7 pages psychology paper

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7 pages psychology paper
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Final Paper Outline Psychology 1 Learning Objectives – By working on this paper, you will: ▪ ▪ ▪ ▪ Understand how to identify and think about actual psychological research Learn more about a topic you’re interested in Become familiar with how to cite sources using APA formatting Practice developing a testable hypothesis Requirements – The steps to writing this paper: 1. Develop a question that will be the focus of your paper. This question should be something that you would genuinely be interested in finding the answer to. ◦ This could be almost anything that you have an interest in because, like I've said, psychologists can study anything people are involved in. ◦ Your question should be a) non-obvious, and b) specific. An obvious question is one that could be answered just by looking in your book or asking Google (e.g., “What is depression?”) A specific question goes beyond a general topic to a specific instance of that topic. For this class, that means fitting your question into the Framework for Thinking in Psychology: picking a level (neuroscience, cognitive science, or social science) and method (abnormal, individual differences, or developmental) for thinking about a topic. ▪ Non-specific question: “Why do we fall in love?” ▪ Specific question: “How does sexual attraction to a person change how we interact with them?” 2. Find three scholarly, empirical articles about your topic. ◦ A scholarly article is written by an expert in a particular field, and has gone through a process of peer review, meaning other experts have already evaluated the article and given the author feedback on what needs to be changed before it can be published. ◦ In an empirical article, the authors themselves have conducted some research that they are now writing about. If the article talks about someone else doing research (i.e., a review article), try to track down the original article. 3. Read the articles It’s fine if you don’t understand an article completely, but if you can’t get through it at all, then it might be better to find a different article. You should be able to identify and understand the key concepts of the research: ◦ Motivation: Why they wanted to do the research; what are the questions and ideas behind the research? ◦ Methods: What they did for their research; what did they measure (and manipulate, if it was an experiment) and how? ◦ Findings: What they found; relating back to the motivation and methods, how did it all turn out? 4. Write your paper Remember: the paper is starting with your question, and ending with your hypothesis – a statement about how you think something works, which could be tested to see if it’s false. The hypothesis should be answering the question you started with, or branching off of it. And that brings us to the... Sections of the paper and scoring rubric o Introduction (10 pts., 1-2 pages) – Introduce and explain your question. What is the topic that you're interested in? What does the reader need to know about this topic before heading into the rest of the paper? Make it engaging and interesting for the reader, and clearly state and explain your question. o Article Reviews (15 pts. each, 1-3 pages per article) – Describe the articles you found in detail. Again, answer the questions: why they wanted to do the research, what they did for their research, and what they found. § Your discussion for the motivation for the research should include the researchers' hypothesis. This should be a statement starting with “The researchers' hypothesis was...” § When you talk about what they found, state the main point of the research. This should be a single sentence that sums up the most important findings. Make sure you explain the important details, as well, but keep the big picture in mind. o Analysis (10 pts., 1-3 pages) – What are your conclusions regarding the research you just described? § Describe the limitations of the evidence presented in each study. Pay special attention to how different variables were defined and measured in each of the studies. Explain how this affects how we can interpret the findings. § Relate the findings of the studies you just discussed to each other, especially considering the main points of each. How are they similar? How are they different? Do they support each other or contradict each other? o Your hypothesis (10 pts., 1-2 pages) – Answer and expand on the question you started out with. § Science is an argument; use the findings you just discussed to support your position. Describe why you think what you do, and focus that into a short statement that could be tested – your hypothesis. § Your hypothesis is a testable prediction. Your hypothesis should be a single sentence (two sentences maximum) that starts with the words "I predict..." § Your hypothesis should be underlined. If it's not, you automatically lose one point (or, in other words, this is the easiest point in the paper). § Your hypothesis should be original. This is not a conclusion that resummarizes the research you discussed earlier, it is your own contribution to the topic. § Your hypothesis should be supported by specific evidence. Don't just say that a study supports your hypothesis—explain why. o Your references (15pts., 1 page + citations) – List, on a separate page, all the articles that you used in your paper in alphabetical order by the first author's last name. These should all be in APA format. For journal articles, you can use this template: Lastname, J., & Lastname, S. (year). Title of the article, capitalizing only the first letter: Or after a colon. Title of the Journal in Italics and Capitalized, volume #(issue #), page # - #. So… Castel, A. D. (2008). Metacognition and learning about primacy and recency effects in free recall: The utilization of intrinsic and extrinsic cues when making judgments of learning. Memory & Cognition, 36(2), 429-437. ◦ Overall quality (10 pts.) – How easy was it to understand the ideas that you expressed in your paper? To what extent did you follow these instructions? Spelling and grammar are not graded specifically, but may contribute to the overall quality score–if I can't understand what you're saying, I can't understand the idea. Additional Notes # Standard formatting – Must be typed, 12 point font, double-spaced, 1.25” margins, page numbers on the top; don’t forget to put your name on it. # You do not need to label the separate sections, and you should not be putting them on separate pages (except the References). You do not need an abstract. # Submit both an emailed and a hard copy. If one of these is not turned in by the due date, your paper will be considered late. # A final note about plagiarism: o Plagiarism is whenever you use another person's words or ideas without giving them credit. This is a serious issue in college, and could get you expelled. If you write some words or ideas that did not come directly from your own brain, you need to cite them. o Simply changing a few words does not work. This is still plagiarism. o If I see any plagiarism in your paper, I will subtract points. If there is substantial plagiarism, this means that you could end up with fewer points than you started with. o A good way to avoid any unintentional plagiarism is to read the article, and then put it aside. Don't look at it until after you've finished writing. This way, you're guaranteed that all your writing came from your own brain. Finally – "What the heck is a Likert scale?" It's just a fancy name for a numerical rating scale. For example: 0 (strongly disagree) -- 1 (disagree) -- 2 (neither agree not disagree) -- 3 (agree) -- 4 (strongly agree)
RESEARCH ARTICLE Pathways and Networks-Based Analysis of Candidate Genes Associated with Nicotine Addiction Meng Liu1, Rui Fan1, Xinhua Liu1, Feng Cheng2*, Ju Wang1* 1 School of Biomedical Engineering, Tianjin Medical University, Tianjin, China, 2 Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, Florida, United States of America * wangju@tmu.edu.cn (JW); fcheng1@health.usf.edu (FC) Abstract OPEN ACCESS Citation: Liu M, Fan R, Liu X, Cheng F, Wang J (2015) Pathways and Networks-Based Analysis of Candidate Genes Associated with Nicotine Addiction. PLoS ONE 10(5): e0127438. doi:10.1371/journal. pone.0127438 Academic Editor: Huiping Zhang, Yale University, UNITED STATES Received: November 26, 2014 Accepted: April 14, 2015 Published: May 12, 2015 Copyright: © 2015 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Nicotine is the addictive substance in tobacco and it has a broad impact on both the central and peripheral nervous systems. Over the past decades, an increasing number of genes potentially involved in nicotine addiction have been identified by different technical approaches. However, the molecular mechanisms underlying nicotine addiction remain largely unclear. Under such situation, a comprehensive analysis focusing on the overall functional characteristics of these genes, as well as how they interact with each other will provide us valuable information to understand nicotine addiction. In this study, we presented a systematic analysis on nicotine addiction-related genes to identify the major underlying biological themes. Functional analysis revealed that biological processes and biochemical pathways related to neurodevelopment, immune system and metabolism were significantly enriched in the nicotine addiction-related genes. By extracting the nicotine addiction-specific subnetwork, a number of novel genes associated with addiction were identified. Moreover, we constructed a schematic molecular network for nicotine addiction via integrating the pathways and network, providing an intuitional view to understand the development of nicotine addiction. Pathway and network analysis indicated that the biological processes related to nicotine addiction were complex. Results from our work may have important implications for understanding the molecular mechanism underlying nicotine addiction. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 31271411) and Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China to JW. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors declare that they have no competing interests. Introduction Cigarette smoking is a worldwide epidemic, and one of the major preventable causes of morbidity and mortality [1–2]. Although there are some effective control policies and interventions, the negative effect of tobacco abuse on public health and social economy is still astonishing, highlighting the need for continuing efforts. According to World Health Organization (WHO), currently there are about 1.3 billion smokers worldwide, most of whom come from the low- or middle-income countries; and it is estimated that more than 5 million PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 1 / 17 Pathway Analysis of Nicotine Addiction-Related Genes smokers die from smoking-related diseases every year [3–4]. If effective measures are not adopted, by 2020, smoking will become the biggest health problem worldwide, and the number of deaths caused by smoking will reach 10 million per year [5]. Besides the health problems, smoking also causes heavy economic burden on society. According to the Centers for Disease Control and Prevention (CDC), in USA alone, the economic burden made by smoking to society, including both the direct health care expenditures and the loss of productivity, can be as high as $193 billion a year [6]. Therefore, developing effective approaches and drugs for the treatment and prevention of smoking are of huge challenge in public health. Nicotine, as the primary psychoactive component of tobacco smoke, binds to neuronal nicotinic acetylcholine receptors (nAChRs), a family of ligand-gated ion channels [7], facilitating various neurotransmitter release such as dopamine, glutamate, serotonin and γ-aminobutyric acid (GABA) [8–10] and thereby producing a number of neurophysiological and behavioral effects. Emerging evidence suggests that repeated exposure to nicotine can alter the level or types of genes expressed in multiple brain regions and such alteration ultimately mediates the functions of the related neurons and neural circuits. Numerous studies aiming to discover genetic variants or candidate genes, such as genome-wide association studies, genome-wide linkage scan, gene expression and candidate gene association studies, have found a large number of promising genes and chromosomal regions involved in the etiology of nicotine addiction [11– 13]. Moreover, various neural pathways and transmitter systems have emerged as compelling candidates for the processing of addictive properties of nicotine, which provide a valuable resource to unravel the molecular mechanism underlying nicotine addiction. Through its interaction directly or indirectly with these genes and biological pathways, nicotine evokes multiple effects in the central nervous system. During the past decade, rapid advances in high-throughput technologies have brought unprecedented opportunities for the large-scale analysis of the nicotine addiction-related genes/ proteins, leading to a rapid generation of large-scale nicotine addiction-related data. These datasets are often heterogeneous and multi-dimensional, which makes integrating and arranging such datasets to ascertain the key molecular mechanisms and to transform the data into meaningful biological phenomenon a major task and challenge. To meet the demand, pathway and network-based analyses have become an important and powerful approach to elucidate the biological implications underlying complex diseases [14–16]. Such a systems biology approach could be pivotal for better understanding of mental disorder at the molecular level [17]. Thus, a comprehensive analysis of the nicotine addiction-related candidate genes within a systematic framework may provide us important insights on the molecular mechanisms underlying nicotine addiction. In this study, we performed a systematic analysis on genes potentially involved in nicotine addiction by identifying the enriched functional categories and pathways, as well as examining the crosstalk among the significantly enriched pathways. Then, we extracted a nicotine addiction-specific network and constructed a molecular network of nicotine addiction. Materials and Methods Data sources In this study, the candidate genes for nicotine addiction included 220 genes prioritized via a multi-source-based gene approach [18]. Briefly, genes identified to be related to nicotine addiction or involved in the physiological response to nicotine exposure or smoking behaviors were collected from different sources, including genetic association analysis, genetic linkage analysis, high throughput gene/protein expression analysis and/or literature search of single gene/protein-based studies. Based on these resources, the 11,781 genes collected were scored and a PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 2 / 17 Pathway Analysis of Nicotine Addiction-Related Genes weight value was determined for each category. The overall relation between a gene and nicotine addiction was measured by a combined score derived from its scores in the four categories. Then, the genes were ranked according to the combined scores with a larger score value indicating a potentially higher correlation between the gene and nicotine addiction. Based on the distribution of the combined score of all the genes, 220 genes on the top of the list (i.e., with the largest combined scores) were selected as the prioritized nicotine addiction-related genes (NAGenes). The human protein-protein interaction (PPI) data were downloaded from the Protein Interaction Network Analysis (PINA) platform (May 21, 2014) [19], which integrated data from six major public PPI databases, namely IntAct, BioGRID, MINT, DIP, HPRD, and MIPS/MPact. Also, we downloaded the related annotation files from NCBI (ftp://ftp.ncbi.nlm.nih.gov/gene/) (May 24, 2014), including the Entrez gene information database of human (Homo_sapiens. gene_info.gz), the dataset specifying relationship between pairs of NCBI and UniProtKB protein accessions (gene_refseq_uniprotkb_collab.dz), and file containing mappings of Entrez Gene records to Entrez RefSeq Nucleotide sequence records (gene2refseq.gz). Then the human PPI data were mapped to NCBI human protein-coding genes and the unmapped proteins were discarded. After removing self-interactions and redundant interacting pairs, a final human PPI network containing 15,093 nodes and 161,419 edges was obtained. Functional enrichment analysis To examine the functional features of NAGenes, WebGestalt [20] and Ingenuity Pathway Analysis system (IPA; https://analysis.ingenuity.com) were applied for functional enrichment analysis, including Gene Ontology (GO) term analysis and pathway analysis. WebGestalt is a web-based integrated data mining system to evaluate the significance of GO terms enrichment in the candidate genes. IPA is designed to identify global canonical pathways from a given list of genes. Basically, the genes with their symbol and/or corresponding GenBank Accession Numbers are uploaded into the IPA and compared with the genes included in each canonical pathway. All the pathways with one or more genes overlapping the candidate genes are extracted, with each of them assigned a p value to denote the probability of overlap between the pathway and the input genes via Fisher’s exact test. Then, the corresponding multiple testing correction p-value is calculated with the method of Benjamini and Hockberg, namely PBHvalue [21]. Pathway crosstalk We further performed pathway crosstalk analysis to explore the interactions among significantly enriched pathways. To describe the overlap between any given pair of pathways, we inj and the Overlap troduced two measurements, i.e., the Jaccard Coefficient ðJCÞ ¼ j A\B A[B jA\Bj Coefficient ðOCÞ ¼ minðjAj;jBjÞ , where A and B are the lists of genes included in the two tested pathways. To construct the pathway crosstalk, we implemented the following procedure: 1. Select a set of pathways for crosstalk analysis. Only the pathways with PBH-value less than 0.01 were used. Meanwhile, the pathways containing less than 5 candidate genes were removed because pathways with too few genes may have insufficient biological information. 2. Count the number of shared candidate genes between any pair of pathways. Pathway pair with less than 3 overlapped genes was removed. 3. Calculate the overlap of all pathway pairs and rank them. All the pathway pairs were ranked according to their JC and OC values. PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 3 / 17 Pathway Analysis of Nicotine Addiction-Related Genes 4. Visualize the selected pathway crosstalk. To focus on the most important biological themes, we only chose those crosstalks with scores in the top 20% using the software Cytoscape [22]. Of note, this criterion was set up somewhat arbitrarily, but the results were well displayed with an appropriate number of nodes (pathways) and edges (crosstalk). We also used other criteria to select the crosstalks among pathways, but the results were similar. Construction of nicotine addiction-specific network Steiner minimal tree algorithm uses a greedy search strategy to merge the smaller trees into larger ones until only one tree connecting all input seeds is built [23]. We applied GenRev [24], a network-based software package to search the optimal intermediate nodes (genes) for the connection of input seed genes via the Steiner minimal tree algorithm, to extract a subnetwork from the human PPI network by using the 220 NAGenes as seeds. To test the non-randomness of this subnetwork, we first generated 1000 random networks with the same number of nodes and edges as the nicotine addiction-specific network using Erdos-Renyi model in R igraph package [25]. Then, we calculated the average values of the shortest-path distance and clustering coefficient. By counting the number of random networks with average shortest-path distance (nL) smaller than that of the nicotine addiction-specific network and the number of random networks with average clustering coefficient (nC) higher than the observed clustering coefficient, we were able to estimate the significance of non-randomness. Finally, we calculated the empirical p-value = nL/1000 and nC/1000, respectively. Results GO enrichment analysis in nicotine addiction-related genes The nicotine addiction-related genes (NAGenes) were involved in diverse biological functions (S1 Table). For example, some genes were related to synaptic transmission, such as the nicotinic cholinergic receptors (e.g., CHRNA1, CHRNA4, CHRNA7, CHRNA10, and CHRNB2) and dopamine receptors (DRD1, DRD2, DRD3, DRD4 and DRD5); some genes were involved in drug metabolism, such as sulfotransferase 1A1 (SULT1A1), alcohol dehydrogenase 1B (ADH1B), aldehyde dehydrogenase 2 (ALDH2), cytochrome P450 17A1 (CYP17A1) and CYP1A1; some genes were related to cellular transport, e.g., solute carrier family 18 (vesicular monoamine) member 2 (SLC18A2), solute carrier family 6 (neurotransmitter transporter, serotonin) member 4 (SLC6A4) and solute carrier family 9 (sodium/hydrogen exchanger) member 9 (SLC9A9). Functional enrichment analysis revealed a more specific function pattern of these genes. Among the GO terms significantly enriched in the candidate genes (Table 1), including those associated with neurodevelopment or synaptic transmission. In the biological process, terms directly related to neurodevelopment, e.g., synaptic transmission (PBH = 1.21×10–36), transmission of nerve impulse (PBH = 1.69×10–36) and neurological system process (PBH = 3.23×10–32) were identified (Table 1). Consistently, for the molecular function category, terms related to the activity of neurotransmitter receptor or channel were enriched, such as neurotransmitter receptor activity (PBH = 1.36×10–26), excitatory extracellular ligand-gated ion channel activity (PBH = 2.62×10–22) and acetylcholine receptor activity (PBH = 5.12×10–22). In the cellular component category, the significantly enriched terms included synaptic membrane (PBH = 5.18×10–27), neuron projection (PBH = 4.51×10–24), axon (PBH = 3.55×10–21), dendritic spine (PBH = 9.87×10–10). Similarly, GO terms related to drug response (e.g., response to alkaloid, response to nicotine, and response to alcohol) and metabolism (e.g., monooxygenase activity, and oxidoreductase activity), were also enriched in NAGenes. These results were consistent PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 4 / 17 Pathway Analysis of Nicotine Addiction-Related Genes Table 1. Gene Ontology terms enriched in nicotine addiction-related genes (NAGenes). GO Termsa No. of genesb P-valuec PBHvalued Biological process GO:0007268 synaptic transmission 67 3.07×10–39 1.21×10–36 GO:0007267 cell-cell signaling 83 3.85×10 1.21×10–36 GO:0019226 transmission of nerve impulse 70 6.75×10 1.69×10–36 GO:0035637 multicellular organismal signaling 70 2.80×10 5.86×10–36 GO:0050877 neurological system process 82 2.06×10 3.23×10–32 GO:0043279 response to alkaloid 25 –25 2.11×10 1.89×10–23 GO:0014070 response to organic cyclic compound 50 2.74×10–25 2.29×10–23 GO:0042493 response to drug 41 8.02×10 6.29×10–23 GO:0097305 response to alcohol 23 2.64×10 1.18×10–19 GO:0031644 regulation of neurological system process 30 4.13×10 1.79×10–19 GO:0050890 cognition 27 9.03×10 3.78×10–19 GO:0007611 learning or memory 26 1.36×10 5.51×10–19 GO:0035094 response to nicotine 15 3.61×10 1.42×10–18 GO:0023061 signal release 35 –20 4.76×10 1.76×10–18 GO:0015837 amine transport 21 5.68×10–20 2.04×10–18 GO:0042417 dopamine metabolic process 14 9.05×10 3.15×10–18 GO:0051952 regulation of amine transport 18 9.50×10 3.22×10–18 GO:0051969 regulation of transmission of nerve impulse 28 1.14×10 3.76×10–18 GO:0050804 regulation of synaptic transmission 27 1.20×10 3.86×10–18 GO:0044057 regulation of system process 39 –19 1.28×10 4.02×10–18 GO:0030594 neurotransmitter receptor activity 25 8.57×10–29 1.36×10–26 GO:0004889 acetylcholine-activated cation-selective channel activity 14 6.47×10–25 5.14×10–23 GO:0005230 extracellular ligand-gated ion channel activity 22 4.94×10 2.62×10–22 GO:0005231 excitatory extracellular ligand-gated ion channel activity 19 1.49×10 5.12×10–22 GO:0015464 acetylcholine receptor activity 14 1.61×10 5.12×10–22 GO:0015276 ligand-gated ion channel activity 24 5.08×10 1.15×10–18 GO:0022839 ion gated channel activity 30 1.62×10 2.58×10–16 GO:0038023 signaling receptor activity 60 3.29×10 4.76×10–16 GO:0042166 acetylcholine binding 10 –17 5.34×10 7.08×10–16 GO:0005261 cation channel activity 27 7.74×10–16 6.48×10–15 GO:0022838 substrate-specific channel activity 32 2.30×10 1.83×10–14 GO:0004888 transmembrane signaling receptor activity 53 3.11×10 2.06×10–13 GO:0035240 dopamine binding 8 4.89×10 3.11×10–13 GO:0015075 ion transmembrane transporter activity 38 9.12×10 5.58×10–11 GO:0008324 cation transmembrane transporter activity 32 9.86×10 5.81×10–11 GO:0022891 substrate-specific transmembrane transporter activity 38 7.70×10 4.22×10–10 GO:0004952 dopamine receptor activity 5 –10 4.67×10 2.40×10–9 GO:0004497 monooxygenase activity 13 4.86×10–10 2.41×10–9 GO:0016705 oxidoreductase activity, acting on paired donors, withincorporation or reduction of molecular oxygen 16 8.54×10 4.11×10–9 GO:0008227 G-protein coupled amine receptor activity 9 3.52×10–9 1.65×10–8 GO:0031406 carboxylic acid binding 16 1.16×10 5.12×10–8 GO:0016597 amino acid binding 12 –8 1.69×10 7.26×10–8 GO:0035254 glutamate receptor binding 7 3.26×10–8 1.36×10–7 –39 –39 –38 –34 –25 –21 –21 –21 –20 –20 –20 –20 –19 –19 Molecular function –24 –23 –23 –20 –17 –17 –15 –14 –14 –12 –12 –11 –10 –8 (Continued) PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 5 / 17 Pathway Analysis of Nicotine Addiction-Related Genes Table 1. (Continued) GO Termsa No. of genesb P-valuec PBHvalued 13 5.60×10–7 2.23×10–6 GO:0005506 iron ion binding GO:0097060 synaptic membrane 35 7.51×10–29 5.18×10–27 GO:0045211 postsynaptic membrane 32 2.29×10 1.05×10–25 GO:0043005 neuron projection 50 1.96×10 GO:0005887 integral to plasma membrane 65 2.72 ×10 5.36×10–23 GO:0031226 intrinsic to plasma membrane 65 1.92×10–23 3.31×10–22 GO:0005892 acetylcholine-gated channel complex 13 8.13×10–23 1.25×10–21 GO:0030424 axon 33 2.57×10 3.55×10–21 GO:0043235 receptor complex 25 7.89×10 9.07×10–19 GO:0030054 cell junction 43 2.51×10 2.48×10–16 GO:0030425 dendrite 30 1.58×10 1.36×10–15 GO:0033267 axon part 20 3.63×10 2.95×10–15 GO:0044306 neuron projection terminus 16 1.11×10 8.51×10–15 GO:0043679 axon terminus 15 –15 5.22×10 3.43×10–14 GO:0043025 neuronal cell body 26 5.04×10–15 3.43×10–14 GO:0043197 dendritic spine 16 1.86×10 9.87×10–10 GO:0044309 neuron spine 16 1.86×10 9.87×10–10 GO:0042734 presynaptic membrane 10 1.54×10 7.59×10–9 GO:0016023 cytoplasmic membrane-bounded vesicle 30 4.81×10 2.07×10–6 GO:0008328 ionotropic glutamate receptor complex 6 1.43×10 5.80×10–6 GO:0044327 dendritic spine head 10 2.19×10 8.17×10–6 GO:0014069 postsynaptic density 10 2.19×10 8.17×10–6 Cellular component –27 –25 – 24 –22 –20 –17 –16 –16 –15 –10 –10 –9 –7 –6 –6 –6 4.51×10–24 a. Only GO terms with hierarchical level%4 and containing 5 or more nicotine addiction-related genes are shown. b. Number of genes in the 220 nicotine addiction-related genes and also in the category c. P-value were calculated by hypergeometric test d. PBH-value were adjusted by Benjamini & Hochberg (BH) method doi:10.1371/journal.pone.0127438.t001 with the pathophysiological background of nicotine addiction, which also indicated the candidate genes are relatively reliable for the following up bioinformatics analysis. Pathway enrichment analysis in NAGenes Identifying biological pathways enriched in the candidate genes may provide important information for our understanding of the molecular mechanism underlying nicotine addiction. We searched for enriched pathways in the NAGenes using IPA and found 97 significant enrichment pathways (PBH$0.01) (S2 Table). The 20 most significantly enrichment pathways are shown in Table 2. Most of the pathways were related to neurotransmission system, consistent with the fact that nicotine addiction is a neuronal disease. Among them, several pathways associated with monoamine neurotransmitters stood out, e.g., dopamine receptor signaling (ranked 4th), serotonin receptor signaling (ranked 11th), glutamate receptor signaling (ranked 14th) and GABA receptor signaling(ranked 19th), all of which play important roles in signaling transduction. Moreover, two pathways, synaptic long term potentiation (PBH = 1.07×10–3) and synaptic long term depression (PBH = 8.13×10–3) were enriched in the NAGenes (S2 Table). These two pathways were critical in synaptic plasticity development and have been reported to be PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 6 / 17 Pathway Analysis of Nicotine Addiction-Related Genes Table 2. Pathways enriched in nicotine addiction-related genes (NAGenes) (top 20 pathways). Canonical Pathways cAMP-mediated signaling Calcium Signaling G-Protein Coupled Receptor Signaling Dopamine Receptor Signaling P-valuea 6.31×10– 17 PBHvalueb NAGenes included in the pathwayc 2.00×10– ADRA2A, ADRB2, AGTR1, AKAP13, CAMK4, CHRM1, CHRM2, CHRM5, CNR1, CREB1, DRD1, DRD2, DRD3, DRD4, DRD5, GABBR1, GABBR2, GNAS, GRM7, HTR1F, HTR6, NPY1R, OPRM1, PDE4D, RAPGEF3 2.00×10– CAMK4, CHRNA1, CHRNA10, CHRNA2, CHRNA3, CHRNA4, CHRNA5, CHRNA6, CHRNA7, CHRNB1, CHRNB2, CHRNB3, CHRNB4, CHRND, CHRNG, CREB1, GRIK1, GRIN2A, GRIN2B, GRIN3A, ITPR2, TRPC7 3.16×10– ADRA2A, ADRB2, AGTR1, CAMK4, CHRM1, CHRM2, CHRM5, CNR1, CREB1, DRD1, DRD2, DRD3, DRD4, DRD5, GABBR1, GABBR2, GNAS, GRM7, HTR1F, HTR2A, HTR6, NPY1R, OPRM1, PDE4D, RAPGEF3 2.51×10– COMT, DRD1, DRD2, DRD3, DRD4, DRD5, GNAS, MAOA, MAOB, NCS1, PPP1R1B, PPP2R2B, SLC18A2, SLC6A3, TH 14 1.00×10– 15 13 2.51×10– 15 13 3.16×10– 14 12 12 10 1.41×10– ABCB1, AHR, CAMK4, CYP1A1, CYP2B6, FMO1, GSTM1, GSTM3, GSTP1, GSTT1, IL6, MAOA, MAOB, MAP3K4, MGMT, NOS2, NQO1, PPP2R2B, SOD3, SULT1A1, TNF, UGT1A9, UGT2B10 2.69×10– 1.45×10–8 CAMK4, CREB1, DRD1, DRD2, DRD3GRIN3A, DRD4, DRD5, GNAS, GRIN2A, GRIN2B, ITPR2, KCNJ6, PPP1R1B, PPP2R2B, PRKG1 2.88×10– 1.45×10–8 AHR, CCND1, CHEK2, CYP1A1, ESR1, GSTM1, GSTM3, GSTP1, GSTT1, IL6, MDM2, NQO1, TGFB1, TNF, TP53 4.37×10– 1.78×10–8 ABCB1, ABCC4, APOE, CD14, CETP, CYP2A6, CYP2B6, FMO1, GSTM1, GSTM3, GSTP1, GSTT1, MAOA, MAOB, MGMT, SOD3, SULT1A1, TNF 4.57×10– 1.78×10–8 ADRA2A, AGTR1, CHRM2, CNR1, DRD2, DRD3, DRD4, GABBR1, GABBR2, GNAS, GRM7, HTR1F, NPY1R, OPRM1 Superpathway of Melatonin Degradation 3.72×10–9 1.32×10–7 CYP1A1, CYP2A6, CYP2B6, CYP2D6, MAOA, MAOB, MPO, SULT1A1, UGT1A9, UGT2B10 Serotonin Receptor Signaling 6.17×10–9 1.95×10–7 HTR2A, HTR6, MAOA, MAOB, SLC18A2, SLC6A4, TPH1, TPH2 eNOS Signaling 1.17×10 –8 3.39×10–7 CAMK4, CHRNA10, CHRNA3, CHRNA4, CHRNA5, CHRNB1, CHRNB4, ESR1, GNAS, HSPA4, ITPR2, NOS3, PRKG1 Glucocorticoid Receptor Signaling 3.89×10–8 1.05×10–6 ADRB2, CCNH, CREB1, ERCC2, ESR1, HSPA4, ICAM1, IFNG, IL13, IL6, IL8, NOS2, NPPA, NR3C1, PTGS2, TGFB1, TNF Glutamate Receptor Signaling 5.13×10–8 1.29×10–6 CAMK4, DLG4, GRIK1, GRIK2, GRIN2A, GRIN2B, GRIN3A, GRM7, SLC1A2 Neuropathic Pain Signaling In Dorsal Horn Neurons 5.50×10 –7 1.29×10–5 BDNF, CAMK4, CREB1, GRIN2A, GRIN2B, GRIN3A, GRM7, ITPR2, KCNQ3, NTRK2 AMPK Signaling 1.10×10–6 2.40×10–5 ADRA2A, ADRB2, CHRNA10, CHRNA3, CHRNA4, CHRNA5, CHRNB1, CHRNB4, GNAS, NOS3, PPP2R2B Hepatic Cholestasis 1.58×10–6 3.09×10–5 ABCB1, CD14, CETP, ESR1, GNAS, IFNG, IL6, IL8, MAP3K4, SLCO3A1, TNF Gαs Signaling 1.58×10–6 3.09×10–5 ADRB2, CHRM1, CHRM5, CNR1, CREB1, DRD1, DRD5, GNAS, HTR6, RAPGEF3 GABA Receptor Signaling 1.95×10 3.47×10 DNM1, GABARAP, GABBR1, GABBR2, GABRA2, GABRA4, GABRE PXR/RXR Activation 2.00×10–6 Xenobiotic Metabolism Signaling 2.00×10– Dopamine-DARPP32 Feedback in cAMP Signaling 10 Aryl Hydrocarbon Receptor Signaling 10 LPS/IL-1 Mediated Inhibition of RXR Function 10 Gαi Signaling a 10 –6 –5 3.47×10–5 ABCB1, CYP2A6, CYP2B6, GSTM1, IL6, NR3C1, TNF, UGT1A9 P-value were calculated by Fisher’s exact test b PBH-value were adjusted by Benjamini & Hochberg (BH) method c 220 nicotine addiction-related genes included in the pathway doi:10.1371/journal.pone.0127438.t002 involved in addiction [26–27]. In addition, we also highlighted other significantly enriched pathways, i.e., cAMP-mediated signaling, calcium signaling, G-protein coupled receptor signaling, neuropathic pain signaling in dorsal horn neurons and CREB signaling in neurons. This result was consistent with prior knowledge of nicotine addiction [28–30], providing valuable evidence for the study of molecular mechanism underlying nicotine addiction. Of note, we found many pathways that were related to immune system in the list, in line with previous reports that nicotine might have effects on organism’s immune [2, 31]. Moreover, three pathways PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 7 / 17 Pathway Analysis of Nicotine Addiction-Related Genes related to retinoid X receptor (RXR) were found to be enriched in the NAGenes, i.e., LPS/IL-1 mediated inhibition of RXR function, PXR (pregnane X receptor)/RXR activation and LXR (liver X receptor)/RXR activation. RXR, a kind of nuclear receptor, is a master regulator during ligand-induced transcription activities [32]. In summary, these results suggest that neurodevelopment, immune and metabolic systems play important roles in the pathogenesis of nicotine addiction. Crosstalk among significantly enriched pathway To take a further step beyond identifying lists of significantly enriched pathways and to understand how they interact with each other, we performed a pathway crosstalk analysis among the 97 significantly enriched pathways. The approach was based on the assumption that two pathways were considered to crosstalk if they shared a proportion of NAGenes [16]. There were 74 pathways containing 5 or more members in NAGenes, of which, 72 pathways met the criterion for crosstalk analysis, i.e., each pathway shared at least 3 genes with one or more other pathways. There were a total of 380 pathway pairs (edges) from the 72 pathways and then we ranked these edges according to the average scores of the JC and the OC. Ultimately, we chose the top 20% edges to construct the pathway crosstalk. Based on their crosstalk, the pathways could be roughly grouped into three major modules, each of which included pathways shared more crosstalks compared with other pathways and may likely be involved in the same or similar biological process (Fig 1). One module mainly consisted of neurodevelopment-related signaling pathways, such as glutamate receptor signaling, synaptic long term potentiation and CREB signaling in neurons. The second module was primarily dominated by immune systemrelated pathways, including role of cytokines in mediating communication between immune cells, T helper cell differentiation and others. Another module composed of the metabolic pathways of neurotransmitters or drug, such as nicotine degradation II, dopamine degradation and serotonin degradation, the roles of these pathways in nicotine addiction have not been fully explored. As indicated by the above results, pathway crosstalk analysis can provide important insights for understanding of nicotine addiction mechanisms. Nicotine addiction-specific network To distill insight into the interaction of NAGenes in a local environment, we extracted the specific subnetwork of nicotine addiction (NA-specific network) from the human PPI network using the Steiner minimal tree algorithm. Basically, this approach linked as many as possible members of NAGenes via the minimal number of connections. As shown in Fig 2, the subnetwork contained 252 nodes and 591 edges. Of the 220 NAGenes, 208 were included in the NAspecific network, which accounted for approximately 94.5% of the candidate genes and 82.5% of the genes in the NA-specific network, indicating a high coverage of NAGenes in the subnetwork. Of note, some of the 44 additional genes, e.g., calmodulin 2 (CALM2), calnexin (CANX), caveolin-1 (CAV1), glutathione S-transferase omega 1 (GSTO1) and protein phosphatase 1 (PPP1CA), had been reported to be associated with addiction in previously studies (Table 3) [33–34]. Moreover, to test randomness of the NA-specific network, 1000 random subnetworks were generated using the Erdos-Renyi model and their average shortest-path distance and average clustering coefficient were compared with the corresponding values of the NA-specific network. For these random subnetworks, the average shortest-path distance was 3.72, which was significantly larger than that of the NA-specific network (shortest-path distance, 2.87; empirical p value < 0.001). The average clustering coefficient of the random subnetworks was 0.02, which was significantly smaller than that of the NA-specific network (clustering coefficient, PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 8 / 17 Pathway Analysis of Nicotine Addiction-Related Genes Fig 1. Pathway crosstalk among NAGenes-enriched pathways. Nodes represent pathways and edges represent crosstalk between pathways. Node size corresponds to the number of NAGenes found in the corresponding pathway. Node color corresponds to the PBH-value of the corresponding pathway. Darker color indicates lower PBH-value. Edge width corresponds to the score of the related pathways. Node shape indicates pathway categories, with ellipse for neurodevelopment, diamond for immune, triangle for metabolism, square for other pathways. doi:10.1371/journal.pone.0127438.g001 0.25; empirical p value < 0.001). Thus, the NA-specific network extracted from the whole PPI network was a non-random network. Molecular network of nicotine addiction Summarizing the results from pathway analysis and network analysis, we were able to obtain a relatively comprehensive view about nicotine addiction (Fig 3). In such network, a number of key genes and pathways played important roles, e.g., the neurotransmitters receptor signaling transduction pathways such as dopamine receptor signaling and serotonin receptor signaling, and several intracellular signaling transduction cascades such as cAMP-mediated signaling, Gprotein coupled receptor signaling and calcium signaling. The network also included several feedback loops, among which the one from N-methyl-D-aspartate subtype glutamate receptor (NMDAR) to a-amino-3-hydroxyl- 5-methylisoxazole-4-propionic acid subtype glutamate receptor (AMPAR) would be considered the shortest loops. We further observed that some loops PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 9 / 17 Pathway Analysis of Nicotine Addiction-Related Genes Fig 2. Nicotine addiction-specific network. Ellipse nodes are NAGenes and triangular nodes are non-NAGenes. Node color corresponds to its degree in the human PPI network. Darker color indicates higher degree. doi:10.1371/journal.pone.0127438.g002 interlinked with each other through CaM (also called CALM) and calcium/calmodulin-dependent protein kinase II (CAMKII). CaM and CAMKII both play important roles in the induction of long term potentiation and long term depression, indicating that they might make contribution to the synaptic plasticity development and they might provide clues to explain the irreversible features of nicotine addiction. In addition, we observed that mitogen-activated protein kinase (MAPK), nuclear factor of kappa light polypeptide gene enhancer in B-cells (NFκB) and related pathways of NF-κB signaling and MAPK signaling also implicated in the process of nicotine addiction, which might be the valuable candidate genes or pathways. Discussion In the past decades, we have made considerable progress in understanding the molecular mechanisms underlying nicotine addiction, which is largely due to the identification of various neurotransmitter receptors, genes or pathways associated with addiction and the development of animal or cell models. Additionally, with the development of high throughput analysis technology, more and more genes/proteins have been suggested to be linked to nicotine addiction and provide a valuable resource to analyze candidate genes function, biochemical pathways and networks related to nicotine addiction. In this study, we provided a comprehensive analysis of the functional features and interaction network of nicotine addiction-related genes. PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 10 / 17 Pathway Analysis of Nicotine Addiction-Related Genes Table 3. Genes included in the NA-specific network but not of NAGenes. Gene symbol Gene name ADIPOQ Adiponectin APOA1 Apolipoprotein A-I APP Amyloid beta (A4) precursor protein B3GNT1 UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 1 BAG6 BCL2-associated athanogene 6 CALM2 Calmodulin 2 CANX Calnexin CASK Calcium/calmodulin-dependent serine protein kinase CAV1 Caveolin 1 CFTR Cystic fibrosis transmembrane conductance regulator COL4A3 Collagen, type IV, alpha 3 CTSH Cathepsin H CYB5A Cytochrome b5 type A EEF1A1 Eukaryotic translation elongation factor 1 alpha 1 ELAVL1 ELAV like RNA binding protein 1 FBLN5 Fibulin 5 FLNA Filamin A, alpha GNA15 Guanine nucleotide binding protein (G protein), alpha 15 (Gq class) GPRASP1 G protein-coupled receptor associated sorting protein 1 GRB2 Growth factor receptor-bound protein 2 GSTO1 Glutathione S-transferase omega 1 IL4R Interleukin 4 receptor IL6ST Interleukin 6 signal transducer ITPR3 Inositol 1,4,5-trisphosphate receptor, type 3 LSM8 LSM8 homolog, U6 small nuclear RNA associated MAPK14 Mitogen-activated protein kinase 14 MMS19 MMS19 nucleotide excision repair homolog NFKB2 Nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 NRP1 Neuropilin 1 NUP98 Nucleoporin 98kDa PAXIP1 PAX interacting (with transcription-activation domain) protein 1 PDZD3 PDZ domain containing 3 POLR2J Polymerase (RNA) II (DNA directed) polypeptide J PPP1CA Protein phosphatase 1, catalytic subunit,alpha isozyme PRADC1 Protease-associated domain containing 1 PRKCZ Protein kinase C, zeta SPP1 Secreted phosphoprotein 1 SPRY2 Sprouty homolog 2 TRAF3IP1 TNF receptor-associated factor 3 interacting protein 1 TTC8 Tetratricopeptide repeat domain 8 UBC Ubiquitin C YWHAE Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon YWHAZ Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta ZDHHC17 Zinc finger, DHHC-type containing 17 doi:10.1371/journal.pone.0127438.t003 PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 11 / 17 Pathway Analysis of Nicotine Addiction-Related Genes Fig 3. Molecular network for nicotine addiction. This network is constructed based on the nicotine addiction-related pathways or pathway crosstalk identified in our study, NA-specific network and literature survey. NAGenes are highlighted in red while neurotransmitters are represented as pink ellipse. The enriched pathways are highlighted in yellow background. doi:10.1371/journal.pone.0127438.g003 Function enrichment analysis revealed the specific biological processes involved by NAGenes. Our GO enrichment analysis indicated that these genes participated in neurodevelopment-related process and ion channel or neurotransmitters activity. For example, terms such as acetylcholine receptor activity, dopamine receptor activity and glutamate receptor binding were significantly enriched in NAGenes, indicating the importance of these neurotransmitters in the development of nicotine addiction. Of note, we found the GO terms of cognition and learning or memory were also in the enriched list, consistent with previous findings of the roles of nicotine in the regulation of various physiological processes, including learning and memory [35–36]. Pathway analysis revealed that pathways related to neurodevelopment were enriched in NAGenes, which further verified the existence of close relationship between the pathology of nicotine addiction and the signaling pathways of nervous system. Four pathways that were related to monoamine neurotransmitters were found to be enriched in the NAGenes, consistent with their central roles in the development of nicotine addiction. Stimulation of nicotinic acetylcholine receptors (nAChR) releases a variety of neurotransmitters in the brain, e.g., dopamine, serotonin, glutamate and GABA. Dopamine is critical for the reinforcing effects or rewarding behaviors of nicotine [37–38], glutamate and GABA are respectively major excitatory and inhibitory neurotransmitter and both of them play important roles in the development PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 12 / 17 Pathway Analysis of Nicotine Addiction-Related Genes of nicotine addiction [39–40]. These neurotransmitters interact with the specific receptors, triggering a series of neuronal signaling pathways and then ultimately realize the regulation of various physiological processes. Of note, our analysis indicated that two pathways, synaptic long term potentiation and synaptic long term depression, were also enriched in genes associated with nicotine addiction. Repeated stimulation of nicotine to nervous system ultimately can modify the neural circuitry and thereby lead to addiction. As for many forms of experience-dependent synaptic plasticity, synaptic long term potentiation and synaptic long term depression play critical roles in the formation, maintenance, and appropriate functioning of neural circuits [41–42]. Therefore, these two pathways might be involved in the early stages of the development of nicotine addiction and facilitate the adaptation of body to changing environments. In addition, synaptic plasticity, as the molecular basis of learning and memory in the nervous system, has been extensively studied. Synaptic long term potentiation and synaptic long term depression have been reported to underline the cognitive and memory effects of the addictive potential of some drugs of abuse [43–44]. This further proved that nicotine could directly or indirectly modulate the physiological processes of learning and memory. Moreover, three signal pathways related to RXR were identified, i.e., LPS/IL-1 mediated inhibition of RXR function, PXR/RXR activation and LXR/RXR activation. Retinoic acid (RA), a class of natural or synthetic vitamin A analogs, exert profound effects on many biological processes, such as development, differentiation and maintenance of nervous system [45] and have been reported that it may serve as potential bridge between the genetic and environmental components of complex diseases [46–47], suggesting that environmental factors also play an important role in nicotine addiction. Interestingly, we found the circadian rhythm signaling was also in the enriched pathway list, supporting that there might be a link between nicotine addiction and abnormal or disrupted circadian rhythms [48]. As indicated by these results, the molecular mechanisms underlying nicotine addiction are quite complex and involve many genes, pathways and their interactions. Of significance, in pathway crosstalk analysis we identified three main modules. One module was mainly dominated by the pathways associated with the activity of the nervous system. Among these pathways, dopamine-DARPP32 feedback in cAMP signaling, glutamate receptor signaling, neuropathic pain signaling in dorsal horn neurons, and CREB signaling in neurons have been well studied to be involved in neuron or central nervous system (CNS) [49–51]. For instance, CREBs, widely been accepted as prototypical transcription factors, play a critical role in biological processes such as neuronal plasticity, learning and memory. Meanwhile, several lines of evidence have pointed that alterations of the activity of CREB by drugs of abuse have a profound effect on behavioral manifestations of drug reward and withdrawal [52]. Subsequently, we collected the genes contributing to the crosstalk, and the most frequently shared genes included glutamate receptor ionotropic N-methyl D-aspartate 2A (GRIN2A), GRIN2B, GRIN3A, calcium/calmodulin-dependent protein kinase IV (CAMK4), CREB1, and glutamate receptor metabotropic 7 (GRM7), suggesting these genes might be more potential targets in the development of nicotine addiction. In addition, the pathway pair of cAMP-mediated signaling and G-protein coupled receptor signaling, which were not included in the three modules, was also noteworthy. In the pathway analysis, we found these two pathways stood out at the top of the list by the statistically significant level (PBH = 2.00×10–14, ranked 1st and PBH = 3.16×10–13, ranked 3rd, respectively) (S2 Table). And the score of this pathway pair was 0.94, ranking the first. Furthermore, these two pathways have been deeply studied for their functions in the nervous system, such as regulating pivotal physiological processes. It was worth noting that, several edges, linking any one of these two pathways and other significant pathways were not displayed in Fig 1 just because they did not meet our criteria, such as the link between cAMPmediated signaling and dopamine receptor signaling. In this study, we just empirically chose PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 13 / 17 Pathway Analysis of Nicotine Addiction-Related Genes the pathway pairs whose crosstalk score fell within the top 20%, therefore some pathway pairs which we might be interested in were not shown in Fig 1. We further extracted the NA-specific network from the reference network. It was interesting to note that some additional genes not among the NAGenes but included in the human PPI network, were associated with nicotine addiction. For instance, CAV1, the structural protein of caveolae, can regulate the function of dopamine receptor D1 in glial cells and hippocampal neurons and may participate in G protein-coupled receptor signaling events [53–54]. CANX, a transmembrane protein in endoplasmic reticulum, can mediate intracellular Ca2+ concentration and directly interact with dopamine and G protein-coupled receptors to regulate their expression (Table 3) [55–56]. As indicated by the results, network-based analysis could not only provide meaningful information about the organization and environment of NAGenes, but also be promising to identify novel candidate genes. Although the quantity and quality of PPI data have been greatly improved, the human PPI network is still far from complete. In such scenario, some proteins may simply have more interaction information than others because they are better studied, instead of they are biologically more important. Also, due to the limitation of current technology, there may be some false positives in the PPI data [57]. Such potential biases associated with human PPI network may affect our interpretation of the results. Conclusions The neurobiological processes that underlie nicotine addiction are complex which relate to multiple factors, such as genetic and environmental factors. In this study, we applied a systems biology framework for a comprehensive functional analysis of nicotine addiction using candidate genes prioritized via a multi-source-based approach. Through integrating the information from GO, pathway and pathway crosstalk analysis, we found neurotransmitters or neurodevelopment-related signal pathway and immune system play key roles in the molecular mechanism of nicotine addiction. Further, we extracted nicotine addiction-specific subnetwork, in which some of the additional genes had been reported to be involved in nicotine addiction. To distill the global view of nicotine addiction process, we preliminarily constructed a molecular network for it. Our results provide important information for the further analysis and suggest that system level analysis is promising for understanding the pathophysiology of nicotine addiction. Supporting Information S1 Table. List of the 220 Nicotine addiction-related genes (NAGenes). (DOC) S2 Table. Pathways significantly enriched in NAGenes. (DOC) Acknowledgments This project was supported in part by grants from National Natural Science Foundation of China (Grant No. 31271411) and Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China. We are grateful to Prof. Ming D Li of University of Virginia for his help on this study. PLOS ONE | DOI:10.1371/journal.pone.0127438 May 12, 2015 14 / 17 Pathway Analysis of Nicotine Addiction-Related Genes Author Contributions Conceived and designed the experiments: ML FC JW. 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HORIZONS REVIEW doi:10.1111/j.1360-0443.2007.02070.x The genetics of nicotine addiction liability: ethical and social policy implications Wayne D. Hall1, Coral E. Gartner1 & Adrian Carter2 School of Population Health, The University of Queensland, Queensland, Australia1 and Queensland Brain Institute, School of Biomedical Sciences, University of Queensland, Queensland, Australia2 ABSTRACT Aim To assess the promise and risks of technological applications of genetic research on liability to develop nicotine dependence. Methods We reviewed (i) the evidence on the genetics of nicotine dependence; (ii) the technical feasibility of using genetic information to reduce smoking uptake and increase cessation; and (iii) policy and ethical issues raised by the uses of genetic information on addiction liability. Results (i) Despite evidence from twin studies that genes contribute to addiction susceptibility, research to date has not identified commonly occurring alleles that are strongly predictive of developing nicotine addiction. Nicotine addiction is likely to involve multiple alleles of small effect that interact with each other and with the environment. (ii) Population screening for susceptibility alleles is unlikely to be effective or cost-effective. Tailoring of smoking cessation treatments with genetic information is more plausible but results to date have been disappointing. Population health strategies such as increased taxation and reduced opportunities to smoke are more efficient in reducing cigarette smoking. Tobacco harm reduction policies applied to populations may also play a role in reducing tobacco-related harm. (iii) Future uses of genomic information on addiction risk will need to assess the risks of medicalising addiction (e.g. pessimism about capacity to quit) and community concerns about genetic privacy. Conclusions Nicotine genomics is a very new and underdeveloped field. On the evidence to date, its advocates would be wise to avoid extravagant claims about its preventive applications. Keywords Ethical implications, genetic screening, genetics, nicotine dependence. Correspondence to: Wayne D. Hall, School of Population Health, The University of Queensland, Herston, Qld 4006, Australia. E-mail: w.hall@sph.uq.edu.au Submitted 3 May 2007; initial review completed 26 June 2007; final version accepted 12 September 2007 INTRODUCTION Family and twin studies indicate that there is a substantial genetic contribution to the risk of developing nicotine dependence. Heritability estimates for nicotine and other types of drug dependence range from 39% to 80% [1–3], indicating that susceptibility to these conditions is influenced by individual genetic makeup as well as by environmental factors. There also appear to be both unique and overlapping genetic factors for initiation of drug use and progression to regular use and dependence [4]. Genes that affect drug metabolism and dopaminergic neurotransmission are plausible candidates for genes that underlie the heritability of nicotine (and other types of drug dependence) [5–7]. In 1999 Francis Collins, Director of the US Human Genome Institute, outlined an optimistic vision of the future contribution of ‘genomic medicine’: the use of genetic information to improve human health [8,9]. Collins foresaw genomic screening being used preventively to identify healthy individuals who carry susceptibility alleles for diseases, such as cancers and heart disease, and intervening with those at higher genetic risk to either change their behaviour (e.g. exercise, eat a healthier diet) or to use drugs (e.g. anti-hypertensives) that reduced their chance of developing these diseases. Collins imagined, for example, screening smokers for genetic susceptibility to lung cancer and counselling those at high risk to stop smoking. Similarly, optimistic projections have been expressed by some addiction genetics researchers [10]. In this paper we consider the promise and the potential harms emerging from research on genetic liability to develop nicotine dependence. We use nicotine © 2008 The Authors. Journal compilation © 2008 Society for the Study of Addiction Addiction, 103, 350–359 Genetics of nicotine addiction liability dependence as a case study for two reasons. First, tobacco smoking is the leading avoidable cause of premature death globally and in developed countries [11]. Secondly, the genetics of nicotine initiation and dependence is a very active research field and explicit attention has been paid to its clinical and public health applications (e.g. [12–14]). We begin by considering briefly what the available evidence suggests about the genetics of nicotine dependence. We then consider the ethical and policy implications that may arise from empirically plausible uses that may be made of genetic information on susceptibility to nicotine dependence and response to different pharmacotherapies for smoking cessation. CHALLENGES IN IDENTIFYING ADDICTION SUSCEPTIBILITY ALLELES Single, autosomal dominant genes of high penetrance have been identified for some human cancers (e.g. breast cancer-1 gene (BRCA1) and BRCA2 in breast cancer and familial adenomatous polyposis (FAP) in colorectal cancer) but these mutations account for very few cases of these diseases. It has proved more challenging to identify alleles that predict susceptibility to common human diseases and disorders [15,16]. Meta-analyses of association studies in common disease have shown that most positive findings have not been replicated and the minority of associations that have been replicated are very modestly predictive of increased disease risk [17,18]. Typically, people who have these susceptibility alleles are only 1.2–1.5 times more likely to develop these diseases than are people who do not [19]. These findings in general medicine have been replicated in addiction genetics [20]. Although adoption and twin studies provide good evidence of a genetic contribution to addiction liability [20,21], specific alleles and chromosomal regions are correlated only weakly with addiction liability [20]. The exception has been the allele that controls the enzyme, alcohol dehydrogenase: people who have one form of this allele are less likely to use alcohol and develop alcohol dependence [20]. In the case of smoking, genome-wide scans have identified associations between nicotine dependence and loci near genes of biological relevance, such as the mu1-opiod receptor (OPRM1), serotonin receptor 5A, alpha2nicotinic acetylcholine receptor (CHRNA2), alpha1Aadrenergic receptor (ADRA1A) and dopamine receptor (D1) genes [22]. Few of these associations, however, have been replicated between studies. This may reflect in part publication bias: journals are more likely to publish early positive associations that are not subsequently replicated [17,18,23]. 351 Studies of candidate genes have been similarly disappointing. Meta-analyses of the most replicated genetic association (Taq1 A1 allele of the ANKK1 gene) demonstrate that people with this allele are only 1.3–1.5 times more likely to be regular smokers [24,25]. Other associations (e.g. cytochome P450 2A6 polymorphisms, a variable-number tandem repeat polymorphism in the dopamine transporter gene, and the 5-HTTLPR polymorphism in the serotonin transporter gene) have not been confirmed [24–26]. Optimists argue that predictive alleles will be identified by improved study designs, attributing the failure to date to studies using small samples and poor designs [23]. More sceptical researchers argue that the lack of replication of genetic associations reflects the complexity of the genetics of human behaviour [27,28]. While some authors have suggested that addictive disorders may be influenced by a small number of alleles that vary between individuals [5], the more popular view is that these disorders are polygenic [10,20]. If addictive disorders are polygenic then we should expect only modest associations between alleles and addiction. This is because there will be multiple susceptibility alleles involved, each of which increases only marginally the risk of developing the disorder because their effects depend upon interactions with other genes and with environmental exposures [10,15,29]. Plausible estimates of the number of susceptibility alleles for major disorders range between the tens, at the most optimistic for autism [30], to the hundreds for common cancers [31,32]. In the remainder of this paper we assume that nicotine dependence is a polygenic disorder. THE PROSPECTS FOR GENOMIC MEDICINE IN NICOTINE DEPENDENCE Genomic prediction of addiction liability Sceptics argue that it will not be feasible to screen populations for genes that predict polygenic disorders such as addiction [33]. Single alleles are poor predictors of disease risk unless the life-time risk of the disease is 5% or more, and the genotype is either rare or it increases addiction risk 20 or more times [33–35]. It will be very costly to screen whole populations for alleles with either a low prevalence and high penetrance or a high prevalence and low penetrance because there will be only a very small number of people at high risk of developing these disorders [36]. Testing for multiple genetic variants can potentially improve the prediction of addiction risk [29,37]. Simulations suggest that the prediction of risk will be improved substantially if multiple susceptibility alleles are tested and the results are combined to produce a risk score © 2008 The Authors. Journal compilation © 2008 Society for the Study of Addiction Addiction, 103, 350–359 352 Wayne D. Hall et al. [34,38]. Nonetheless, even on the most optimistic variants of this scenario, large populations still need to be screened to identify the small number of people who will be at high risk because they carry multiple susceptibility alleles [39]. The efficiency of genomic screening could be improved if screening was confined to people at high risk of the disease on the basis of a history of early-onset disease among first-degree relatives, i.e. around 10% of the population [29,37,40]. Appropriate preventive interventions could then be provided to this group [40]. Triaging genetic screening on the basis of family history is, nonetheless, a substantial retreat from the wholepopulation screening envisaged by Collins. A critical policy question will be: will the addition of genetic information improve upon family history? Epidemiological modelling of breast cancer genetics suggests that it may [38], but evaluations are needed in the addictions field. presented with hypothetical genetic feedback have found less motivation to quit among those presented with a ‘low-risk’ result [51]. Similarly, randomized trials have found that smokers who were told that they were at low risk of tobacco-related diseases had lower smoking cessation rates than those not provided with any genetic risk information [44]. Genetic testing of children and adolescents to discourage smoking initiation has also been proposed. Such testing poses additional ethical concerns. The potential impact of ‘labelling’ a child or adolescent as being at ‘high risk’ of addiction is unknown, but could be damaging to self-image and future behaviour [50]. These issues require careful consideration, as some providers of adolescent medicine have already expressed an interest in genetic testing of their patients for nicotine addiction susceptibility [52], as have adolescents themselves [53,54]. Nicotine pharmacogenetics Effects of genetic information on quitting and initiation Screening is ethically justifiable only if there is an effective intervention to prevent the disorder in those who are identified as being at risk [15,40,41]. Because smoking is a necessary condition for nicotine dependence, everyone should be advised not to smoke regardless of their genotype [15,42,43]. Francis Collins assumed that people will be more likely to comply with advice not to smoke if they have been given personalized feedback on their genetic susceptibility to tobacco-related diseases. Randomized trials of personalized feedback about genetic susceptibility to tobacco-related disease have failed to demonstrate improvements in long-term smoking cessation rates [44–47]. Smokers who were advised they had a positive test result for genetic susceptibility to lung cancer (CYP2D6 status) were no more likely to attempt to quit, nor were they more likely to succeed in quitting, than smokers who were not advised of their genetic risk [45,48]. In one study, smokers who were told they had a greater genetic susceptibility to chronic obstructive pulmonary disease (COPD) were more likely to attempt to quit and to use cessation aids than those who tested negative [47]. Smokers who tested positive were more likely to be abstinent at 3 months than those who tested negative; however, the difference was not substantial (12% versus 4%). Another study, which provided nicotine replacement therapy (NRT) and telephone counselling for all participants, did not observe any difference in cessation rates between smokers advised of a positive or negative test result [46]. A further concern is that smokers who are told that they are at lower genetic risk of tobacco-related diseases may be less motivated to quit [49,50]. Studies of smokers Genetic information could be used to select treatment for persons who are nicotine dependent. For example, genetic information about nicotine metabolism or dopamine response to nicotine could be used to match smokers to the treatment that was most likely to produce abstinence [55]. This presupposes: (i) that there are alleles that predict different responses to smoking cessation treatments; and (ii) that matching in this way is more cost-effective than giving everyone the treatment that is the most effective regardless of genotype [39]. For nicotine pharmacogenetics to be cost-effective, the genotypes identified must predict reliably a differential response to treatment that is of sufficient size to justify the additional costs of genetic testing [56]. To date, pharmacogenetic studies of smoking cessation have examined polymorphisms in the DRD2 [57–62], OPRM1 [63,64], CYP2B6 [65], SLC6A3 [66], SLC6A4 [67,68], DBH [69], FREQ [70] and COMT genes [71]. Some trials report a differential response to treatment (typically NRT or bupropion compared to placebo), but the differences are small, they weaken over time, and most of these findings have not been replicated. Attempts to replicate two such findings, for example, produced a null effect in both studies for one [67,68] and contradictory findings for the other [63,64]. The positive results are also of questionable utility because of the low prevalence in the population of the polymorphisms tested [14]. For example, a polymorphism that Berrettini et al. [71] found predicted that a poor response to bupropion was found in only 11% of Caucasian smokers. Because of the poor results with individual alleles, many pharmacogenetic studies now examine combinations of multiple alleles in more than one polymorphism © 2008 The Authors. Journal compilation © 2008 Society for the Study of Addiction Addiction, 103, 350–359 Genetics of nicotine addiction liability (e.g. [57,70,71]). The studies to date have found a low prevalence of predictive allele combinations and also failed to replicate each others’ results. Evaluations of pharmacogenetic tests will also need to evaluate the psychological effects of giving smokers genetic information on their likelihood of quitting. Will smokers interpret genetic risk information as meaning that smoking is an immutable behaviour that can only be changed with great difficulty, if at all, by biological interventions [49,72,73]? Two studies of smokers’ understanding of the implications of information about genetic risk for cessation [74,75] have suggested that they may. In these studies, smokers who accepted that genetic factors contributed to cigarette smoking were less confident about their self-efficacy in quitting and were more likely to believe that a biological intervention was required to help them become abstinent. Research will also need to examine the effects of providing genetic information on future quit attempts. Smokers who fail to achieve abstinence despite having treatment tailored to their genotype may be discouraged from trying again. This would be an undesirable outcome, because most smokers need to make a number of failed quit attempts before achieving long-term abstinence [76–78]. It is also unclear whether pharmacogenetic strategies will reduce population smoking prevalence. Despite their efficacy in clinical trials, bupropion and NRT have not had a measurable population impact because of low uptake in the community [79]. It is difficult to believe that pharmacogenetic tests will increase uptake rates given the additional costs of genetic testing that are cited commonly as barriers to use of existing treatments [79,80]. Disadvantaged groups, who typically have the highest smoking prevalence, may find genetic tests particularly unappealing because of their cost and, possibly, fears of discrimination [81]. Finally, pharmacogenetic research on smoking may distract researchers from developing therapies that are more effective for all smokers. Nicotine vaccines and varenicline are two new therapies that have shown promising early results [82,83]. The cannabinoid antagonist, rimonabant, while possibly no more effective than bupropion and denied approval by the FDA as a smoking cessation aid, may be more attractive to smokers because it may prevent weight gain [83,84]. New, faster-acting and stronger-dose preparations of NRT may also be more effective at relieving withdrawal symptoms and more attractive to smokers [85]. 353 have halved cigarette smoking rates in Australia [86] and the United States [87] over the past three decades. These strategies contrast with the strategy of using genetic information to identify and intervene with those at ‘highest risk’ [41] entailed by genomic medicine [34]. Population-based strategies are likely to be more efficient than high-risk strategies when smoking prevalence is high [41]. In this situation it makes more sense to reduce cigarette smoking by increasing taxes on tobacco products, banning cigarette advertising and restricting opportunities to smoke than it does to spend resources on identifying those at higher genetic risk of becoming nicotine dependent, if they smoke tobacco [34,42]. A major challenge for advocates of using genomic medicine to reduce nicotine dependence will be in obtaining any health benefits from genetic screening without undermining effective public health policies [13,34]. Tobacco harm reduction A more controversial population health strategy is to encourage current smokers to adopt less harmful ways of using nicotine [88,89]. Snus, or oral snuff, appears to have substantially lower health risks compared to cigarettes. Because snus is a smokeless product, it does not produce any of the combustion products of smoked tobacco and it has low levels of tobacco-specific nitrosamines, the main carcinogens in tobacco. Research in Sweden, where men have used snus for several decades, have so far failed to detect any increase in the risks of oral cancers or cardiovascular disease among snus users [90–92]. The evidence of a substantially reduced risk with snus use compared to smoking is convincing, but the potentially detrimental effect on other tobacco control policies from its promotion also needs to be considered. Critics argue that the promotion of snus may reduce tobaccorelated mortality and morbidity in current smokers at the cost of increasing tobacco use in the population by recruiting new tobacco users and discouraging smokers from quitting. Epidemiological modelling indicates that for net harm to result from snus use, many more nonsmokers would need to take up snus for each smoker who switched to snus [93]. If snus use is confined to current smokers, switching from smoking tobacco to using snus would produce a net population health benefit, as it appears to have done in Sweden [94]. ETHICAL AND POLICY CONCERNS Medicalization of addictive behaviour Competing public health strategies Population-based tobacco control strategies such as taxing cigarettes and reducing the opportunities to smoke A major concern expressed by critics of genetic studies of human behaviour is that it will ‘medicalize’ human behaviour [95–98]; that is, it will lead to an overemphasis © 2008 The Authors. Journal compilation © 2008 Society for the Study of Addiction Addiction, 103, 350–359 354 Wayne D. Hall et al. on the biological, and particularly genetic, origins of behaviour, at the expense of social and psychological explanations, in ways that will adversely affect people who engage in stigmatized forms of behaviour such as smoking [13]. If addictions are seen as genetic disorders, critics argue that it could lead to a focus on medical interventions to the detriment of social measures such as higher taxes, prohibition of access to under 18s and so on [13,43,99]. Medicalization could also potentially affect the types of cessation treatments that are made available. Pharmacological treatments and genetic tests could be marketed to smokers for commercial rather than health gains, as some argue has happened with NicoTest (http://www.nicotest.com), a commercially available pharmacogenetic test marketed as a way of choosing either NRT or bupropion for smoking cessation [100,101]. This is particularly relevant for the treatment of nicotine addiction, where the consumer may be desperate to quit because of the social, health and financial burden of smoking. Critics argue that behaviour genetics may also change the way in which we think about nicotine dependence, and the ability of smokers to quit [13,102]. Such a view could lead to the further stigmatization of those who possess particular genetic alleles or mutations, or genetic markers associated with smoking [13]. From this viewpoint, behavioural genetics could lead to both institutionalized discrimination, particularly by courts, educators and employers, and health and life insurers, as well as intensifying more informal stigmatization [103–108]. These possibilities deserve to be investigated [13,50,55,109,110]. Third-party uses of genetic information Genetic information on addiction risk may potentially be used by third parties such as insurance companies, employers and educators and the courts. Given the nature of genetic transmission, the implications of this information affect not only the individual being tested, but also their close relatives. This raises ethical issues about who should be able to access this information. What measures should be taken to protect privacy? Under what circumstances should this information be shared and with whom [103,111,112]? Bioethicists’ concerns about the ethical and policy implications of genetic testing have been influenced strongly by experiences with genetic testing for Mendelian disorders, the paradigm case being Huntington’s disease [113]. Because the mutations that cause this serious neurological disorder are strongly predictive of disease risk, and there is no effective treatment, genetic testing creates serious ethical dilemmas for affected indi- viduals and family members [113]. Such testing also raises real concerns about the discriminatory use of genetic risk information by health and life insurers and employers [55,104,114]. However, Huntington’s disease is a poor model of the situation that we face with the addictive disorders. As argued above, as addictive disorders are most likely to be polygenic disorders, genetic testing will probably improve only modestly upon the prognostic value of family history. Any discussion of the ethical implications of the predictive genomics of addiction has to take account of the most likely ways in which genomics information will be used. If the pessimists are right, the ethical and policy issues identified by bioethicists will not arise because we will not identify predictively useful alleles for addiction. Even on the most optimistic scenario, the predictive genomics of addition is unlikely to lead to genetic screening of whole populations for the reasons outlined above. Rather, predictive genetic testing is more likely to be offered to the minority of people with a family history of early-onset addictive disorders, perhaps 10% of the population. Fear of genetic discrimination may, none the less, deter people with family histories of addictive disorders from having genetic tests that may benefit them. Similar fears may also deter individuals from participating in genetic research on addictive disorders, thereby impairing the acquisition of scientific knowledge about the genetics of these disorders. It remains to be seen whether community concerns about third-party use of genetic information prove to be a major impediment to addiction genomic research and future medical applications. We can, of course, eliminate the risks of third-party use of genetic information by banning all genetic tests, but this policy could prevent us from realizing any benefits that genetic testing may bring; it would also be an overly paternalistic policy. A better approach would be to look for safeguards to prevent individuals’ privacy and confidentiality being unfairly compromised. The challenge will be to develop policies that allow for the use of genetic information to reduce the incidence of disease and improve the health and welfare of individuals and society, while minimizing stigmatization and discrimination. Preventive uses of addiction genetics If we were able to predict genetic liability to nicotine dependence, we would need to decide if we should use potentially coercive means to prevent adolescents from smoking [42]. For example, vaccines that are being developed against nicotine, primarily for smoking cessation [115–118], could potentially be used in childhood to prevent ‘high-risk’ adolescents from smoking [116,119]. © 2008 The Authors. Journal compilation © 2008 Society for the Study of Addiction Addiction, 103, 350–359 Genetics of nicotine addiction liability Children are unable to consent to such interventions but parents may be able to consent on their behalf, as they do for other childhood vaccinations and health care interventions. In order to be ethical, the preventive use of a nicotine vaccine would need to demonstrate: (i) the long-term benefits of the vaccine [116,117,119]; and (ii) that genetic tests predict accurately the risk of nicotine addiction. Given the limited predictive power of genes studied to date, and doubts about the long-term efficacy of preventive vaccination [39], it is unlikely that preventive vaccination would be an effective or an ethical intervention [115]. Challenges for public education Popular understandings of the role of genetics, at least as expressed in the media, are often deterministic, suggesting that if you have ‘the gene for X’ you are very likely to develop that disorder, and conversely that you will be at low risk of doing so if you do not have the ‘gene’ for that disorder [120]. For example, popular media reporting of NicoTest describes it as a test for ‘the smoker’s gene’ or the ‘addiction gene’ [121,122]. These views probably reflect the media focus on Mendelian disorders such as Huntington’s disease, cystic fibrosis and Tay-Sachs disease, where modes of genetic transmission are easier to understand [120]. If these views of genetics are indeed held widely, the challenge for public education will be explaining the personal and public health implications of polygenic disorders in which individual alleles predict risk weakly, and interact with each other and with the person’s environment. If conducted well, this type of public education may allay anxieties about the third-party uses of genetic information. Public education will also need to avoid any unintended message that public health tobacco strategies can be replaced by high-risk genomic medicine strategies [43,99,123]. The surest way for many individuals in developed societies to reduce their disease risks remains to stop smoking, reduce caloric intake and increase exercise [31,36,41,43]. In order to avoid blaming individuals for their risk status we also need to modify physical and social environments in ways that facilitate desirable changes in risk behaviour. CONCLUSIONS Despite good evidence from twin studies that genes contribute to addiction susceptibility, substantial challenges remain before Francis Collin’s vision of genomic medicine can be realized in nicotine addiction. A major challenge has been the failure to date to identify commonly occurring susceptibility alleles that are strongly predic- 355 tive for nicotine addiction. The susceptibility alleles that have been identified to date predict addiction risk only weakly. Multiple alleles may predict individual risk more effectively but the costs of screening and counselling large numbers of individuals in order to identify the small number at high risk may be difficult to justify, especially in the absence of any effective preventive strategies. Population health strategies such as increased taxation and reduced opportunities to smoke are also more efficient in reducing cigarette smoking. Tobacco harm reduction policies applied to populations may also have an important role to play in reducing tobacco-related harm, although this remains controversial. Any future predictive use of genomic information on addiction risk will need to address ethical and policy issues such as community concerns about privacy and the third-party use of genetic information (e.g. by insurers or employers). Public education will be needed about the implications of the genetics of nicotine dependence and research is needed on how best to present genetic information to motivate desired behavioural change and avoid undermining successful public health strategies for reducing disease risk. References 1. Evans A., Van Baal G. C., McCarron P., DeLange M., Soerensen T. I., De Geus E. J. et al. 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Hindawi Publishing Corporation BioMed Research International Volume 2015, Article ID 313709, 9 pages http://dx.doi.org/10.1155/2015/313709 Research Article A Systematic Analysis of Candidate Genes Associated with Nicotine Addiction Meng Liu, Xia Li, Rui Fan, Xinhua Liu, and Ju Wang School of Biomedical Engineering, Tianjin Medical University, 22 Qixiangtai Road, Tianjin 300070, China Correspondence should be addressed to Ju Wang; wangju@tmu.edu.cn Received 30 September 2014; Revised 28 December 2014; Accepted 2 January 2015 Academic Editor: Yuedong Yang Copyright © 2015 Meng Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Nicotine, as the major psychoactive component of tobacco, has broad physiological effects within the central nervous system, but our understanding of the molecular mechanism underlying its neuronal effects remains incomplete. In this study, we performed a systematic analysis on a set of nicotine addiction-related genes to explore their characteristics at network levels. We found that NAGenes tended to have a more moderate degree and weaker clustering coefficient and to be less central in the network compared to alcohol addiction-related genes or cancer genes. Further, clustering of these genes resulted in six clusters with themes in synaptic transmission, signal transduction, metabolic process, and apoptosis, which provided an intuitional view on the major molecular functions of the genes. Moreover, functional enrichment analysis revealed that neurodevelopment, neurotransmission activity, and metabolism related biological processes were involved in nicotine addiction. In summary, by analyzing the overall characteristics of the nicotine addiction related genes, this study provided valuable information for understanding the molecular mechanisms underlying nicotine addiction. 1. Introduction Cigarette smoking is the most common form of tobacco use and is one of the main preventable causes of premature death and disability worldwide [1, 2]. Although there are some effective control policies and interventions on tobacco abuse, the negative impact of tobacco dependence on society is still staggering. The World Health Organization estimates that there are currently about 1.3 billion smokers worldwide, resulting in approximately 5 million annual tobacco attributable deaths [3, 4]. If the current trend continues, by 2020, smoking will become the largest single health problem worldwide, causing 10 million deaths annually, mostly in low- and middle-income countries [5]. Despite these grim statistics, cigarette smoking continues to impose substantial health and financial costs on society. According to the Centers for Disease Control and Prevention (CDC), in USA alone, the economic burden caused by smoking to society, including both the direct health care expenditures and the loss of productivity, can be as high as $193 billion a year [6]. In china, the prevalence of smoking remains high with 350 million smokers, and it is estimated that, by 2025, the annual number of deaths attributed to tobacco use will increase from 1.2 million to 2 million [7]. Although many cigarette smokers report a desire to quit smoking [8], few are successful [9, 10]. Thus, developing effective therapeutic approaches that can help smokers achieve and sustain abstinence from smoking, as well as methods that can prevent people from starting smoking, remains a huge challenge in public health. Nicotine, as the primary psychoactive component of tobacco smoke, produces diverse neurophysiological, motivational, and behavioural effects through interactions with nicotinic acetylcholine receptors (nAChRs) in the central nervous system (CNS). Twins, family and adoption studies have suggested that nicotine addiction is closely related to genetic and environmental factors, and genetic factors play an important role in the risk to the development of addiction [11, 12]. Numerous studies aiming to identify the genetic variants or candidate genes have found a large number of promising genes and chromosomal regions involved in the etiology of nicotine addiction [13]. In addition, various pathways and neurotransmitter systems have been found to be related to the psychoactive and addictive properties of nicotine, such as the mesocorticolimbic dopamine system [14–16], the serotonin 2 system, the glutamate system, and the GABA system [17–19]. Further, emerging evidence suggests that nicotine can also regulate the expression of genes/proteins involved in various functions such as ERK1/2, CREB, and c-FOS [20–22], as well as the expression state of multiple biochemical pathways, for example, mitogen-activated protein kinase (MAPK), phosphatidylinositol phosphatase signaling, growth factor signaling, and ubiquitin-proteasome pathways [23–25]. During the past decade, the application of high-throughput technologies to nicotine addiction study has greatly enhanced our ability to identify the nicotine addictionrelated molecular factors [26–28]. In spite of these progresses, our understanding of the molecular mechanism underlying nicotine addiction is still incomplete. Under such situation, how to integrate the available knowledge and data in heterogeneous datasets to obtain the relevant biological information has become an important task. Among the available approaches to explore the molecular mechanisms underlying various complex diseases, investigating the interactions between proteins encoded by the candidate genes in the human protein-protein interaction (PPI) network has been emerging as a powerful way [29–31]. Furthermore, genes/proteins with similar functions usually interact with each other more closely than those functionally unrelated genes [32], and cluster analysis on the molecular candidates within a PPI network can provide an intuitive view to understand its major biological functions. Taking together, a comprehensive analysis of the candidate genes within a systematic framework may be a powerful approach to analyze the molecular mechanisms underlying complex diseases like nicotine addiction. In this study, the global network topological properties of nicotine addiction-related genes (NAGenes) were explored in the context of human PPI network and were compared with other gene sets. Then, cluster analysis was utilized to detect the major functional modules related to nicotine addiction in the PPI network. Additionally, the significantly enriched functional clusters were identified for the NAGenes. This study provides useful insights for understanding the molecular mechanisms of nicotine addiction at the systems biological level. 2. Materials and Methods 2.1. Data Sources. Multiple gene sets related to nicotine abuse have been reported [27, 33, 34]. In an earlier study, we obtained 220 NAGenes prioritized via a multisourcebased gene approach [35], which represented a relatively comprehensive gene set for nicotine addiction. Briefly, genes identified to be related to nicotine addiction or involved in the physiological response to nicotine exposure or smoking behaviors were collected by integrating four categories of evidence, that is, association studies, linkage analysis, gene expression analysis, and literature search of single gene/protein-based studies. A category-specific score was assigned to each gene and a combined score was computed for all the collected genes based on an optimized weight matrix. Then, the genes were ranked according to the combined scores with a larger score value indicating a potentially higher BioMed Research International correlation between the gene and nicotine addiction. Based on the distribution of the combined score of all the genes collected, 220 genes on the top of the list were selected as the prioritized NAGenes. For the purpose of comparison, we collected two other gene sets, that is, an alcohol addiction-related gene set (alcohol genes) and a cancer-related gene set (cancer genes). Alcohol addiction can evoke the dysfunction of neuronal system and has been suggested to share some biological mechanisms with nicotine addiction. In this study, we selected the gene set with 316 alcohol genes collected by Li et al. [33]. Cancer has been well studied and is expected to have substantially different pathological characteristics from nicotine addiction. We downloaded the cancer genes (522 genes) from the Cancer Gene Census database (http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/). To investigate the network topological characteristics of a gene set, we first need to construct a relatively comprehensive and reliable PPI network. Here, we downloaded the human PPI data from the Protein Interaction Network Analysis (PINA) platform (May 21, 2014) [36], which collected and annotated data from six major protein interaction databases, that is, IntAct, BioGRID, MINT, DIP, HPRD, and MIPS/MPact. Also, we downloaded several related annotation files from NCBI (ftp://ftp.ncbi.nlm.nih.gov/gene/) (May 24, 2014), including the Entrez gene information database of human (Homo sapiens.gene info.gz), the data set specifying relationship between pairs of NCBI and UniProtKB protein accessions (gene refseq uniprotkb collab.dz), and file containing mappings of Entrez Gene records to Entrez RefSeq Nucleotide sequence records (gene2refseq.gz). For the proteins included in the human PPI database, only those that could be mapped to NCBI Entrez Gene were included in our subsequent analysis. After excluding the redundant and self-interacting pairs, we constructed a human PPI network containing 15,093 nodes and 161,419 edges. 2.2. Global Network Topological Properties. In network analysis, different metrics can be used to describe the network characteristics. We applied four measures to assess the network topological characteristic of NAGenes, that is, degree and degree distribution, clustering coefficient, closeness, and eccentricity. For a network, degree of a node (gene/protein in our case) is the number of direct connections that it has to other nodes in the network, and highly linked nodes are usually thought to make important contribution to the global structure or the behavior organization of a biological network [37, 38]. Degree distribution is the probability distribution of the degrees of all nodes over the whole network. Clustering coefficient quantifies the probability that two nodes linking to the same node connect with each other and describes the overall organization of the relationships within a network [39, 40]. The closeness of a node is the reciprocal of its average distance to each node in the network, while the eccentricity of a node is the distance to its farthest reachable node [41]. 2.3. Cluster Analysis within the Global Network. To intuitively observe the biological functions involved in the large nicotine BioMed Research International addiction-related network, we applied the Molecular Complex Detection (MCODE) (Version 1.4) (http://baderlab.org/ Software/MCODE) implemented in Cytoscape platform (http://www.cytoscape.net/) to identify the molecule modules or clusters. MCODE is a local clustering algorithm that can effectively detect densely connected regions of a molecular interaction network. In our analysis, the global network that we constructed was uploaded into the Cytoscape [42] and then MCODE was run to detect gene clusters in the network using the haircut option which identified nodes having limited connectivity at the cluster periphery. For the other parameters, the default settings were adopted. 2.4. Functional Annotation Cluster. To assess the candidate genes in the context of function similarity, we performed enrichment analysis on their Gene Ontology (GO) annotations using the Database for Annotation and Integrated Discovery (DAVID) (Version 6.7) [43]. The genes with their gene ID or GenBank Accession Numbers were submitted to DAVID under the functional annotation option specifying Homo sapiens as the species. In the DAVID functional annotation clustering, the significantly overrepresented GO terms, that is, biological process (BP), molecular function (MF), and cellular component (CC), were retrieved by using the options GOTERM BP ALL, GOTERM MF ALL and GOTERM CC ALL. The default parameters and corresponding false discovery rate (FDR) by the Benjamini and Hochberg approach [44] were used to determine the enrichment score. 3. Results and Discussions 3.1. Global Network Topological Properties of NAGenes. PPI network analysis provides an effective approach to investigate the biological themes related to a list of genes at the molecular level. In particular, the topological properties of nodes (genes) and edges (connections between genes) can help to understand the underlying biological themes associated with the network [45]. To depict the network topological properties of NAGenes, we first constructed a human PPI network by integrating information from multiple databases, to which NAGenes were then mapped. Subsequently, the characteristics of the NAGenes in the network were assessed by four network topological measurements, that is, degree, clustering coefficient, closeness, and eccentricity. As a comparison, we also calculated the topological measures of the networks corresponding to alcohol genes and cancer genes. Of the 220 NAGenes, 208 could be mapped onto the human PPI network and the average degree of these genes was 39.1, which measured the average number of direct connections between each member of NAGenes and other genes included in the PPI network, while, for the alcohol genes, 304 of the 316 genes could be mapped onto the human PPI network, with an average degree of 52.9 and for the cancer genes, 488 of the 519 genes could be mapped onto the human PPI network, with an average degree of 59.8. In order to have a more intuitive understanding of the degree characteristics, we plotted the degree distributions of the three gene sets (Figure 1). As shown, for all the three gene sets, the degrees scattered 3 in a rather large range from 1 to more than 500. But the degree distributions were right-skewed, that is, the majority of the genes had only a few connections with other genes and a small number of genes had a large number of connections. Compared with the NAGenes, the average degree of the alcohol genes appeared to be closer to the cancer genes, but statistical test indicated that significant difference existed between the degrees of all the three gene sets (alcohol genes versus cancer genes, 𝑃 = 1.93 × 10−7 ; alcohol genes versus NAGenes, 𝑃 = 0.0031, Wilcoxon rank sum test). The degree distribution of NAGenes was also significantly different from that of both alcohol genes and cancer genes (NAGenes versus alcohol genes, 𝑃 = 0.0031; NAGenes versus cancer genes, 𝑃 = 1.93 × 10−13 , Wilcoxon rank sum test). But compared with the cancer genes, the NAGenes and the alcohol genes tended to have lower or moderate connections, for example, 67% and 54% of the NAGenes, and the alcohol genes fell in the degree interval of 1–20, respectively, while only 37% of the cancer genes were included in this range (Figure 2). A close check of the degree of NAGenes showed that genes with more specific functions, such as those related to synaptic transmission (e.g., neuronal acetylcholine receptor subunit alpha-1 [CHRNA1], CHRNA2, CHRNB1, and CHRNB2), drug metabolism (e.g., N-acetyltransferase 2 [NAT2], tryptophan hydroxylase 2 [TPH2], and cytochrome P450 2A6 [CYP2A6]), and transport (e.g., solute carrier family 9 member 9 [SLC9A9], solute carrier organic anion transporter family member 3A1 [SLCO3A1], and solute carrier family 1 member 2 [SLC1A2]), tended to have smaller degrees, while the genes expressed in a large range of cell types/tissues or involved in broad physiological processes were more likely to larger degrees, for example, nuclear receptor subfamily 3 group C member 1 (NR3C1), beta-2 adrenergic receptor (ADRB2), estrogen receptor alpha (ESR1), and tumor protein p53 (TP53). Thus, although all the members of NAGenes may be nicotine addiction-related, those with smaller degrees are more likely to be involved in biological processes or neuronal activities invoked by nicotine. Clustering coefficient measures the interconnectivity of neighboring genes in a network. Generally, a gene with larger clustering coefficient has a higher density of network connection. The average clustering coefficients of NAGenes, alcohol genes, and cancer genes were 0.02, 0.03, and 0.06, respectively. To better describe the characteristics of the clustering coefficient, we summarized them using histogram with an interval of 0.1 (Figure 3(a)). Among the three gene sets, the proportion of genes with clustering coefficient of 0 was much higher for NAGenes (67.8%) than the alcohol genes (44.1%) and cancer genes (16.0%). Within the intervals 0-0.1, the proportion of NAGenes included was 96.2%, which is higher than the other two gene sets (alcohol: 95.7%; cancer: 81.6%). Interestingly, when the clustering coefficient was greater than 0.4, the proportion of NAGenes was 0. Thus, NAGenes were likely to be less connected with each other than the alcohol genes or the cancer genes. In addition, we also analyzed the distribution of closeness and eccentricity of the NAGenes in the human PPI network. Usually, a gene with higher closeness is more likely to be a central gene in the network, and a gene with larger eccentricity Proportion of proteins 4 BioMed Research International 0.08 0.06 0.04 0.02 0 1 2 5 10 20 (deg) 50 100 200 500 NAGenes Cancer Alcohol Figure 1: Degree distribution and the average degree of NAGenes, alcohol genes, and cancer genes. 𝑦-axis represents the proportion of proteins having a specific degree. Vertical line represents the average value of the degrees. Black line denotes NAGenes, gray line denotes alcohol genes, and dotted line denotes cancer genes. 0.35 Proportion of proteins 0.3 0.25 0.2 0.15 0.1 NAGenes Alcohol genes Cancer genes 100< 51–70 71–100 (deg) 41–50 31–40 21–30 16–20 11–15 6–10 0 1–5 0.05 Figure 2: Degree distribution of NAGenes, alcohol genes, and cancer genes. 𝑦-axis represents the proportion of proteins having a specific degree. is closer to the fringe of the network [46, 47]. Figure 3(b) showed that NAGenes had a smaller closeness compared with the alcohol genes or the cancer genes, but the eccentricity distribution of NAGenes showed an opposite trend, following a more right-skewed distribution (Figure 3(c)). These results revealed that the NAGenes may be less central in the PPI network compared with the other two gene sets. 3.2. Cluster Analysis within the Global Network of NAGenes. Besides characterizing the interaction networks with respect to their topological features, the biological network can also be clustered or partitioned into modules, which provides an insight into the overall organization of the relationship within the PPI network [32]. Clustering algorithms have previously been shown to be useful in predicting the molecular modules that participate in similar biological process. By using the clustering algorithm to the network associated with nicotine addiction, we identified 6 clusters including 81 nodes (genes in our case) and 126 edges. Out of these nodes, 30 (37.04%) were included in the 208 genes mapping into the human PPI network. These clusters were ranked according to their density and the number of proteins (genes) included (Table 1 and Figure 4). As shown, the clusters were involved in multiple biological functional categories. For example, the majority of the genes in cluster I were associated with apoptotic and macromolecular metabolic process. Three genes associated with nicotine addiction, estrogen receptor 1 (ESR1), arrestin beta 1 (ARRB1), and ARRB2, were located close to the center of this cluster (Figure 4). ESR1, as the specific nuclear receptor of sex hormones, widely distributes in the dopaminergic midbrain neurons and is able to modulate the neurotransmitter systems of the brain reward circuitry [48]. Moreover, ESR1 also plays an important role in apoptotic process. ARRB1 and ARRB2 are ubiquitous scaffolding proteins. They can regulate multiple intracellular signaling proteins involved in cell proliferation and differentiation and have important roles in mitogenic and antiapoptotic function of nicotine [49, 50]. The overall functional theme of Clusters II, III, and VI was synaptic transmission. Dopamine receptor D2 (DRD2) and DRD4 are both dopamine receptors that are critical for the reinforcing effects or rewarding behaviors of nicotine [51, 52]. GABA B receptor 1 (GABBR1) and GABBR2, the two receptors of the major inhibitory neurotransmitter GABA, play important roles in the development of nicotine addiction [53]. Each cluster also contained genes not included in NAGenes (Figure 4). A close inspection showed that some of these additional genes were potentially related to nicotine addiction. For example, N-ethylmaleimide-sensitive factor (NSF) [54], ubiquitin b (UBB) [55], small ubiquitin-related modifier 2 (SUMO2) [55], cyclin-dependent kinase 5 (CDK5) [56], and phospholipase C gamma 1 (PLCG1) [57] have been reported to be associated with nicotine addiction or regulated by nicotine exposure. Thus, further exploration on the genes included in these clusters may help us to identify more nicotine addiction-related candidate genes. 3.3. Functional Annotation Analysis. To obtain a more systematic view of the biological function of the genes involved in nicotine addiction, we performed functional enrichment analysis on NAGenes. In earlier study, a preliminary functional annotation analysis showed that genes related to biological processes like neurodevelopment and signal transduction were overrepresented in NAGenes [35]. Here, we provided a more comprehensive exploration on the function features of these genes. For the 220 genes, 73 annotation clusters were identified in the candidate genes (enrichment score > 1.3). Of these annotation clusters, eight clusters with enrichment scores higher than 10 were displayed with the representative GO terms (Figure 5 and Table S1). From a wide view of the annotation clusters, functional annotations associated with neurodevelopment and neurotransmitters were significantly overrepresented in the NAGenes. In the top two annotation clusters (Clusters 1 and 2), eight terms, including transmission of nerve impulse (FDR = 1.85 × 10−28 ), synaptic 5 0.8 0.7 0.7 0.6 0.2 0.16–0.20 0.9-1.0 0.8-0.9 0.7-0.8 0.6-0.7 0.5-0.6 0.4-0.5 0.3-0.4 0 0.2-0.3 0 0.1-0.2 0.1 0.0-0.1 0.1 Clustering coefficient 0.40< 0.2 0.3 0.36–0.40 0.3 0.4 0.31–0.35 0.4 0.5 0.26–0.30 0.5 0.21–0.25 Proportion of proteins 0.6 0 Proportion of proteins BioMed Research International Closeness NAGenes Alcohol genes Cancer genes NAGenes Alcohol genes Cancer genes (a) (b) 0.9 Proportion of proteins 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 <5 5 6 7 7< Eccentricity NAGenes Alcohol genes Cancer genes (c) Figure 3: Topological measures distribution of NAGenes, alcohol genes, and cancer genes. 𝑦-axis represents the proportion of proteins having a specific measurement. (a) Clustering coefficient. (b) Closeness. (c) Eccentricity. transmission (FDR = 3.32 × 10−28 ), system process (FDR = 3.84 × 10−19 ), and neurological system process (FDR = 2.76 × 10−18 ), were directly related to neurodevelopment, consistent with the previous reports that there is a relationship between the pathology of nicotine addiction and the development of neuron system. Moreover, the majority of terms in Cluster 3 were associated with neurotransmitter receptor or channel activity, for example, extracellular ligand-gated ion channel activity (FDR = 2.28 × 10−19 ), neurotransmitter receptor activity (FDR = 5.80 × 10−18 ), and acetylcholine receptor activity (FDR = 6.06 × 10−18 ) (Table S1). These results indicated the importance of neurotransmitters and related molecules in the development of nicotine addiction. Importantly, we found that calcium ion transport (FDR = 0.02) was also overrepresented in the candidate genes, consistent with the reports that the ligand-gated cation channels play an important role in regulating various neuronal activities by mediating intracellular Ca2+ concentration, including neurotransmitter release [58, 59]. In Cluster 7, the overall functional theme was various neurotransmitter or substances metabolic process, such as dopamine metabolic process (FDR = 1.76 × 10−12 ), catecholamine metabolic process (FDR = 6.58 × 10−11 ), diol metabolic process (FDR = 6.58 × 10−11 ), and cellular amino acid derivative metabolic process (FDR = 5.21 × 10−6 ). These metabolic processes had important roles not only in the development of nicotine addiction, but also in the harm to human health. In addition, Cluster 8 was concentrated on learning or memory, which reflected a kind of pathological forms of nicotine addiction. In summary, the molecular mechanisms underlying nicotine addiction are 6 BioMed Research International Cluster I Cluster II Cluster III Apoptotic/macromolecular metabolic process Synaptic transmission/intracellular messenger signaling cascade Behavioral response to nicotine AR DHNC4 UBE3A NR3C1 NEDD4 RPS3 HDAC1 ARRB2 HIF1A ESR1 PDE4D UBC NBN ERCC6 GCDH ITCH NOS3 UBB PSEN1 CUL4A UBKLNI GNA11 TTN HTR2A CHNB4 MAP1A ACTN1 Cluster IV Cluster V Cluster VI Response to abiotic stimulus/ cellular metabolic process Cellular response to DNA damage stimulus/DNA metabolic process Synaptic transmission/ cell-cell signaling PRKG1 GAPDH KPNB1 HECW2 H2AFX RPA3 BRCA2 MGMT MRE11A CHRNA3 DRD2 ADRB2 GNAS CHEK2 XRCC6 KCNJ3 KCNJ9 ARR3 ARRB1 SUMO1 MSH6 BRCA1 DRD4 TUBB2A ERCC3 CDK5 ATR TCEAL1 PRMT1 PDCD5 GRIN2A CDK2 RPA2 NCL TP53 USP11 CCNH SPTAN1 GTF2H1 GRIN2B MYC PLCG1 XRCC1 RPA1 SUMO2 XPC MLH1 LMNA NSF HSPA4 PCNA GABBR1 GABBR2 ATM Genes included in NAGenes Genes included in the network but not in NAGenes Figure 4: Gene clusters identified by MCODE. NAGenes are shown as triangular nodes and non-NAGenes are ellipse nodes. The functional descriptors of each cluster are based on Gene Ontology term. Table 1: Gene clusters identified in the nicotine addiction-related network. Scorea Nodes Edges Apoptotic/macromolecular metabolic process 4.08 25 49 II Synaptic transmission/intracellular and second messenger signaling cascade 2.67 13 16 III Behavioral response to nicotine 3.00 3 3 IV Response to abiotic stimulus/cellular metabolic process 3.05 22 32 V Cellular response to DNA damage stimulus/DNA metabolic process 3.29 15 23 VI Synaptic transmission/cell-cell signaling 3.00 3 3 Cluster Cluster function I a Gene symbol ARRB2, ARRB1, CUL4A, HDAC1, RPS3, ERCC6, GNAS, UBE3A, NBN, CHEK2, BRCA1, ESR1, ARR3, AR, HDAC2, NEDD4, UBB, MSH6, NR3C1, UBC, PDE4D, SUMO1, HIF1A, TUBB2A, ITCH KCNJ9, DRD2, ADRB2, DRD4, NOS3, MAP1A, GCDH, TTN, HTR2A, PSEN1, ACTN1, KCNJ3, GNA11 UBQLN1, CHRNB4, CHRNA3 BRCA2, GAPDH, H2AFX, SPTAN1, PRMT1, PRKG1, MGMT, NCL, HECW2, USP11, ATR, LMNA, GRIN2A, CDK5, TP53, GRIN2B, KPNB1, XRCC6, MRE11A, TCEAL1, PLCG1, PDCD5 RPA2, RPA3, CCNH, HSPA4, ERCC3, XRCC1, RPA1, GTF2H1, MLH1, PCNA, MYC, XPC, ATM, CDK2, SUMO2 NSF, GABBR1, GABBR2 Score is defined as the product of the cluster density and the number of vertices (proteins) in the cluster (DC × |𝑉|). BioMed Research International 7 Synaptic transmission/neurological system process Regulation of cell communication/regulation of neurological system process Channel activity/neurotransmitter receptor activity Transmembrane receptor activity/signal transducer activity Membrane fraction Response to alkaloid/response to nicotine Neurotransmitter or substances metabolic process Learning or memory 0 5 10 15 20 25 30 Enrichment score Figure 5: Enriched functional annotation in NAGenes (enrichment score > 10). Detailed information can be seen in supplementary Table S1 in Supplementary Material available online at http://dx.doi.org/10.1155/2015/313709. extremely complex in that they involve many genes and biological functions. Through its direct or indirect interactions with these genes, nicotine can regulate various physiological processes, such as learning and memory, synaptic function, response to stress, and addiction [60–63]. Our results also demonstrated that functional annotation cluster analysis can provide useful insights for intuitive understanding of addiction mechanisms. Furthermore, as neurodevelopment system and neuronal signaling cascades in the brain play important roles in the pathology of nicotine addiction, the genes and pathways related to these biological processes should be the major targets in nicotine addiction study. 4. Conclusions To achieve better understanding of the molecular mechanisms underlying nicotine addiction, it is necessary to adopt a system biology frame to analyze the candidate genes related to nicotine addiction. In this study, we explored the global network topological characteristics of nicotine addiction. The results revealed that the topological features of NAGenes were significantly different from alcohol genes and cancer genes. Specifically, NAGenes tended to have a more moderate degree and weaker clustering coefficient and they were likely to be in the network margin. Further, integrating the information from the functional modules identified in the global network and annotation cluster analysis, we found that nicotine addiction was involved in many biological functions, such as neurodevelopment, neurotransmitters activity, and various metabolic processes. Our preliminary results present a wealth of potential functional information underlying the mechanism of nicotine addiction and they are valuable for further investigation. Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper. Acknowledgments This project was supported in part by Grants from National Natural Science Foundation of China (Grant no. 31271411) and Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China. 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