- Methodology article
- Open Access
Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: alcohol, smoking and opioid addiction
- Cielito C. Reyes-Gibby1Email author,
- Christine Yuan†1,
- Jian Wang†2,
- Sai-Ching J. Yeung1 and
- Sanjay Shete2
- Received: 30 September 2014
- Accepted: 12 May 2015
- Published: 5 June 2015
Abstract
Background
Addictions to alcohol and tobacco, known risk factors for cancer, are complex heritable disorders. Addictive behaviors have a bidirectional relationship with pain. We hypothesize that the associations between alcohol, smoking, and opioid addiction observed in cancer patients have a genetic basis. Therefore, using bioinformatics tools, we explored the underlying genetic basis and identified new candidate genes and common biological pathways for smoking, alcohol, and opioid addiction.
Results
Literature search showed 56 genes associated with alcohol, smoking and opioid addiction. Using Core Analysis function in Ingenuity Pathway Analysis software, we found that ERK1/2 was strongly interconnected across all three addiction networks. Genes involved in immune signaling pathways were shown across all three networks. Connect function from IPA My Pathway toolbox showed that DRD2 is the gene common to both the list of genetic variations associated with all three addiction phenotypes and the components of the brain neuronal signaling network involved in substance addiction. The top canonical pathways associated with the 56 genes were: 1) calcium signaling, 2) GPCR signaling, 3) cAMP-mediated signaling, 4) GABA receptor signaling, and 5) G-alpha i signaling.
Conlusions
Cancer patients are often prescribed opioids for cancer pain thus increasing their risk for opioid abuse and addiction. Our findings provide candidate genes and biological pathways underlying addiction phenotypes, which may be future targets for treatment of addiction. Further study of the variations of the candidate genes could allow physicians to make more informed decisions when treating cancer pain with opioid analgesics.
Keywords
- Pain
- Opioid
- Smoking
- Alcohol
- Addiction
- Genes
- Inflammation
- Cancer
Background
Pain is a debilitating problem that cancer patients face, impairing their quality of life. Pain may be related to multiple factors, including radiotherapy, chemotherapy, surgery, and cancer progression. In order to mitigate therapy-related pain or cancer-related pain, physicians often prescribe opioid analgesics to cancer patients [1, 2]. The prescription of opioids for pain carries risk for opioid abuse and addiction. Because of the increased survival rate in cancer patients, their exposure to prescriptions of opioids are also prolonged, further increasing their risk for opioid abuse and addiction [3–5].
Studies showed that opioid abuse was associated with past histories of drug and alcohol abuse in patients treated for cancer-related pain with opioid analgesics [6, 7]. Several clinical trials also found that patients with a history of cigarette smoking and illicit drug abuse had a significantly higher risk for opioid addiction than those without the history [8–11]. Taken together, these studies suggest that past addictive behaviors to various substances may predict opioid addiction in cancer patients with opioid prescriptions for pain. However, very few studies have explored whether there exists a genetic basis and common pathways to the relationship between smoking, alcohol, and opioid addiction.
Bioinformatics uses methods and software tools to organize and analyze biological data [12]. Specifically, gene network analyses have been used frequently to identify genes associated with drug abuse and addiction [13–15]. However, there has been limited application of bioinformatics in understanding multiple addiction phenotypes, specifically, smoking, alcohol and opioid addiction. We hypothesize that the associations between alcohol, smoking, and opioid addiction observed in the clinical setting have a genetic basis.
The goal of the current study is to use bioinformatics tools to determine whether there exists a genetic basis and common pathways to the relationship between smoking, alcohol, and opioid addiction and identify new candidate target genes. Understanding the genetic bases of addiction will underscore the importance of integrating genetic studies into the process of drug administration, as well as allow clinicians to more accurately tailor a patient’s drugs and dosage based on medical history and genetic risk factors [16].
Methods
With the goal of identifying commonly shared genes for alcohol, smoking and opioid addiction we conducted a literature search as described below. Subsequently, using genes pooled from literature as a starting point, we performed gene network analyses: a) specific to each phenotype (Phenotype Specific Biological Network) and b) commonly shared between alcohol, smoking and opioid addiction (Common Biological Network). Finally, we used the Connect function from IPA My Pathway toolbox to connect the commonly shared genes of the three phenotypes to the signaling network involved in neuronal adaptation/plasticity in substance addiction [17, 18].
Literature search
Literature search flowchart. *Subset after using the following Exclusion criteria: Literature review/meta-analysis, non-human experiments, other mental disorders, recovery/withdrawal, unrelated to phenotype, genes that were not replicated in or confirmed by at least one independent study. **Some overlaps between phenotypes for articles and genes
Ingenuity pathway analysis
Ingenuity Pathway Analysis (IPA) was used to produce a comprehensive analysis of the genes commonly shared in these addiction pathways. IPA is a software used to connect molecules based on the Ingenuity Knowledge Base, its database of information on biomolecules and their relationships [19]. The Core Analysis function was used to compare genes pooled from literature for each phenotype of addiction with the genes and other molecules in IPA’s database and generates gene networks based on their interactions.
IPA network generation process
Phenotype specific biological network
Gene networks were created for each addiction phenotype. Only the networks with a p-score of 5 or higher were considered significant (i.e., p-value ≤ 10−5), a nominal significance used in previous studies [21]. The genes in each network were ranked based on number of edges, or interactions with other genes in the network.
Common biological network
Network generated by pooling all 56 focus genes for alcohol, nicotine and opioid addiction (p-score = 45)
Finally, in order to understand the biological context of the gene network (association of genetic variations with addiction to opioids, alcohol and nicotine), we used the Connect function from IPA My Pathway toolbox to connect the commonly shared genes of three phenotypes to the signaling network involved in neuronal adaptation/plasticity in substance addiction [17, 18]. The Connect function adds specific interactions between molecules. While performing this analysis, we limited the interactions from only human studies. All results were generated through the use of Ingenuity® iReport [19].
Results
Literature search
Summary of literature search - alcohol addiction
Author | Ethnicity | Sample size | Phenotype | Salient gene(s) | Salient SNP(s) | Statistical analysis |
---|---|---|---|---|---|---|
Batel et al. [47] | EA | 134 | Alcohol dependence | DRD1 | rs686 | P = 0.0008 |
Bierut et al. [77] | EA, AA | 5632 | Increased aversion from alcohol | ADH1B | rs1229984 | OR = 0.34 P = 6.6E-10 |
Cao et al. [78] | Han Chinese | 603 | Alcohol addiction | 5-HTR | rs6313 | OR = 0.71 P = 0.001 |
Chen et al. [79] | EA, AA | 3627 | Alcohol addiction | PKNOX2 | rs1426153 rs11220015 rs11602925 rs750338 rs12273605 rs10893365 rs10893366 rs12284594 | P = 5.75E-5, 6.86E-5, 4.24E-5, 4.26E-5, 3.0E-4, 1.72E-5, 1.37E-5, 1.97E-6 |
Deb et al. [25] | South Asian | 144 | Alcohol addiction | OPRM1 | rs1799971 | P = 0.02 |
Desrivieres et al. [80] | E | 145 | Drinking behavior | P13K | rs2302975 rs1043526 | P = 0.0019, 0.0379 |
Enoch et al. [81] | AA | 360 | Alcohol addiction | HTR3B | rs1176744 | P = 0.002 |
Ehringer et al. [35] | EA, Hisp, AA | 108 | Alcohol response | CHRNB2 | rs2072658 | |
Haller et al. [37] | EA, AA | 1315 | Alcohol addiction | CHRNB3 | rs149775276 | P = 2.6E-4 for EA, P = 0.006 for AA |
Hill et al. [82] | EA | 1000 | Alcohol dependence | KIAA0040 | rs2269650 rs2861158 rs1008459 rs2272785 rs10912899 rs3753555 | P = 0.033, 0.037, 0.014, 0.062, 0.035, 0.020 |
Kalsi et al. [83] | EA, AA | 847 | Alcohol addiction | DKK2 | rs427983 rs419558 rs399087 | P = 0.007 |
Kumar et al. [26] | Bengali/Hindu | 310 | Alcohol addiction | OPRM1 | rs16918875 rs702764 rs963549 | P = 0.0364 |
Kuo et al. [84] | E | 1238 | Initial sensitivity to alcohol | GAD | P = 0.002 | |
London et al. [85] | EA | Risk for alcohol addiction | ANKK1 | rs1800497 | P = 0.001 | |
Mignini et al. [51] | E | 560 | Dopaminergic system; alcohol dependence | DRD2/ANKK1 | rs1800497 | P = 0.023 |
Munoz et al. [86] | E | 1533 | Number of drinks per day | ADH1B, ADH6 | rs1229984 in ADH1B rs3857224 in ADH6 | rs1229984: OR = 0.19, P = 4.77E-10 for men, OR = 0.48, P = 0.0067 for women; rs38572: OR = 1.61, P = 1.01E-3 for women, NS for men |
Novo-Veleiro et al. [87] | E | 457 | Risk for alcohol addiction | miR-146a | rs2910164 | OR = 1.615 P = 0.023 |
Preuss et al. [88] | E (German & Polish) | 3091 | Alcoholism | ADH4 | rs1800759 rs1042364 | rs1800759: OR = 0.88 rs1042364: OR = 0.87 |
Ray et al. [89] | CA, As, Latino, NA, AA | 124 | Level of response to alcohol/drinking problems | GABRG1 | rs1497571 | P < 0.01 |
Samochowiec et al. [90] | EA | 275 | Alcohol dependence | MMP-9 | rs3918242 | P < 0.01 |
Schumann et al. [91] | E | 1544 | Alcohol dependence | NR2A, MGLUR | OR = 2.35, 1.69 | |
Treutlein et al. [92] | E | 296 | Potential alcohol dependence | CRHR1 | ||
Wang et al. [42] | EA, AA | 2309 | Alcohol dependence | CHRNA5 | rs680244 | P = 0.003 |
Wang et al. [93] | EA | 2010 | Alcohol dependence | C15orf53 | rs12903120 rs12912251 | rs12903120: P = 5.45E − 8 |
Xuei et al. [94] | EA | 1923 | Risk for alcohol addiction | GABRR1, GABRR2 | rs17504587 rs282129 rs13211104 rs9451191 rs2821211 rs6942204 | P = 0.04. 0.03, 0.03, 0.021, 0.025, 0.04 |
Yang et al. [95] | EA, AA | 3564 | Alcohol dependence | HTR3B | rs3891484 rs375898 | D’ > 7 |
Summary of literature search - smoking addiction
Author | Ethnicity | Sample size | Phenotype | Salient gene(s) | Salient SNP(s) | Statistical analysis |
---|---|---|---|---|---|---|
Agrawal et al. [96] | EA | 1929 | Nicotine dependence | GABRA4, GABRA2, GABRE | P = 0.030 | |
Agrawal et al. [97] | EA | 1921 | Nicotine dependence | GABRA4, GABRA2 | P = 0.002 | |
Anney et al. [98] | E | 815 | Cigarette dose | CHRM5 | rs7162140 | P = 0.01 |
Baker et al. [31] | EA | 886 | Nicotine dependence | CHRNA5-A3-B4 | P = 0.04 | |
Berrettini et al. [99] | EA | 1276 | Nicotine addiction | CYP2A6 | rs410514431 | P = 1.0E-12 |
Beuten et al. [100] | EA, AA | 2037 | Nicotine dependence | BDNF | rs6484320 rs988748 rs2030324 rs7934165 | P = 0.002 |
Beuten et al. [101] | EA, AA | Nicotine dependence | GABAB2 | rs2491397 rs2184026 rs3750344 rs1435252 rs378042 rs2779562 rs3750344 | P = 0.003 | |
Beuten et al. [102] | EA, AA | Nicotine dependence | COMT | rs933271 rs4680 rs174699 | P = 0.0005 | |
Broms et al. [32] | E | 1428 | Nicotine dependence | CHRNA5, CHRNA3, CHRNB4 | rs2036527 rs578776 rs11636753 rs11634351 rs1948 rs2036527 | P = 0.000009, 0.0001, 0.0059, 0.0069, 0.0071, 0.0003 |
Chen et al. [103] | 688 | Nicotine dependence | CNR1 | rs2023239 rs12720071 rs806368 | P < 0.001 | |
Chen et al. [79] | EA, AA | 3627 | Nicotine addiction | PKNOX2 | rs1426153 rs11220015 rs11602925 rs750338 rs12273605 rs10893365 rs10893366 rs12284594 | P = 0.0159, 0.0163, 0.0136, 0.0491, 0.0921, 0.0411, 0.0621, 0.0239 |
Conlon et al. [33] | EA | 1122 | Nicotine dependence | CHRNA5, CHRNA3, AGPHD1 | rs16969968 rs578776 rs8034191 | OR = 3.2, 2.8, 0.3 |
Culverhouse et al. [34] | AA, EA | 18500 | Nicotine dependence | CHRNB3, CHRNA7 | rs13273442 | P = 0.00058 for EA, 0.05 for AA |
Docampo et al. [104] | E | 752 | Lower risk for smoking behavior | NRXN3 | rs1424850 rs221497 rs221473 | rs1424850: OR = 0.55, P = 0.0002 |
rs221497: OR = 0.47, P = 0.0020 | ||||||
rs221473: OR = 0.54, P = 0.0009 | ||||||
Ehringer et al. [35] | EA, Hisp, AA | 108 | Nicotine response | CHRNB2 | rs2072658 | |
Ella et al. [105] | Japanese | 2521 | Nicotine addiction | DBH | rs5320 | P = 0.030 |
Gabrielsen et al. [36] | Norwegian | 155941 | Smoking status(cigarettes per day, duration, packs per year) | CHRNA5/A3/B4 | rs16969968 | P = 3.15E-25, 1.11E-6, 3.01E-23 (respectively for phenotypes) |
Huang et al. [106] | EA, AA | 3403 | Nicotine dependence | ANKK1 | rs2734849 | P = 0.0026 |
Lang et al. [107] | E | 320 | Smoking behavior | BDNF | P = 0.045 | |
Li et al. [38] | EA, AA | 2037 | Nicotine dependence | CHRNA4 | rs2273504 rs1044396 rs3787137 rs2236196 | |
Liu et al. [108] | EA, AA | 2091 | Smoking behavior | IL15 | rs4956302 | P = 8.8E-8 |
Ma et al. [109] | EA, AA | 2037 | Nicotine dependence | DDC | rs3735273 rs1451371 rs3757472 rs3735273 rs1451371 rs2060762 | P = 0.005, 0.006 |
Mobascher et al. [110] | German | 5500 | smoking behavior/nicotine addiction | CHRM2 | rs324650 | OR = 1.17 |
Nees et al. [39] | E, EA | 965 | Nicotine dependence | CHRNA5/A3/B4 | rs578776 | P < 0.05 |
Sherva et al. [40] | EA, AA | 435 | Smoking | CHRNA5 | rs16969968 | P = 0.0001 |
Rice et al. [29] | EA, AA | 3365 | Nicotine dependence | CHRNB3 | rs1451240 | P = 2.4E-8 |
Sarginson et al. [30] | EA, Asian, AA, Hispanic | 577 | Smoking behavior | CHRNA5/A3/B4 | rs16969968 rs1051730 | P < 0.0001 |
Sorice et al. [41] | E | 2272 | Smoking behavior | CHRNA5-A3-B4 | rs1051730 | P = 0.0151, 0.022, 0.22 for three populations |
Voisey et al. [52] | EA | 378 | Nicotine dependence | DRD2 | rs1800497 | P = 0.0003 |
Wang et al. [43] | EA, AA | 3622 | ND (smoking quantity and FTND) | CHRNA2, CHRNA6 | EA: rs3735757 rs2472553 | EA: P = 0.0068 for FTND, AA: P = 0.0043 for SQ and 0.00086 for FTND |
Wassenaar et al. [44] | E | 860 | Nicotine dependence | CYP2A6 and CHRNA5-A3-B4 | rs1051730 | P =0.036 |
Weiss et al. [45] | E | 2827 | Nicotine dependence | CHRNA5-A3-B4 | rs17486278 | P = 0.0005 |
Zeiger et al. [46] | EA, Hisp | 1056 | Response to smoking | CHRNA6, CHRNB3 | rs4950 rs13280604 rs2304297 | P = 0.043, 0.011, 0.053 |
Summary of literature search - opioid addiction
Author | Ethnicity | Sample size | Phenotype | Salient gene(s) | Salient SNP(s) | Statistical analysis |
---|---|---|---|---|---|---|
Beer et al. [22] | E | 284 | Opioid dependence | GAL, OPRD1 | rs948854 rs2236861 | P = 0.001 |
Bunten et al. [23] | 184 | Opioid addiction | OPRM1 | rs1799971 | P = 0.0046 | |
Compton et al. [24] | EA | 109 | Opioid addiction | OPRM1 | rs1799971 | |
Clarke et al. [111] | Han Chinese | 858 | Opioid dependence | PDYN | rs1997794 rs1022563 | P = 0.019, 0.006 |
Clarke et al. [48] | EA, AA | 992 | Opioid addiction | DRD2 | rs1076560 | OR = 1.29, P = 0.0038 |
Crist et al. [112] | EA, AA | 671 | Opioid addiction | WLS | rs3748705 (AA) rs983034 rs1036066 (EA) | AA: P = 0.025EA: P = 0.043, 0.045 |
de Cid et al. [113] | E | 91 | Opioid Addiction | BDNF | ||
Doehring et al. [49] | CA | 184 | Opioid addiction | DRD2 | rs1076560 rs1799978 rs6277 rs12364283 rs1799732 rs6468317 rs6275 rs1800498 rs1800497 | P = 0.022, 0.048 |
Gelernter et al. [114] | EA, AA | 8246 | Opioid dependence | KCNG2 | rs62103177 | P = 3.60E-10 |
Herman et al. [115] | EA, AA | 1367 | Opioid dependence | CNR1 | rs6928499 rs806379 rs1535255 rs2023239 | |
Ho et al. [50] | Chinese | 252 | Opioid dependence | DRD4 | P = 0.041 | |
Kumar et al. [116] | South Asian | 260 | Opioid dependence | CREBBP | rs3025684 | P < 0.0001 |
Kumar et al. [26] | Bengali/Hindu | 330 | Opioid addiction | OPRM1 | rs16918875 rs702764 rs963549 | P = 0.0264 |
Levran et al. [117] | 74 | Opioid addiction | CYP2B6 | |||
Liu et al. [118] | African | 3627 | Opioid addiction | NCK2 | rs2377339 | P = 1.33E-11 |
Nagaya et al. [28] | Asian | 160 | Opioid addiction | OPRM1 | rs1799972 | OR = 1.77, P < 0.0001 |
Zhu et al. [53] | Chinese | 939 | Opioid dependence/addiction | DRD1 | rs686 | P = 0.0003 |
Overlapping genes for networks of nicotine, alcohol and opioid addiction; focus genes from literature are bolded
A: Opioids ∩ Alcohol | B: Opioids ∩ Nicotine | C: Opioids ∩ Alcohol ∩ Nicotine | |||
---|---|---|---|---|---|
Molecule | Edges in opioid network/edges in alcohol network | Molecule | Edges in opioid network/edges in nicotine network | Molecule | Edges in opioid network/edges in alcohol network/edges in nicotine network |
NFkB (complex) | 112/86 | ERK1/2 | 74/76 | ERK1/2 | 74/62/76 |
ERK1/2 | 74/62 | ARRB2 | 8/3 | DRD2 | 6/3/4 |
IL1R1 | 7/4 | DRD2 | 6/4 | TAP1 | 5/5/3 |
IL1 | 6/8 | HSPD1 | 5/4 | SAA | 4/3/4 |
DEFB4A/DEFB4B | 6/4 | TAP1 | 5/3 | PSMB9 | 4/3/3 |
DRD2 | 6/3 | SAA | 4/4 | TAPBP | 4/3/3 |
ELANE | 5/6 | PSMB9 | 4/3 | ELF3 | 4/3/2 |
F2RL1 | 5/6 | TAPBP | 4/3 | TAC1 | 4/3/2 |
TAP1 | 5/5 | ELF3 | 4/2 | CLEC11A | 3/4/2 |
F2R | 5/3 | TAC1 | 4/2 | SMPD2 | 3/3/3 |
ADRBK1 | 5/2 | PSMB10 | 3/3 | CXCL3 | 3/3/2 |
Ikb | 4/4 | SMPD2 | 3/3 | P2RY6 | 3/3/2 |
CXCL2 | 4/3 | AKAP13 | 3/2 | PSMB10 | 3/2/3 |
ELF3 | 4/3 | CLEC11A | 3/2 | AKAP13 | 3/2/2 |
FPR2 | 4/3 | CXCL3 | 3/2 | TLR6 | 3/2/2 |
PSMB9 | 4/3 | P2RY6 | 3/2 | CRHR1 | 2/4/3 |
SAA | 4/3 | TLR6 | 3/2 | CD244 | 2/3/3 |
TAC1 | 4/3 | CD244 | 2/3 | CXCL5 | 2/3/2 |
TAPBP | 4/3 | CRHR1 | 2/3 | CCL21 | 2/2/2 |
DEFB103A/DEFB103B | 4/2 | CCL21 | 2/2 | GMFG | 2/2/2 |
LTF | 3/5 | CNR1 | 2/2 | ||
TNFSF11 | 3/5 | CXCL5 | 2/2 | ||
TNFSF15 | 3/5 | GMFG | 2/2 | ||
CLEC11A | 3/4 | GPRASP1 | 2/2 | ||
TLR1 | 3/4 | BDNF | 2/1 | ||
CXCL3 | 3/3 | ||||
KLF6 | 3/3 | ||||
P2RY6 | 3/3 | ||||
SMPD2 | 3/3 | ||||
AKAP13 | 3/2 | ||||
ARF6 | 3/2 | ||||
IER3 | 3/2 | ||||
PSMB10 | 3/2 | ||||
TLR6 | 3/2 | ||||
TRPC6 | 3/2 | ||||
CRHR1 | 2/4 | ||||
CCL22 | 2/3 | ||||
CD244 | 2/3 | ||||
CXCL5 | 2/3 | ||||
CC2D1A | 2/2 | ||||
CCL21 | 2/2 | ||||
GMFG | 2/2 | ||||
SH3GLB2 | 2/2 | ||||
STAB2 | 2/2 | ||||
TSC22D3 | 2/2 | ||||
OPRM1 | 1/2 |
IPA – Phenotype-specific biological network
Individual gene networks were generated through IPA’s Core Analysis for each addiction phenotype (Additional file 1: Figures S1-S3). TNF, NF-κB, and ERK1/2 were present as highly interconnected genes for alcohol addiction (103, 86, and 62 edges, respectively). For nicotine addiction, TNF, ERK1/2 and Akt had the most edges (85, 76, and 53, respectively). NF-κB, RELA, and ERK1/2 were most interconnected for opioid addiction (112, 92, and 74 edges respectively).
IPA – Common biological network
Table 4 lists overlapping genes for alcohol and opioids (A), smoking and opioids (B), and all three addiction phenotypes (C). Genes were ranked by the number of edges within the opioid network. The network for opioid addiction was found to have the most number of genes that overlap with the network for alcohol addiction relative to the smoking addiction genes. ERK1/2 was found to be very strongly interconnected across all three addiction networks with 74 edges in opioid network, 62 edges in alcohol network and 76 edges in nicotine network (Table 4, panel C). ERK1/2 also shows with highest number of edges in opioid and nicotine network (Table 4, panel B) and second highest edges in opioid and alcohol network (Table 4, panel A). We also noticed that some commonly shared genes are involved in the immune response. Specifically, the immune response genes that were common in the three networks (panel C) were: corticotropin-releasing hormone receptor 1 (CRHR1), chemokine ligand 21 (CCL21), chemokine ligand 3 (CXCL3), chemokine ligand 5 (CXCL5) and toll-like receptor 6 (TLR6). In addition to the above genes, the following immune response genes were also found in opioid and alcohol genes networks (panel A): beta-defensin 103 (DEFB103A/DEFB103B), beta-defensin 2 (DEFB4A/DEFB4B), elastase neutrophil expressed (ELANE), protease activated receptor 2 (F2RL1), lactoferrin (LTF), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kappa B), toll-like receptor 1 (TLR1), TSC22 domain family protein 3 (TSC22D3), chemokine ligand 22 (CCL22), chemokine ligand 2 (CXCL2), interleukin 1 receptor type 1 (IL1R1), tumor necrosis factor ligand superfamily member 11 and 15 (TNFSF11 and TNFSF15).
Top canonical pathways obtained by pooling all 56 focus genes for alcohol, nicotine and opioid addiction. Blue bars: p-score for each of the canonical pathways. Yellow lines: ratio for each of the canonical pathways, calculated as the number of focus genes included in the canonical pathway divided by the total number of genes that constitute the canonical pathway
Biological context
The links of genes associated with addiction to opioids, tobacco and alcohol to components of the brain “reward circuit”
Discussion
One of the most challenging areas of oncologic medicine is the management and treatment of severe, chronic pain that arises from cancer therapies, including surgery, chemotherapy, and radiation, as well as cancer itself. Opioids remain the drugs of choice for cancer pain management [54], however, the use of opioids for treatment of chronic pain in cancer patients remains debatable. An increasing concern is the potential rise in aberrant drug-taking behaviors of cancer patients undergoing treatment for chronic pain [3, 55]. Given that addictions to alcohol and tobacco are known risk factors for cancer, exploring genetic markers of risk for these addiction phenotypes in cancer patients may help in risk stratification. Indeed, studies have begun to show that genetic vulnerability to different substances of addiction may partly overlap [56]. The primary aims of this study were to determine whether there exists a genetic basis to the relationship between smoking, alcohol, and opioid addiction, and to identify candidate genes associated with the three phenotypes for further study.
We used IPA, a bioinformatics tool, to identify commonly shared genes for alcohol, smoking, and opioid addiction. Of the 20 genes commonly shared across the alcohol, smoking and opioid addiction phenotypes, extracellular-signal-regulated kinases 1 and 2 (ERK1/2) was found to have the most interconnections across all three addiction networks as indicated by the number of edges (biological interactions; Table 4). Recent studies suggest the relevance of ERK pathway in drug addiction. Several studies have cited the role of ERK in brain’s response to drugs of abuse [57–59]. Specifically, Valjent et al. [59] demonstrated that multiple drugs of abuse increased activation of ERK1/2. Molecular mechanisms underlying ERK1/2 activation by drugs of abuse and the role of ERK1/2 signaling in long-term neuronal plasticity in the striatum may provide novel targets for therapeutic intervention in addiction [60]. Moreover, studies exploiting ERK activation for cancer therapy have been promising, including the use of MEK inhibitors to block ERK activation in acute lymphoblastic leukemia for instance [61]. Future studies are needed to assess the potential clinical relevance of ERK1/2 for addiction, e.g., to genotype ERK1/2 and stratify patients for prompt intervention, or to determine appropriate dosage of opioid analgesics to patients with specific genotypes.
Of note, the identified shared genes for the three addiction phenotypes are involved in immune response. This is consistent with recent research that implicates immune signaling in drug addiction. Dafney et al. demonstrated that certain immunosuppressive treatments controlled morphine withdrawal in rats [62, 63]. More recent studies demonstrated that blocking pro-inflammatory glial activation could block the elevation of dopamine induced by opioid receptor activity [64, 65]. Hutchinson et al. have also found evidence that toll-like receptors (TLRs), a class of innate immune receptors, interact with opioids and glial cells, contributing to opioid reward behaviors [65]. Our recent studies also showed that cytokine genes are implicated in pain, depressed mood, and fatigue in cancer patients [66–68], and these cytokines may serve as biomarkers of risk for persistent pain in cancer patients.
Furthermore, it is also speculated that synaptic plasticity induced by substances of abuse in the neuronal circuits of reward may underlie behavioral changes that characterize addiction. Importantly, NF-kappa B may be the link between inflammation and neuronal/synaptic plasticity involved in behavioral changes in addiction, as we have shown that all the commonly shared immune response genes of three addiction phenotypes were linked to NF-kappa B in the reward circuit (Fig. 5). NF-kappa B is one of several transcription factors present at the synapse, and it is activated by brain-specific activators such as glutamate (via AMPA/KA and NMDA receptors) and neurotrophins [69]. To date, there are currently no pharmacotherapies for drug addiction targeting immune signaling.
Our results also showed the top canonical pathways associated with all the 56 focus genes of three addiction phenotypes were: 1) calcium signaling, 2) GPCR signaling, 3) cAMP-mediated signaling, 4) GABA receptor signaling, and 5) Gαi signaling. These pathways have been confirmed to be associated with substance addiction in the literature [70–74]. They are the post-receptor signaling pathways for the glutaminergic, dopaiminergic and GABAergic neurons involved in the “reward circuitry” in mammalian brains [75]. Whether these pathways can be used as targets for drug addiction therapy needs to be explored. Our approach of identifying genetic variations associated with addiction to multiple substances and linking to known the neural signaling network involved in substance addiction in the brain has clarified the functional significance of many of the genetic associations to substance addiction. This bioinformatics approach has also identified signaling pathways that may be targeted by drugs. Promising research has shown that allosteric modulators of GPCRs can be used to treat addiction by altering the affinity of the GPCR to its ligand or impacting its downstream signaling responses [72]. Other studies have also suggested positive allosteric modulation of GABAB as a therapeutic strategy for treatment of addiction [71, 76].
Among the limitations of this study is that edges are simplified in the IPA designates only a single edge between each pair of molecules in a network regardless of the number of interactions the two molecules share. Furthermore, this bioinformatics analysis is hypothesis-generating, and the findings must be further investigated and validated experimentally.
Conclusions
Studying smoking, alcohol, and opioid addiction phenotypes in conjunction allowed us to identify molecules and pathways involved in multiple types of drug addiction. IPA is able to use large-scale information to produce comprehensive networks of genes and underlying biological pathways implicated in a phenotype [19]. Most of the current literature on addiction genes focuses on genes specific to each type of addiction, while in this study we studied genes relating to multiple addiction phenotypes. Our findings show immune signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes including alcohol, smoking and opioid addiction. Future studies are needed to validate our findings in large cohorts of patients.
Notes
Declarations
Authors’ Affiliations
References
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