Abstract
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.
Introduction
Cigarette smoking is a worldwide epidemic, and one of the major
preventable causes of morbidity and mortality [[31]1–[32]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
smokers die from smoking-related diseases every year [[33]3–[34]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 [[35]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 [[36]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 [[37]7], facilitating various
neurotransmitter release such as dopamine, glutamate, serotonin and
γ-aminobutyric acid (GABA) [[38]8–[39]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 [[40]11–[41]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
[[42]14–[43]16]. Such a systems biology approach could be pivotal for
better understanding of mental disorder at the molecular level
[[44]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 [[45]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 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)
[[46]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
([47]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 [[48]20] and
Ingenuity Pathway Analysis system (IPA;
[49]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 P[BH]-value [[50]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 introduced two
measurements, i.e., the Jaccard Coefficient
[MATH: (JC)=|A∩BA∪B<
/mrow>| :MATH]
and the Overlap Coefficient
[MATH: (OC)=|A∩B|
min(|A|,|B|)<
/mo> :MATH]
, 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 P[BH]-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.
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 [[51]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 [[52]23]. We applied GenRev [[53]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 [[54]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 (n[L]) smaller than that of the nicotine
addiction-specific network and the number of random networks with
average clustering coefficient (n[C]) higher than the observed
clustering coefficient, we were able to estimate the significance of
non-randomness. Finally, we calculated the empirical p-value =
n[L]/1000 and n[C]/1000, respectively.
Results
GO enrichment analysis in nicotine addiction-related genes
The nicotine addiction-related genes (NAGenes) were involved in diverse
biological functions ([55]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 ([56]Table 1), including those associated with
neurodevelopment or synaptic transmission. In the biological process,
terms directly related to neurodevelopment, e.g., synaptic transmission
(P[BH] = 1.21×10^–36), transmission of nerve impulse (P[BH] =
1.69×10^–36) and neurological system process (P[BH] = 3.23×10^–32) were
identified ([57]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
(P[BH]= 1.36×10^–26), excitatory extracellular ligand-gated ion channel
activity (P[BH] = 2.62×10^–22) and acetylcholine receptor activity
(P[BH] = 5.12×10^–22). In the cellular component category, the
significantly enriched terms included synaptic membrane (P[BH] =
5.18×10^–27), neuron projection (P[BH] = 4.51×10^–24), axon (P[BH] =
3.55×10^–21), dendritic spine (P[BH] = 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 with the pathophysiological background of
nicotine addiction, which also indicated the candidate genes are
relatively reliable for the following up bioinformatics analysis.
Table 1. Gene Ontology terms enriched in nicotine addiction-related genes
(NAGenes).
GO Terms[58] ^a No. of genes[59] ^b P-value[60] ^c P[BH]-value[61] ^d
Biological process
GO:0007268 synaptic transmission 67 3.07×10^–39 1.21×10^–36
GO:0007267 cell-cell signaling 83 3.85×10^–39 1.21×10^–36
GO:0019226 transmission of nerve impulse 70 6.75×10^–39 1.69×10^–36
GO:0035637 multicellular organismal signaling 70 2.80×10^–38
5.86×10^–36
GO:0050877 neurological system process 82 2.06×10^–34 3.23×10^–32
GO:0043279 response to alkaloid 25 2.11×10^–25 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^–25 6.29×10^–23
GO:0097305 response to alcohol 23 2.64×10^–21 1.18×10^–19
GO:0031644 regulation of neurological system process 30 4.13×10^–21
1.79×10^–19
GO:0050890 cognition 27 9.03×10^–21 3.78×10^–19
GO:0007611 learning or memory 26 1.36×10^–20 5.51×10^–19
GO:0035094 response to nicotine 15 3.61×10^–20 1.42×10^–18
GO:0023061 signal release 35 4.76×10^–20 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^–20 3.15×10^–18
GO:0051952 regulation of amine transport 18 9.50×10^–20 3.22×10^–18
GO:0051969 regulation of transmission of nerve impulse 28 1.14×10^–19
3.76×10^–18
GO:0050804 regulation of synaptic transmission 27 1.20×10^–19
3.86×10^–18
GO:0044057 regulation of system process 39 1.28×10^–19 4.02×10^–18
Molecular function
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^–24 2.62×10^–22
GO:0005231 excitatory extracellular ligand-gated ion channel activity
19 1.49×10^–23 5.12×10^–22
GO:0015464 acetylcholine receptor activity 14 1.61×10^–23 5.12×10^–22
GO:0015276 ligand-gated ion channel activity 24 5.08×10^–20 1.15×10^–18
GO:0022839 ion gated channel activity 30 1.62×10^–17 2.58×10^–16
GO:0038023 signaling receptor activity 60 3.29×10^–17 4.76×10^–16
GO:0042166 acetylcholine binding 10 5.34×10^–17 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^–15
1.83×10^–14
GO:0004888 transmembrane signaling receptor activity 53 3.11×10^–14
2.06×10^–13
GO:0035240 dopamine binding 8 4.89×10^–14 3.11×10^–13
GO:0015075 ion transmembrane transporter activity 38 9.12×10^–12
5.58×10^–11
GO:0008324 cation transmembrane transporter activity 32 9.86×10^–12
5.81×10^–11
GO:0022891 substrate-specific transmembrane transporter activity 38
7.70×10^–11 4.22×10^–10
GO:0004952 dopamine receptor activity 5 4.67×10^–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^–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^–8 5.12×10^–8
GO:0016597 amino acid binding 12 1.69×10^–8 7.26×10^–8
GO:0035254 glutamate receptor binding 7 3.26×10^–8 1.36×10^–7
GO:0005506 iron ion binding 13 5.60×10^–7 2.23×10^–6
Cellular component
GO:0097060 synaptic membrane 35 7.51×10^–29 5.18×10^–27
GO:0045211 postsynaptic membrane 32 2.29×10^–27 1.05×10^–25
GO:0043005 neuron projection 50 1.96×10^–25 4.51×10^–24
GO:0005887 integral to plasma membrane 65 2.72 ×10^–24 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^–22 3.55×10^–21
GO:0043235 receptor complex 25 7.89×10^–20 9.07×10^–19
GO:0030054 cell junction 43 2.51×10^–17 2.48×10^–16
GO:0030425 dendrite 30 1.58×10^–16 1.36×10^–15
GO:0033267 axon part 20 3.63×10^–16 2.95×10^–15
GO:0044306 neuron projection terminus 16 1.11×10^–15 8.51×10^–15
GO:0043679 axon terminus 15 5.22×10^–15 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^–10 9.87×10^–10
GO:0044309 neuron spine 16 1.86×10^–10 9.87×10^–10
GO:0042734 presynaptic membrane 10 1.54×10^–9 7.59×10^–9
GO:0016023 cytoplasmic membrane-bounded vesicle 30 4.81×10^–7
2.07×10^–6
GO:0008328 ionotropic glutamate receptor complex 6 1.43×10^–6
5.80×10^–6
GO:0044327 dendritic spine head 10 2.19×10^–6 8.17×10^–6
GO:0014069 postsynaptic density 10 2.19×10^–6 8.17×10^–6
[62]Open in a new tab
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. P[BH]-value were adjusted by Benjamini & Hochberg (BH) method
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 (P[BH]≤0.01) ([63]S2 Table). The 20 most significantly
enrichment pathways are shown in [64]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 4^th), serotonin receptor signaling (ranked
11^th), glutamate receptor signaling (ranked 14^th) and GABA receptor
signaling(ranked 19^th), all of which play important roles in signaling
transduction. Moreover, two pathways, synaptic long term potentiation
(P[BH] = 1.07×10^–3) and synaptic long term depression (P[BH] =
8.13×10–3) were enriched in the NAGenes ([65]S2 Table). These two
pathways were critical in synaptic plasticity development and have been
reported to be involved in addiction [[66]26–[67]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 [[68]28–[69]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 [[70]2, [71]31]. Moreover, three pathways 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 [[72]32]. In summary, these
results suggest that neurodevelopment, immune and metabolic systems
play important roles in the pathogenesis of nicotine addiction.
Table 2. Pathways enriched in nicotine addiction-related genes (NAGenes) (top
20 pathways).
Canonical Pathways P-value[73] ^a P[BH]-value[74] ^b NAGenes included
in the pathway[75] ^c
cAMP-mediated signaling 6.31×10^–17 2.00×10^–14 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
Calcium Signaling 1.00×10^–15 2.00×10^–13 CAMK4, CHRNA1, CHRNA10,
CHRNA2, CHRNA3, CHRNA4, CHRNA5, CHRNA6, CHRNA7, CHRNB1, CHRNB2, CHRNB3,
CHRNB4, CHRND, CHRNG, CREB1, GRIK1, GRIN2A, GRIN2B, GRIN3A, ITPR2,
TRPC7
G-Protein Coupled Receptor Signaling 2.51×10^–15 3.16×10^–13 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
Dopamine Receptor Signaling 3.16×10^–14 2.51×10^–12 COMT, DRD1, DRD2,
DRD3, DRD4, DRD5, GNAS, MAOA, MAOB, NCS1, PPP1R1B, PPP2R2B, SLC18A2,
SLC6A3, TH
Xenobiotic Metabolism Signaling 2.00×10^–12 1.41×10^–10 ABCB1, AHR,
CAMK4, CYP1A1, CYP2B6, FMO1, GSTM1, GSTM3, GSTP1, GSTT1, IL6, MAOA,
MAOB, MAP3K4, MGMT, NOS2, NQO1, PPP2R2B, SOD3, SULT1A1, TNF, UGT1A9,
UGT2B10
Dopamine-DARPP32 Feedback in cAMP Signaling 2.69×10^–10 1.45×10^–8
CAMK4, CREB1, DRD1, DRD2, DRD3GRIN3A, DRD4, DRD5, GNAS, GRIN2A, GRIN2B,
ITPR2, KCNJ6, PPP1R1B, PPP2R2B, PRKG1
Aryl Hydrocarbon Receptor Signaling 2.88×10^–10 1.45×10^–8 AHR, CCND1,
CHEK2, CYP1A1, ESR1, GSTM1, GSTM3, GSTP1, GSTT1, IL6, MDM2, NQO1,
TGFB1, TNF, TP53
LPS/IL-1 Mediated Inhibition of RXR Function 4.37×10^–10 1.78×10^–8
ABCB1, ABCC4, APOE, CD14, CETP, CYP2A6, CYP2B6, FMO1, GSTM1, GSTM3,
GSTP1, GSTT1, MAOA, MAOB, MGMT, SOD3, SULT1A1, TNF
Gαi Signaling 4.57×10^–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^–6 3.47×10^–5 DNM1, GABARAP, GABBR1,
GABBR2, GABRA2, GABRA4, GABRE
PXR/RXR Activation 2.00×10^–6 3.47×10^–5 ABCB1, CYP2A6, CYP2B6, GSTM1,
IL6, NR3C1, TNF, UGT1A9
[76]Open in a new tab
^a P-value were calculated by Fisher’s exact test
^b P[BH]-value were adjusted by Benjamini & Hochberg (BH) method
^c 220 nicotine addiction-related genes included in the pathway
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 [[77]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
([78]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 system-related 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.
Fig 1. Pathway crosstalk among NAGenes-enriched pathways.
[79]Fig 1
[80]Open in a new tab
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 P[BH]-value of the
corresponding pathway. Darker color indicates lower P[BH]-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.
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 [81]Fig 2, the subnetwork contained 252 nodes and 591 edges.
Of the 220 NAGenes, 208 were included in the NA-specific 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 ([82]Table 3) [[83]33–[84]34].
Fig 2. Nicotine addiction-specific network.
[85]Fig 2
[86]Open in a new tab
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.
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
[87]Open in a new tab
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, 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 ([88]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, G-protein
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 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 (NF-κB) 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.
Fig 3. Molecular network for nicotine addiction.
[89]Fig 3
[90]Open in a new tab
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.
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.
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 [[91]35–[92]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 [[93]37–[94]38],
glutamate and GABA are respectively major excitatory and inhibitory
neurotransmitter and both of them play important roles in the
development of nicotine addiction [[95]39–[96]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 [[97]41–[98]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
[[99]43–[100]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
[[101]45] and have been reported that it may serve as potential bridge
between the genetic and environmental components of complex diseases
[[102]46–[103]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 [[104]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) [[105]49–[106]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 [[107]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 (P[BH] = 2.00×10^–14, ranked 1^st and
P[BH] = 3.16×10^–13, ranked 3^rd, respectively) ([108]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 [109]Fig 1 just because they did not meet our
criteria, such as the link between cAMP-mediated signaling and dopamine
receptor signaling. In this study, we just empirically chose 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
[110]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 [[111]53–[112]54]. CANX, a
transmembrane protein in endoplasmic reticulum, can mediate
intracellular Ca^2+ concentration and directly interact with dopamine
and G protein-coupled receptors to regulate their expression
([113]Table 3) [[114]55–[115]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 [[116]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)
[117]Click here for additional data file.^ (241.5KB, doc)
S2 Table. Pathways significantly enriched in NAGenes.
(DOC)
[118]Click here for additional data file.^ (144KB, doc)
Acknowledgments