Jepson C, Wileyto EP, Epstein LH, Rukstalis M, Patterson F et al (2006). Role of functional genetic variation in the dopamine D2 receptor (DRD2) in response to bupropion and nicotine replacement therapy for tobacco dependence: results of two randomized clinical trials. Neuropsychopharmacology 31: 231–242.
Lerman C, Shields PG, Audrain J, Main D, Cobb B, Boyd NR et al (1998). The role of the serotonin transporter gene in cigarette smoking. Cancer Epidemiol Biomarkers Prev 7: 253–255.
Lerman C, Wileyto EP, Patterson F, Rukstalis M, Audrain-McGovern J, Restine S et al (2004). The functional mu opioid receptor (OPRM1) Asn40Asp variant predicts short-term response to nicotine replacement therapy in a clinical trial. Pharmacogenomics J 4: 184–192.
Lerman CE, Schnoll RA, Munafo MR (2007). Genetics and smoking cessation improving outcomes in smokers at risk. Am J Prev Med 33 (6 Suppl): S398–S405.
Lessov-Schlaggar CN, Pergadia ML, Khroyan TV, Swan GE (2008). Genetics of nicotine dependence and pharmacotherapy. Biochem Pharmacol 75: 178–195.
Lessov CN, Martin NG, Statham DJ, Todorov AA, Slutske WS, Bucholz KK et al (2004a). Defining nicotine dependence for genetic research: evidence from Australian twins. Psychol Med 34: 865–879.
Lessov CN, Swan GE, Ring HZ, Khroyan TV, Lerman C (2004b). Genetics and drug use as a complex phenotype. Subst Use Misuse 39: 1515–1569.
Lewis A, Miller JH, Lea RA (2007). Monoamine oxidase and tobacco dependence. Neurotoxicology 28: 182–195.
Li CY, Mao X, Wei L (2008). Genes and (common) pathways underlying drug addiction. PLoS Comput Biol 4: e2.
Li MD, Beuten J, Ma JZ, Payne TJ, Lou XY, Garcia V et al (2005). Ethnic- and gender-specific association of the nicotinic acetylcholine receptor alpha4 subunit gene (CHRNA4) with nicotine dependence. Hum Mol Genet 14: 1211–1219.
Li MD, Cheng R, Ma JZ, Swan GE (2003). A meta-analysis of estimated genetic and environmental effects on smoking behavior in male and female adult twins. Addiction 98: 23–31.
Li MD, Sun D, Lou XY, Beuten J, Payne TJ, Ma JZ (2007). Linkage and association studies in African- and Caucasian-American populations demonstrate that SHC3 is a novel susceptibility locus for nicotine dependence. Mol Psychiatry 12: 462–473.
Ling D, Niu T, Feng Y, Xing H, Xu X (2004). Association between polymorphism of the dopamine transporter gene and early smoking onset: an interaction risk on nicotine dependence. J Hum Genet 49: 35–39.
Liu Y, Yoshimura K, Hanaoka T, Ohnami S, Ohnami S, Kohno T et al (2005). Association of habitual smoking and drinking with single nucleotide polymorphism (SNP) in 40 candidate genes: data from random population-based Japanese samples. J Hum Genet 50: 62–68.
Lou XY, Ma JZ, Payne TJ, Beuten J, Crew KM, Li MD (2006). Gene-based analysis suggests association of the nicotinic acetylcholine receptor beta1 subunit (CHRNB1) and M1 muscarinic acetylcholine receptor (CHRM1) with vulnerability for nicotine dependence. Hum Genet 120: 381–389.
Lou XY, Ma JZ, Sun D, Payne TJ, Li MD (2007). Fine mapping of a linkage region on chromosome 17p13 reveals that GABARAP and DLG4 are associated with vulnerability to nicotine dependence in European-Americans. Hum Mol Genet 16: 142–153.
Ma JZ, Beuten J, Payne TJ, Dupont RT, Elston RC, Li MD (2005). Haplotype analysis indicates an association between the DOPA decarboxylase (DDC) gene and nicotine dependence. Hum Mol Genet 14: 1691–1698.
Maes HH, Sullivan PF, Bulik CM, Neale MC, Prescott CA, Eaves LJ et al (2004). A twin study of genetic and environmental influences on tobacco initiation, regular tobacco use and nicotine dependence. Psychol Med 34: 1251–1261.
Malaiyandi V, Sellers EM, Tyndale RF (2005). Implications of CYP2A6 genetic variation for smoking behaviors and nicotine dependence. Clin Pharmacol Ther 77: 145–158.
Mangold JE, Payne TJ, Ma JZ, Chen G, Li MD (2008). Bitter taste receptor gene polymorphisms are an important factor in the development of nicotine dependence in African Americans. J Med Genet 45: 578–582.
Marshall DL, Redfern PH, Wonnacott S (1997). Presynaptic nicotinic modulation of dopamine release in the three ascending pathways studied by in vivo microdialysis: comparison of naive and chronic nicotine-treated rats. J Neurochem 68: 1511–1519.
Mayhew KP, Flay BR, Mott JA (2000). Stages in the development of adolescent smoking. Drug Alcohol Depend 59 (Suppl 1): S61–S81.
McKinney EF, Walton RT, Yudkin P, Fuller A, Haldar NA, Mant D et al (2000). Association between polymorphisms in dopamine metabolic enzymes and tobacco consumption in smokers. Pharmacogenetics 10: 483–491.
Minematsu N, Nakamura H, Furuuchi M, Nakajima T, Takahashi S, Tateno H et al (2006). Limitation of cigarette consumption by CYP2A6*4, *7 and *9 polymorphisms. Eur Respir J 27: 289–292.
Morley KI, Lynskey MT, Madden PA, Treloar SA, Heath AC, Martin NG (2007). Exploring the inter-relationship of smoking age-at-onset, cigarette consumption and smoking persistence: genes or environment? Psychol Med 37: 1357–1367.
Morton LM, Wang SS, Bergen AW, Chatterjee N, Kvale P, Welch R et al (2006). DRD2 genetic variation in relation to smoking and obesity in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Pharmacogenet Genomics 16: 901–910.
Munafo MR, Elliot KM, Murphy MF, Walton RT, Johnstone EC (2007). Association of the mu-opioid receptor gene with smoking cessation. Pharmacogenomics J 7: 353–361.
Munafo MR, Johnstone EC (2008). Genes and cigarette smoking. Addiction 103: 893–904.
Munafo MR, Johnstone EC, Wileyto EP, Shields PG, Elliot KM, Lerman C (2006). Lack of association of 5-HTTLPR genotype with smoking cessation in a nicotine replacement therapy randomized trial. Cancer Epidemiol Biomarkers Prev 15: 398–400.
Murray S (2006). A smouldering epidemic. CMAJ 174: 309–310.
Nestler EJ (2005). Is there a common molecular pathway for addiction? Nat Neurosci 8: 1445–1449.
Noble EP, St Jeor ST, Ritchie T, Syndulko K, St Jeor SC, Fitch RJ et al (1994). D2 dopamine receptor gene and cigarette smoking: a reward gene? Med Hypotheses 42: 257–260.
Nussbaum J, Xu Q, Payne TJ, Ma JZ, Huang W, Gelernter J et al (2008). Significant association of the neurexin-1 gene (NRXN1) with nicotine dependence in European- and African-American smokers. Hum Mol Genet 17: 1569–1577.
O'Gara C, Knight J, Stapleton J, Luty J, Neale B, Nash M et al (2008). Association of the serotonin transporter gene, neuroticism and smoking behaviours. J Hum Genet 53: 239–246.
O'Gara C, Stapleton J, Sutherland G, Guindalini C, Neale B, Breen G et al (2007). Dopamine transporter polymorphisms are associated with short-term response to smoking cessation treatment. Pharmacogenet Genomics 17: 61–67.
Olsson C, Anney R, Forrest S, Patton G, Coffey C, Cameron T et al (2004). Association between dependent smoking and a polymorphism in the tyrosine hydroxylase gene in a prospective population-based study of adolescent health. Behav Genet 34: 85–91.
Osler M, Holst C, Prescott E, Sorensen TI (2001). Influence of genes and family environment on adult smoking behavior assessed in an adoption study. Genet Epidemiol 21: 193–200.
Ozaki S, Oyama T, Isse T, Kagawa N, Uramoto H, Sugio K et al (2006). Smoking cessation program and CYP2A6 polymorphism. Front Biosci 11: 2590–2597.
Park HJ, Kim ST, Yoon DH, Jin SH, Lee SJ, Lee HJ et al (2005). The association between the DRD2 TaqI A polymorphism and smoking cessation in response to acupuncture in Koreans. J Altern Complement Med 11: 401–405.
Pomerleau OF, Pomerleau CS, Chu J, Kardia SL (2007). Genome-wide linkage analysis for smoking-related regions, with replication in two ethnically diverse populations. Nicotine Tob Res 9: 955–958.
Prestes AP, Marques FZ, Hutz MH, Roman T, Bau CH (2007). Tobacco smoking and the ADRA2A C-1291G polymorphism. J Neural Transm 114: 1503–1506.
Radwan GN, El-Setouhy M, Mohamed MK, Hamid MA, Azem SA, Kamel O et al (2007). DRD2/ANKK1 TaqI polymorphism and smoking behavior of Egyptian male cigarette smokers. Nicotine Tob Res 9: 1325–1329.
Ray R, Jepson C, Patterson F, Strasser A, Rukstalis M, Perkins K et al (2006). Association of OPRM1 A118G variant with the relative reinforcing value of nicotine. Psychopharmacology (Berl) 188: 355–363.
Ray R, Jepson C, Wileyto EP, Dahl JP, Patterson F, Rukstalis M et al (2007a). Genetic variation in mu-opioid-receptor-interacting proteins and smoking cessation in a nicotine replacement therapy trial. Nicotine Tob Res 9: 1237–1241.
Ray R, Jepson C, Wileyto P, Patterson F, Strasser AA, Rukstalis M et al (2007b). CREB1 haplotypes and the relative reinforcing value of nicotine. Mol Psychiatry 12: 615–617.
Reuter M, Hennig J (2005). Pleiotropic effect of the TPH A779C polymorphism on nicotine dependence and personality. Am J Med Genet B Neuropsychiatr Genet 134B: 20–24.
Reuter M, Hennig J, Amelang M, Montag C, Korkut T, Hueweler A et al (2007). The role of the TPH1 and TPH2 genes for nicotine dependence: a genetic association study in two different age cohorts. Neuropsychobiology 56: 47–54.
Robinson JD, Lam CY, Minnix JA, Wetter DW, Tomlinson GE, Minna JD et al (2007). The DRD2 TaqI-B polymorphism and its relationship to smoking abstinence and withdrawal symptoms. Pharmacogenomics J 7: 266–274.
Rogausch A, Kochen MM, Meineke C, Hennig J (2007). Association between the BclI glucocorticoid receptor polymorphism and smoking in a sample of patients with obstructive airway disease. Addict Biol 12: 93–99.
Saal D, Dong Y, Bonci A, Malenka RC (2003). Drugs of abuse and stress trigger a common synaptic adaptation in dopamine neurons. Neuron 37: 577–582.
Saccone SF, Hinrichs AL, Saccone NL, Chase GA, Konvicka K, Madden PA et al (2007). Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs. Hum Mol Genet 16: 36–49.
SAMHSA (2006). Results from the 2005 National Survey on Drug Use and Health: National Findings (Office of Applied Studies, NSDUH Series H-30, DHHS Publication No. SMA 06-4194) DHHS: Rockville, MD.
Schlaepfer IR, Hoft NR, Collins AC, Corley RP, Hewitt JK, Hopfer CJ et al (2008). The CHRNA5/A3/B4 gene cluster variability as an important determinant of early alcohol and tobacco initiation in young adults. Biol Psychiatry 63: 1039–1046.
Schmidt K, Kins S, Schild A, Nitsch RM, Hemmings BA, Gotz J (2002). Diversity, developmental regulation and distribution of murine PR55/B subunits of protein phosphatase 2A. Eur J Neurosci 16: 2039–2048.
Schwab SG, Franke PE, Hoefgen B, Guttenthaler V, Lichtermann D, Trixler M et al (2005). Association of DNA polymorphisms in the synaptic vesicular amine transporter gene (SLC18A2) with alcohol and nicotine dependence. Neuropsychopharmacology 30: 2263–2268.
Segman RH, Kanyas K, Karni O, Lerer E, Goltser-Dubner T, Pavlov V et al (2007). Why do young women smoke? IV. Role of genetic variation in the dopamine transporter and lifetime traumatic experience. Am J Med Genet B Neuropsychiatr Genet 144B: 533–540.
Shields PG, Lerman C, Audrain J, Bowman ED, Main D, Boyd NR et al (1998). Dopamine D4 receptors and the risk of cigarette smoking in African-Americans and Caucasians. Cancer Epidemiol Biomarkers Prev 7: 453–458.
Skowronek MH, Laucht M, Hohm E, Becker K, Schmidt MH (2006). Interaction between the dopamine D4 receptor and the serotonin transporter promoter polymorphisms in alcohol and tobacco use among 15-year-olds. Neurogenetics 7: 239–246.
Smith SS, Fiore MC (1999). The epidemiology of tobacco use, dependence, and cessation in the United States. Prim Care 26: 433–461.
Spitz MR, Shi H, Yang F, Hudmon KS, Jiang H, Chamberlain RM et al (1998). Case-control study of the D2 dopamine receptor gene and smoking status in lung cancer patients. J Natl Cancer Inst 90: 358–363.
Stapleton JA, Sutherland G, O'Gara C (2007). Association between dopamine transporter genotypes and smoking cessation: a meta-analysis. Addict Biol 12: 221–226.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA et al (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102: 15545–15550.
Sullivan PF, Jiang Y, Neale MC, Kendler KS, Straub RE (2001). Association of the tryptophan hydroxylase gene with smoking initiation but not progression to nicotine dependence. Am J Med Genet 105: 479–484.
Sullivan PF, Kendler KS (1999). The genetic epidemiology of smoking. Nicotine Tob Res 1 (Suppl 2): S51–S57; discussion S69–70.
Sullivan PF, Neale BM, van den Oord E, Miles MF, Neale MC, Bulik CM et al (2004). Candidate genes for nicotine dependence via linkage, epistasis, and bioinformatics. Am J Med Genet B Neuropsychiatr Genet 126B: 23–36.
Sun D, Ma JZ, Payne TJ, Li MD (2008). Beta-arrestins 1 and 2 are associated with nicotine dependence in European American smokers. Mol Psychiatry 13: 398–406.
Swan GE, Valdes AM, Ring HZ, Khroyan TV, Jack LM, Ton CC et al (2005). Dopamine receptor DRD2 genotype and smoking cessation outcome following treatment with bupropion SR. Pharmacogenomics J 5: 21–29.
Takimoto T, Terayama H, Waga C, Okayama T, Ikeda K, Fukunishi I et al (2005). Cholecystokinin (CCK) and the CCKA receptor gene polymorphism, and smoking behavior. Psychiatry Res 133: 123–128.
Tarca AL, Draghici S, Khatri P, Hassan SS, Mittal P, Kim JS et al (2009). A novel signaling pathway impact analysis. Bioinformatics 25: 75–82.
Thomas MJ, Malenka RC (2003). Synaptic plasticity in the mesolimbic dopamine system. Philos Trans R Soc Lond B Biol Sci 358: 815–819.
Timberlake DS, Haberstick BC, Lessem JM, Smolen A, Ehringer M, Hewitt JK et al (2006). An association between the DAT1 polymorphism and smoking behavior in young adults from the National Longitudinal Study of Adolescent Health. Health Psychol 25: 190–197.
Tochigi M, Suzuki K, Kato C, Otowa T, Hibino H, Umekage T et al (2007). Association study of monoamine oxidase and catechol-O-methyltransferase genes with smoking behavior. Pharmacogenet Genomics 17: 867–872.
True WR, Xian H, Scherrer JF, Madden PA, Bucholz KK, Heath AC et al (1999). Common genetic vulnerability for nicotine and alcohol dependence in men. Arch Gen Psychiatry 56: 655–661.
Tyndale RF (2003). Genetics of alcohol and tobacco use in humans. Ann Med 35: 94–121.
Tyndale RF, Pianezza ML, Sellers EM (1999). A common genetic defect in nicotine metabolism decreases risk for dependence and lowers cigarette consumption. Nicotine Tob Res 1 (Suppl 2): S63–S67; discussion S69–70.
Uhl GR, Liu QR, Drgon T, Johnson C, Walther D, Rose JE (2007). Molecular genetics of nicotine dependence and abstinence: whole genome association using 520,000 SNPs. BMC Genet 8: 10.
Uhl GR, Liu QR, Drgon T, Johnson C, Walther D, Rose JE et al (2008). Molecular genetics of successful smoking cessation: convergent genome-wide association study results. Arch Gen Psychiatry 65: 683–693.
Vandenbergh DJ, O'Connor RJ, Grant MD, Jefferson AL, Vogler GP, Strasser AA et al (2007). Dopamine receptor genes (DRD2, DRD3 and DRD4) and gene-gene interactions associated with smoking-related behaviors. Addict Biol 12: 106–116.
Vink JM, Beem AL, Posthuma D, Neale MC, Willemsen G, Kendler KS et al (2004). Linkage analysis of smoking initiation and quantity in Dutch sibling pairs. Pharmacogenomics J 4: 274–282.
Vink JM, Smit AB, de Geus EJ, Sullivan P, Willemsen G, Hottenga JJ et al (2009). Genome-wide association study of smoking initiation and current smoking. Am J Hum Genet 84: 367–379.
Wonnacott S (1997). Presynaptic nicotinic ACh receptors. Trends Neurosci 20: 92–98.
Xian H, Scherrer JF, Madden PA, Lyons MJ, Tsuang M, True WR et al (2003). The heritability of failed smoking cessation and nicotine withdrawal in twins who smoked and attempted to quit. Nicotine Tob Res 5: 245–254.
Xu Q, Huang W, Payne TJ, Ma JZ, Li MD (2009). Detection of genetic association and a functional polymorphism of dynamin 1 gene with nicotine dependence in European and African Americans. Neuropsychopharmacology 34: 1351–1359.
Yu JH, Bigler J, Whitton J, Potter JD, Ulrich CM (2006a). Mismatch repair polymorphisms and colorectal polyps: hMLH1-93G>A variant modifies risk associated with smoking. Am J Gastroenterol 101: 1313–1319.
Yu Y, Panhuysen C, Kranzler HR, Hesselbrock V, Rounsaville B, Weiss R et al (2006b). Intronic variants in the dopa decarboxylase (DDC) gene are associated with smoking behavior in European-Americans and African-Americans. Hum Mol Genet 15: 2192–2199.
Yudkin P, Munafo M, Hey K, Roberts S, Welch S, Johnstone E et al (2004). Effectiveness of nicotine patches in relation to genotype in women versus men: randomised controlled trial. BMJ 328: 989–990.
Zeiger JS, Haberstick BC, Schlaepfer I, Collins AC, Corley RP, Crowley TJ et al (2008). The neuronal nicotinic receptor subunit genes (CHRNA6 and CHRNB3) are associated with subjective responses to tobacco. Hum Mol Genet 17: 724–734.
Zhang H, Ye Y, Wang X, Gelernter J, Ma JZ, Li MD (2006a). DOPA decarboxylase gene is associated with nicotine dependence. Pharmacogenomics 7: 1159–1166.
Zhang L, Kendler KS, Chen X (2006b). Association of the phosphatase and tensin homolog gene (PTEN) with smoking initiation and nicotine dependence. Am J Med Genet B Neuropsychiatr Genet 141B: 10–14.
Zhang L, Kendler KS, Chen X (2006c). The mu-opioid receptor gene and smoking initiation and nicotine dependence. Behav Brain Funct 2: 28.
Acknowledgements
This project was funded by the National Institutes of Health grants DA-12844 and DA-13783. We thank Dr David L Bronson for his excellent editing of this manuscript and Dr Tianhua Niu for help on pathway analysis via DAVID, GeneTrail, and Onto Pathway-Express.
Author information
Authors and Affiliations
Corresponding author
Additional information
Disclosure
The authors declare no conflict of interest.
Rights and permissions
About this article
Cite this article
Wang, J., Li, M. Common and Unique Biological Pathways Associated with Smoking Initiation/Progression, Nicotine Dependence, and Smoking Cessation. Neuropsychopharmacol 35, 702–719 (2010). https://doi.org/10.1038/npp.2009.178
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/npp.2009.178
Keywords
This article is cited by
-
Integrative systems and functional analyses reveal a role of dopaminergic signaling in myelin pathogenesis
Journal of Translational Medicine (2020)
-
Analyzing the genes related to nicotine addiction or schizophrenia via a pathway and network based approach
Scientific Reports (2018)
-
Significant association of the CHRNB3-CHRNA6 gene cluster with nicotine dependence in the Chinese Han population
Scientific Reports (2017)
-
Pathways to smoking behaviours: biological insights from the Tobacco and Genetics Consortium meta-analysis
Molecular Psychiatry (2017)
-
Network and Pathway-Based Analyses of Genes Associated with Parkinson’s Disease
Molecular Neurobiology (2017)
