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Open Access

Peer-reviewed

Research Article

Genes and (Common) Pathways Underlying Drug Addiction

• Chuan-Yun Li,
• Xizeng Mao,
• Liping Wei

x

• Published: January four, 2008
• https://doi.org/10.1371/periodical.pcbi.0040002

Figures

Abstract

Drug addiction is a serious worldwide trouble with strong genetic and environmental influences. Different technologies have revealed a multifariousness of genes and pathways underlying addiction; nevertheless, each individual technology can be biased and incomplete. Nosotros integrated 2,343 items of bear witness from peer-reviewed publications between 1976 and 2006 linking genes and chromosome regions to habit by single-factor strategies, microrray, proteomics, or genetic studies. We identified 1,500 human addiction-related genes and adult KARG (http://karg.cbi.pku.edu.cn), the showtime molecular database for habit-related genes with all-encompassing annotations and a friendly Web interface. We and so performed a meta-assay of 396 genes that were supported past two or more contained items of evidence to place 18 molecular pathways that were statistically significantly enriched, covering both upstream signaling events and downstream furnishings. 5 molecular pathways significantly enriched for all iv different types of addictive drugs were identified as common pathways which may underlie shared rewarding and addictive actions, including two new ones, GnRH signaling pathway and gap junction. We connected the common pathways into a hypothetical common molecular network for addiction. Nosotros observed that fast and slow positive feedback loops were interlinked through CAMKII, which may provide clues to explicate some of the irreversible features of addiction.

Author Summary

Drug addiction has become one of the most serious problems in the earth. It has been estimated that genetic factors contribute to 40%–60% of the vulnerability to drug addiction, and environmental factors provide the balance. What are the genes and pathways underlying addiction? Is at that place a common molecular network underlying addiction to different calumniating substances? Is there any network property that may explain the long-lived and often irreversible molecular and structural changes after addiction? These important questions were traditionally studied experimentally. The explosion of genomic and proteomic data in recent years both enabled and necessitated bioinformatic studies of addiction. Nosotros integrated data derived from multiple technology platforms and collected 2,343 items of evidence linking genes and chromosome regions to habit. We identified 18 statistically significantly enriched molecular pathways. In particular, v of them were common for four types of addictive drugs, which may underlie shared rewarding and addictive deportment, including two new ones, GnRH signaling pathway and gap junction. We continued the mutual pathways into a hypothetical mutual molecular network for addiction. We observed that fast and wearisome positive feedback loops were interlinked through CAMKII, which may provide clues to explain some of the irreversible features of addiction.

Citation: Li C-Y, Mao 10, Wei L (2008) Genes and (Common) Pathways Underlying Drug Addiction. PLoS Comput Biol four(1): e2. https://doi.org/x.1371/periodical.pcbi.0040002

Editor: Peter D. Karp, SRI Artificial Intelligence Center, U.s. of America

Received: July 18, 2007; Accustomed: November 19, 2007; Published: January 4, 2008

Copyright: © 2008 Li et al. This is an open-admission article distributed nether the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in whatsoever medium, provided the original writer and source are credited.

Funding: This work is supported by the Hi-Tech Research and Development Programme of Communist china (863 Program, 2006AA02Z334, 2006AA02A312), National Keystone Basic Research Program of China (2006CB910404), and China Ministry of Education 111 Project (B06001).

Competing interests: The authors have alleged that no competing interests be.

Abbreviations: KARG, Knowledgebase of Addiction-Related Genes; QTL, quantitative trait locus

Introduction

Drug addiction, defined as "the loss of control over drug apply, or the compulsive seeking and taking of drugs despite adverse consequences," has become ane of the nearly serious issues in the world [i]. It has been estimated that genetic factors contribute to twoscore%–sixty% of the vulnerability to drug habit, and ecology factors provide the remainder [two]. What are the genes and pathways underlying addiction? Is there a common molecular network underlying addiction to different abusive substances? Is at that place any network holding that may explicate the long-lived and often irreversible molecular and structural changes afterward addiction? These are all important questions that need to be answered in order to understand and command drug addiction.

Knowing the genes and vulnerable chromosome regions that are related to habit is an important first stride. Over the past iii decades, a number of technologies accept been used to generate such candidate genes or vulnerable chromosome regions. For example, in hypothesis-driven studies, genes in dissimilar brain regions were selectively expressed, downregulated, or knocked out in fauna models of addiction [three]. Contempo high-throughput expression-profiling technologies such every bit microarray and proteomics analyses identified candidate genes and proteins whose expression level changed significantly among different states in addiction [4,5]. Finally, genetic studies such as fauna Quantitative Trait Locus (QTL) studies, genetic linkage studies, and population clan studies identified chromosomal regions that may contribute to vulnerability to addiction [6–8]. However, as addiction involves a wide range of genes and complicated mechanisms, any individual engineering platform or study may be express or biased [three,ix–14]. There is a need to combine data across applied science platforms and studies that may complement i another [3,15,16]. The resultant cistron list, preferably in a database form with additional functional data, would be a valuable resources for the community. Systematic and statistical assay of the genes and the underlying pathways may provide a more complete picture of the molecular mechanism underlying drug habit.

Although different addictive drugs have disparate pharmacological effects, there are also similarities after acute and chronic exposure such as acute rewarding and negative emotional symptoms upon drug withdrawal [17]. Recently it was asked "Is there a mutual molecular pathway for addiction?" because elucidation of common molecular pathways underlying shared rewarding and addictive actions may assistance the development of effective treatments for a wide range of addictive disorders [17]. Several individual pathways have been proposed as common pathways [17]; however, they have not been studied systematically and statistically.

Central behavioral abnormalities associated with addiction are long-lived with stable and irreversible molecular and structural changes in the brain, implying a "molecular and structural switch" from controlled drug intake to compulsive drug abuse [xviii]. It was proposed that the progress of addiction may involve positive feedback loops that were known to make continuous processes discontinuous and reversible processes irreversible [19]. Once a common molecular network for addiction is constructed, we can look for the existence of positive feedback loops in the network and study the coupling between the loops. It may provide clues to explain the network behaviour and the habit process.

Results

Almost Comprehensive Collection and Database of Addiction-Related Genes to Appointment

Equally currently the information is scattered in literature, we retrieved and reviewed more than ane,000 peer-reviewed publications from betwixt 1976 and 2006 linking genes and chromosome regions to habit. In total, we collected 2,343 items of show linking 1,500 human genes to addiction. The detailed statistics is shown in Figure 1 and Table S1. A Knowledgebase of Addiction-Related Genes (KARG) is made publicly available at http://karg.cbi.pku.edu.cn. A description of the database statistics is given in Table S1, and the functional annotation fields are listed in Table S2. Two screenshots of the database user interface are shown in Figures S1 and S2. The interface supports browsing of the genes by chromosome or pathways, advanced text search by cistron ID, organism, type of addictive substance, technology platform, poly peptide domain, and/or PUBMED ID, and sequence search by Boom similarity [20]. All information, database schema, and MySQL commands are freely available for download at http://karg.cbi.pku.edu.cn/download.php.

Figure i. Pipeline for Collection of Data and Identification of (Common) Molecular Networks for Drug Addiction

Strategies used to report the genetic and environmental influences underlying addiction were divided into two types. Candidate gene-based strategies identified a list of genes related to addiction, including candidate genes identified in classical creature models, significantly differentially expressed genes identified in microarray or proteomics assays, and OMIM annotations. Strategies focused on genetic factors identified a listing of habit-vulnerable regions through animal QTL studies, genetic linkage studies, and population association studies. We integrated these datasets and obtained a list of human addiction-related genes. This dataset was then divided into four subsets based on addictive drugs, and analyzed using KOBAS, a statistical method to identify enriched molecular pathways. Molecular pathways enriched for all subsets were considered to exist mutual pathways for drug addiction, which were further continued to construct a common molecular network underlying different types of addiction.

https://doi.org/x.1371/periodical.pcbi.0040002.g001

Statistically Significantly Enriched Pathways in Addiction-Related Genes

We analyzed in item 396 genes that were supported past two or more contained items of testify. Nosotros institute that 18 pathways were statistically significantly enriched in addiction-related genes compared to the whole genome as background, including both metabolic and signalling pathways (Table 1). These pathways could exist clustered into two categories: (i) upstream events of drug addiction including crosstalk among MAPK signaling, insulin signalling, and calcium signalling, which share properties with long-term potentiation; and (ii) downstream effects including regulation of glycolysis metabolism, regulation of the actin cytoskeleton, and apoptosis, which share components with a list of neurodegenerative disorders such as Huntington disease and amyotrophic lateral sclerosis. Gene Ontology enrichment analysis confirmed the findings (run across details in Text S1 and Table S3).

Common Molecular Pathways for Drug Addiction

Because we collected metadata about each item of bear witness linking genes to addiction, in detail the nature of the addictive substance, we could enquire adjacent what are the pathways underlying habit to each type of substance, and what are the common pathways among them. We identified five pathways shared by all four addictive substances (Table 2). 3 of the pathways had been linked to addictive behaviors in previous studies and were statistically confirmed here. For example, "long-term potentiation" had been linked to addiction-induced adaptations in glutamatergic transmission and synaptic plasticity [21]. In item, a core component of this pathway, CAMKII, had been reported to regulate neurite extension and synapse germination through regulation of the actin cytoskeleton [22], providing possible explanations for morphological changes triggered by addictive drugs [17]. This pathway was likewise considered a key molecular circuit underling the memory system, highlighting the possible shared mechanisms betwixt drug habit and the learning and memory system [23]. "MAPK signaling pathway" is another case, equally previous studies had suggested its roles in regulating synaptic plasticity related to long-lasting changes in both retentivity function and addictive properties [24].

More than interestingly, 2 other common pathways identified hither had non been directly linked to addiction. "GnRH signaling pathway" was reported to activate gene expression and secretion of gonadotropins and regulate stress pathways in the hypothalamo-pituitary gonadal centrality and mammalian reproduction [25]. Information technology is reasonable to hypothesize that the pathway may also exist involved in the regulation and control of sure emotional behaviors in addiction such as stress-induced drug-seeking. Another common pathway identified in our study, "Gap junctions", tin can be regulated direct by 3 addiction-related kinases in the "long-term potentiation" pathway, PKA, PKC, and ERK. Since gap junctions are not simply an important type of connection for neuroglial cells simply likewise the most prevalent group of electrical synapses in the brain [26], this regulation may imply potential modification of cell communication in addiction. It would be interesting to investigate the roles of these pathways in future experimental studies.

A pathway is in itself a subjective concept, whereas the real systems are dynamic and include wide-ranging crosstalk amid functional modules. Connecting the common pathways with additional protein–protein interaction information, we constructed a hypothetical mutual molecular network for drug addiction, shown in Figure 2 (see details in Text S2 and Figure S3).

Effigy 2. Hypothetical Common Molecular Network for Drug Addiction

The network was synthetic manually based on the common pathways identified in our study and protein interaction information. Addiction-related genes were represented as white boxes while neurotransmitters and secondary massagers were highlighted in royal. The common pathways are highlighted in green boxes. Related functional modules such as "regulation of cytoskeleton", "regulation of cell cycle", "regulation of gap junction", and "gene expression and secretion of gonadotropins" were highlighted in carmine boxes. Several positive feedback loops were identified in this network. Fast positive feedback loops were highlighted in red lines and slow ones were highlighted in bluish lines.

https://doi.org/x.1371/journal.pcbi.0040002.g002

Positive Feedback Loops in the Network

From the common pathway network we identified four positive feedback loops, shown in Figure 2. We further observed that they interlinked with each other through CAMKII (Effigy 2). Two of these positive feedback loops involved signal transduction and would be considered "fast" loops, whereas the other two loops involved transcription and translation and would be considered "slow" loops. Information technology had been reported in a dozen systems, such equally budding yeast polarization and Xenopus oocyte maturation, that coupled fast and irksome positive feedback loops could create a switch that was inducible and resistant to noise and played central roles in discontinuous and irreversible biological process, features characteristic of addiction [27–29]. It was also known that activation of CAMKII played central roles in the development and maintenance of addiction states [30,31]. Disruption of dendritic CaMKII translation impaired the stabilization of synaptic plasticity and memory consolidation [32,33]. These evidences, taken together, suggested that the fast and dull positive feedback loops interlinked through CAMKII may be essential for the development and consolidation of addiction and may provide a systems-level explanation for some of the characteristics of addictive disorders.

Word

The addiction-related genes, (mutual) pathways, and networks were traditionally studied experimentally. The explosion of genomic and proteomic data in recent years both enabled and necessitated bioinformatic studies of addiction. Integration of data from multiple sources could remove biases of any unmarried technology platform, and statistical and network analysis of the integrated data could uncover high-level patterns non detectable in any individual study. For instance, our analysis revealed not only many pathways already implicated in addiction [34–38], just likewise new ones such as GnRH signaling pathway and gap junction, as well as the coupled positive feedback loops through CAMKII. They could serve equally interesting hypotheses for farther experimental testing.

The drove of addiction-related genes and pathways in KARG, the outset bioinformatic database for addiction, is the nearly comprehensive to appointment. However, as new technologies continue to be developed and used, more and more genes volition exist linked to addiction. In 2004, a paper asked why proteomics technology was not introduced to the field of drug habit [5]; since then xi studies take identified well-nigh 100 differentially expressed proteins in drug addiction. Tilling-array engineering, another new strategy for whole-genome identification of transcription factors binding sites, has been used to identify targets of CREB, an of import transcription cistron implicated in drug addiction [39]. In improver, as 100 K and 500 K SNP arrays have been introduced recently, whole genome association studies volition also identify more closely packed and unbiased hypothesis-gratuitous vulnerable positions [forty]. Nosotros volition go on to integrate new data and update the gene list and molecular pathways toward a better understanding of drug addiction.

Materials and Methods

Collection of addiction-related genes.

The information collection pipeline is summarized in Figure 1. The data and noesis linking genes and chromosome regions to addiction were extracted from reviewing more than 1,000 peer-reviewed publications from between 1976 and 2006. This list of publications, available on KARG Web site at http://karg.cbi.pku.edu.cn/pmid.php, included recent review papers on habit selected from results of PUBMED query '(addiction OR "drug abuse") AND review' also as research papers selected from PUBMED query '(addiction OR "drug corruption") AND (gene OR microarray OR proteomics OR QTL OR "population association" OR "genetic linkage")'. The information spanned multiple technology platforms including classical hypothesis-testing of single genes, identification of significantly differentially expressed genes in microarray experiments, identification of significantly differentially expressed proteins in proteomics assays, identification of habit-vulnerable chromosome regions in animal QTL studies, genetic linkage studies, population clan studies, and OMIM annotations [41]. From each publication we nerveless the genes, proteins, or chromosome regions linked to habit, every bit well as metadata such as species, nature of the addictive substance, studied encephalon regions, engineering platforms, and experimental parameters. For candidate genes or chromosomal regions identified in mouse or rat, nosotros mapped them to man genes through ortholog mapping by Homologene or syntenic mapping, respectively [41]. For chromosome regions identified in genetic studies, we identified candidate genes when at least one positive marker lay (i) within the gene or (ii) in 3′ or 5′ flanking sequences that were contained on a block of high restricted haplotype diverseness along with exon sequences from the aforementioned gene [8]. In total, we collected 2,343 items of evidence linking one,500 human genes to habit. Amid them 396 genes were supported past 2 or more items of prove (run into full listing in Table S4). This more reliable subset was used in subsequent analysis.

Identification of pathways statistically significantly enriched in addiction-related genes

We used the FASTA sequences of the 396 human habit-related genes every bit input to the KOBAS software, using all known genes in the human genome as groundwork [42,43]. KOBAS had been shown to pb to experimentally validated pathways [44]. It maps the input sequences to similar sequences in known pathways in the KEGG database [45] (as determined by Blast similarity search with evaluated cutting off e-values <1e-5, rank ≤x), and and so groups the input genes past pathways. Considering some pathways are naturally large, they may appear highly represented in a random choice of genes or gene products. To resolve this, KOBAS selects the pathways that are more likely to be biologically meaningful by calculating the statistical significance of each pathway in the input fix of genes or gene products against all pathways in the whole genome every bit background. For each pathway that occurs in the input genes, KOBAS counts the total number of genes in the input that are involved in the pathway, named yard, and the total number of genes in the whole genome that are involved in the same pathway, named M. If input has northward genes and the whole genome has N genes, the p-value of the pathway is calculated using a hypergeometric distribution:

KOBAS so performs FDR correction [42] to conform for multiple testing. Pathways with FDR-corrected Q-value < 0.05 were considered statistically significantly enriched in the input set of habit-related genes.

Identification of "common" molecular pathways and network.

For each of the iv addictive substances, cocaine, opiate, alcohol and nicotine, we input its listing of related genes to KOBAS to place the statistically significantly enriched pathways. Molecular pathways that were identified every bit significantly enriched for all iv addictive substances were selected as mutual pathways for drug addiction.

We synthetic a big molecular network of habit-related genes with the nodes being the gene products and the links extracted from the KEGG database, the Biomolecular Interaction Network Database (BIND), and Human being Interactome Map (HIMAP) [46]. The network was analyzed and visualized past Medusa [47]. We selected a more biologically meaningful sub-network representing simply the mutual pathways identified above.

Evolution of a database for habit-related genes.

We developed a database with MySQL relational schema. Cross-reference to key external databases were included to integrate functional information about the genes, such as gene annotation [41], Gene Ontology notation [48], interacting proteins [46], and functional domain annotations [49]. In add-on, a link was given to the original literature reference in the NCBI PubMed database [41]. We implemented a Web-based user interface of the database using PHP and queries of the database using PHP/SQL query script.

Supporting Information

Figure S1. Chromosome View of Addiction-Related Genes and Genetic Vulnerability Points for Habit

In window (A), + and − indicate habit-related genes on the plus or the minus chain, respectively, while '*' labels addiction-vulnerable points identified in population association studies. Clicking blueish + or − in the (A) window links to detailed descriptions of that gene (B), including basic data, evidence implicating it in addiction and diverse functional annotations. Clicking the red stars links to a detailed clarification of this genetic vulnerable point (C), including evidence for implication in addiction and functional annotations of this indicate (such as the nearest genes and possible effects on these genes).

https://doi.org/x.1371/journal.pcbi.0040002.sg001

(1.4 MB TIF)

Figure S2. Pathway View of Addiction-Related Genes

In window (A), statistically significant pathways are listed with p-values and Q-values. Clicking pathway names link to (B), pages showing interactive charts for that pathway, derived from KEGG. In the nautical chart, habit-related genes are highlighted in crimson. Detailed descriptions of each cistron tin can be retrieved when clicking genes in the chart.

https://doi.org/10.1371/journal.pcbi.0040002.sg002

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Figure S3. Poly peptide Interaction Networks of Habit-Related Genes

On the ground of KEGG data and protein interaction data deposited in BIND and HIMAP, we adult a hypothetical addiction-related molecular network using the whole set of human addiction-related genes (A). The network was analyzed and visualized by Medusa. Upstream events, including crosstalk among the MAPK pathway, insulin signaling, and calcium signaling, are highlighted in the xanthous square, while events implicated in cell development and communication are marked in carmine circles (including focal adhesion, adhesion junction, tight junction, gap junction, and axon guidance). Genes implicated in neurodegeneration are highlighted as diamonds. It is articulate that genes in upstream events and downstream events accept an interface, which are further manually separated and visualized (B). Genes represented in this interface are highlighted in regal. Genes represented in upstream events or downstream events, which have straight interaction with interface genes, are highlighted in ruby-red or blue, respectively. Especially, several genes having more than three interactions with interface genes are highlighted in green. This subnetwork may provide a screenshot to explain the relationship betwixt upstream kinase signaling pathways and downstream events such equally cytoskeletal modification.

https://doi.org/10.1371/journal.pcbi.0040002.sg003

(6.ii MB TIF)

Acknowledgments

We thank Drs. Gang Pei, Heping Cheng, Qingrong Liu, and Ka Wan Li for insightful suggestions, Shuqi Zhao, Anyuan Guo, Zhiyu Peng, and Lei Kong for assistance with Spider web server development, and Dr. Iain Bruce for manuscript revision.

Author Contributions

LW conceived and designed the experiments. CYL performed the experiments. CYL, XM, and LW analyzed the data. CYL and LW wrote the paper.

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