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. Author manuscript; available in PMC: 2021 Sep 1.
Published in final edited form as: Drug Alcohol Depend. 2020 Jul 2;214:108155. doi: 10.1016/j.drugalcdep.2020.108155

Leveraging genetic data to investigate molecular targets and drug repurposing candidates for treating alcohol use disorder and hepatotoxicity

Joshua C Gray 1,*, Mikela Murphy 1, Lorenzo Leggio 2
PMCID: PMC7423741  NIHMSID: NIHMS1612077  PMID: 32652377

Abstract

Background:

Novel treatments for alcohol use disorder (AUD) and alcohol-related liver disease (ALD) are greatly needed. Genetic information can improve drug discovery rates by facilitating the identification of novel biological targets and potential drugs for repurposing.

Methods:

The present study utilized a recently developed Bayesian approach, Integrative Risk Gene Selector (iRIGS), to identify additional risk genes for alcohol consumption using SNPs from the largest alcohol consumption GWAS to date (N = 941,280). iRIGS incorporates several genomic features and closeness of these genes in network space to compute a posterior probability for protein coding genes near each SNP. We subsequently used the Target Central Resource Database to search for drug-protein interactions for these newly identified genes and previously identified risk genes for alcohol consumption.

Results:

We identified several genes that are novel contributions to the previously published alcohol consumption GWAS. Namely, ACVR2A, which is critical for liver function and linked to anxiety and cocaine self-administration, and PRKCE, which has been linked to alcohol selfadministration. Notably, only a minority of the SNPs (18.4%) were linked to genes with confidence (≥.75), underscoring the need to apply multiple methods to assign function to loci. Finally, some previously identified risk genes for alcohol consumption code for proteins that are implicated in liver function and are targeted by drugs, some of which are candidates for managing hepatotoxicity.

Conclusions:

This study demonstrates the value of incorporating regulatory information and drug-protein interaction data to highlight additional molecular targets and drug repurposing candidates for treating AUD and ALD.

Keywords: alcohol, drug repurposing, hepatotoxicity, liver disease, psychiatric genetics

1. Introduction

Improving the treatment of patients with alcohol use disorder (AUD) and alcohol-related liver disease (ALD) is of vital importance from a clinical and public health standpoint (Leggio and Lee, 2017). Only three medications have been approved by the FDA to treat AUD – the last approval took place almost 15 years ago. No medications are approved for ALD. Targeting disease mechanisms with genetic support can increase success in drug development (Nelson et al., 2015). However, translating genome-wide association studies (GWASs) of complex diseases to target discovery and medication development remains challenging (Oprea et al., 2018). AUD is exemplary of this challenge; numerous large GWASs have yielded many significant SNPs (Kranzler et al., 2019; Liu et al., 2019), yet limited drug targets for treating AUD and alcohol-related consequences, such as ALD, have been identified.

We conducted two sets of analyses to identify additional risk genes for alcohol consumption and assess target druggability. First, we applied a modified version of the Integrative Risk Gene Selector (iRIGS) (Wang et al., 2019) to 98 genome-wide significant SNPs from the largest alcohol consumption GWAS to date, which assessed drinks per week (M = 7.84) in 941,280 individuals (Liu et al., 2019). In this study, iRIGS ranked genes at each SNP by integrating evidence from distal regulatory elements-promoter links to yield potential risk genes for alcohol consumption. Second, we classified gene encoded protein druggability based on the Target Development/Druggability Level (TDL) classification system (Oprea et al., 2018) from the Target Central Resource Database (TCRD).

2. Material and methods

2. 1. Integrative Risk Gene Selector (iRIGS).

For the first set of analyses, we applied a modified version of iRIGS to 98 genome-wide significant SNPs (p < 5 x 10−8) associated with drinks per week from the GSCAN study (Liu et al., 2019) (rs7074871 was excluded because it had no nearby protein-coding genes). Briefly, iRIGS integrates two layers of information: 1) genomic features (i.e., distance from gene to SNP and four sets of regulatory connections derived from distal regulatory elements-promoter links from the chromosome conformation capture techniques Hi-C and capture Hi-C, and Functional Annotation of the Mammalian Genome 5 [FANTOM5] data) for genes within a 1 megabase (Mb) flanking region of each SNP; and 2) closeness of each gene in the network (Wang et al., 2019). Our modified version of the iRIGS method did not include de novo mutation enrichment or differential expression because the datasets used for the latter were specific to schizophrenia, there are no equivalent datasets to our knowledge for alcohol consumption, and there are many non-brain tissues relevant to alcohol consumption. We defined the cutoff for potential risk genes to be a posterior probability of ≥.75, indicating the gene is 75% likely to be related to the SNP (the probabilities of all genes within ±1Mb of a given SNP add up to 100%).

2. 2. Target Development/Druggability Level (TDL).

For the second set of analyses, we incorporated genes identified in the iRIGS analyses and the analyses conducted in the prior GWAS. The methods for generating these alcohol consumption genes are discussed in detail in the prior study (Liu et al., 2019). Briefly, they defined a gene as implicated if it harbored variation of LD r2 > .3 with a genome-wide significant SNP or if it was located within 500kb of the SNP and was significant by the PASCAL gene-based test. This yielded 307 unique genes for our analyses. In order to identify the overlap of new and previously identified risk genes for alcohol consumption with preexisting drugs, we integrated drug-protein interaction information from the TCRD (Oprea et al., 2018). The TCRD defines TDLs according to 4 levels of confidence: Tclin, targets have approved drug(s) with known mechanism(s) of action; Tchem targets have drugs or small molecules that satisfy activity thresholds; Tbio targets have no known drugs or small molecules that satisfy thresholds, but have Gene Ontology (GO) leaf term annotations, Online Mendelian Inheritance in Man (OMIM) phenotypes, or meet two of the three conditions: a fractional PubMed count > 5, > 3 National Center for Biotechnology Gene Reference Intro Function annotations, or > 50 commercial antibodies; Tdark refers to proteins that have been manually curated in UniProt, but do not meet criteria for the above categories. Finally, to explore if the findings were specific to alcohol consumption, we conducted the aforementioned iRIGS and TDL analyses for the 4 smoking phenotypes from the GSCAN study (age of initiation, cigarettes per day, cessation, and initiation).

3. Results

3. 1. Integrative Risk Gene Selector (iRIGS).

18 of the 98 genes (18.4%) exhibited high posterior probability (≥.75), indicating support from multiple genetic features including distance to SNP and regulatory information. 7 of these 18 genes (38.9%) were the not the closest protein coding gene to the corresponding SNP. This is consistent with prior work finding many HRGs are not the most proximal to the SNP (Wang et al., 2019). The HRGs that exhibited high posterior probability and were not the closest gene to the SNP were GALNT17 (polypeptide Nacetylgalactosaminyltransferase 17), AKAP13 (A-kinase anchoring protein 13), PPP3CA (protein phosphatase 3 catalytic subunit alpha), KLF4 (Kruppel-like factor 4), PRKCE (protein kinase C epsilon), and ZEB2 (zinc finder E-box binding homeobox 2).

Only 5 of these 18 genes (27.8%) overlapped with the 307 genes identified in the prior alcohol consumption GWAS. This indicates that 13 unique genes were identified with iRIGS (Table 1). The highest ranking genes for each SNP as well as all protein-coding genes considered within the 1Mb flanking region of each SNP are in the Supplementary Materials (Table S1 and Table S2, respectively).

Table 1.

Genetic and target development/druggability level (TDL) information for the highprobability (≥.75) high-confidence risk gene (HRGs)

SNP HRG Posterior
probability		 TDL Nearest protein
coding gene		 Overlap of HRG with
Liu et al. (2019)
rs62044525 CDH11 1 Tbio CDH11 N
rs62250685 CADM2 1 Tbio CADM2 Y
rs74664784 CADM2 1 Tbio CADM2 Y
rs4916723 MEF2C 0.99 Tbio MEF2C N
rs9950000 TCF4 0.98 Tbio TCF4 Y
rs10085696 GALNT17 0.98 Tbio AUTS2 N
rs12907323 AKAP13 0.91 Tbio AGBL1 N
rs10004020 FBXW7 0.90 Tbio FBXW7 N
rs72859280 ACVR2A 0.88 Tchem ACVR2A N
rs4699791 PPP3CA 0.87 Tchem EMCN N
rs4842786 BTG1 0.86 Tbio BTG1 N
rs10978550 KLF4 0.85 Tbio ZNF462 N
rs11739827 TENM2 0.84 Tbio TENM2 Y
rs1004787 PRKCE 0.83 Tchem SIX3 N
rs13024996 ZEB2 0.82 Tbio ARHGAP15 N
rs13383034 PRKCE 0.81 Tchem SIX3 N
rs682011 SORL1 0.75 Tbio SORL1 N
rs12088813 PDE4B 0.75 Tclin PDE4B Y
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Note. The definitions of Tbio, Tchem, and Tclin are provided in the Materials and methods.

3. 2. Target Development/Druggability Level (TDL).

Of the 18 genes identified by iRIGS, PDE4B was Tclin, targeted by 8 approved drugs; ACVR2A (activin receptor type-2A), PRKCE, and PPP3CA were Tchem (i.e., small molecules bind to them with high potency); and the rest were Tbio (Table 1). PDE4B was already identified in the alcohol consumption GWAS and thus no novel Tclin genes were identified with iRIGS. However, ACVR2A, PRKCE, and PPP3CA were all unique genes (Liu et al., 2019). ACVR2A and PRKCE were also identified in the iRIGS analyses of the GSCAN smoking phenotypes (Table S3-S6).

Of the 307 protein-coding genes identified in the alcohol consumption GWAS, 17 were Tclin, targeted by 104 unique drugs (65 of which target DRD2), 29 were Tchem, 198 were Tbio, and 63 were Tdark. The 17 Tclin genes and their approved drugs are depicted in Figure 1. The TDLs for all 307 genes are provided in the Supplementary Materials (Table S7). All of the Tclin genes were specific to alcohol consumption with the exception of PDE4B, DRD2, CYP3A5, CYP3A43, CYP3A7, and CYP3A4; all of which were also associated with one or more GSCAN smoking phenotypes (Liu et al., 2019).

Fig 1.

Fig 1.

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Approved drugs that interact with proteins produced by the genes identified in Liu et al. (2019). Anatomical Therapeutic Chemical (ATC) Classification System codes are color coded as indicated in the key. Medications with multiple ATC codes are assigned all of the corresponding colors (e.g., miconazole). DRD2 is linked to 65 approved drugs (not depicted here as it is a wellestablished top psychiatric drug target (Liu et al., 2019; Oprea et al., 2018)). No unique Tclin genes were identified by the iRIGS alcohol consumption analysis.

4. Discussion

This study identified several novel potential risk genes for alcohol consumption and highlights putative targets for the treatment of AUD and/or ALD. In particular, ACVR2A codes for activin receptor type-2A, which has been linked to liver function (Haridoss et al., 2017), cocaine self-administration (Gancarz et al., 2015; Wang et al., 2017), and anxiety (Ageta et al., 2008). With regard to liver function, one study using in vitro models found activin A, a ligand that binds with high affinity to activin type 2 receptors, is critical to normal liver function and suggested inhibition of activin A or its downstream signaling could be a new approach for treating liver disease (Haridoss et al., 2017). Furthermore, activin A serum levels have been found to be elevated in patients with ALD compared to patients with non-alcohol related liver disease at various stages (Voumvouraki et al., 2012). Rodent studies have identified increases in activin A and activin type-2A levels in the nucleus accumbens following withdrawal from cocaine (Gancarz et al., 2015; Wang et al., 2017). Relatedly, two studies using transgenic mice expressing a dominant-negative activin receptor type-1B (also recruited in the activin A signaling pathway (Loomans and Andl, 2014)) in forebrain neurons found reduced alterations in GABAergic inhibition, hypersensitivity to the sedating effects of alcohol (Zheng et al., 2016), and low anxiety (Zheng et al., 2009).

With regard to PRKCE, rodent studies have found a robust link of PKCε with alcohol consumption (Choi et al., 2002; Cozzoli et al., 2016; Lesscher et al., 2009). In particular, alcohol exposure causes changes to PKCε expression and localization in various brain regions that are implicated in addiction leading to increased alcohol tolerance and consumption (for a review see (Pakri Mohamed et al., 2018)). A recent study tested several novel molecules that act as PKCε inhibitors, finding that two promising compounds that inhibited PKCε with selectivity, crossed the blood-brain barrier, prevented alcohol-stimulated GABA release in the central amygdala, and reduced alcohol consumption in wild-type but not in Prkce−/− mice (Blasio et al., 2018). Thus, this study provides the first human genetic support for the link between PRKCE and alcohol consumption, and the fact that iRIGS identified a distant gene linked to alcohol consumption that has been previously identified experimentally, supports the validity of iRIGS for identifying additional relevant genes.

PPP3CA codes for calcineurin, which has been found to be a regulator of GABAA receptor synaptic retention and plasticity (Bannai et al., 2015; Eckel et al., 2015) and linked to diazepam response in vitro (Nicholson et al., 2018) and in mice (Lorenz-Guertin et al., 2019). Given GABAA receptors are a primary target responsible for the effects of alcohol (for a review see Roberto and Varodayan, 2017), calcineurin is likely linked with drinking via this mechanism.

This study’s analysis of the 307 genes from the prior GWAS (Liu et al., 2019) using the TDL system highlighted 17 genes that code for proteins that are targeted by at least one approved drug. Many of the drugs are promising candidates for managing liver toxicity. Fomepizole (.ADH1A, ADH1B, ADH1C) blocks alcohol dehydrogenase and is approved to treat methanol and ethylene glycol toxicity (Ng et al., 2018). Metformin (NDUFS3) has been highlighted as a promising hepatoprotective agent including for ALD (Iranshahy et al., 2019). Likewise, beraprost and misoprostol (PTGER3), have shown promise for managing acute liver injury and liver disease (Deng et al., 2018; Gobejishvili et al., 2015; Misawa et al., 2017). Pentoxifylline has been often used to treat severe alcoholic hepatitis and a recent clinical practice update indicates that patients with a contraindication to glucocorticoids may be treated with pentoxifylline (Mitchell et al., 2017). However, this clinical practice update also notes that recent data question its clinical utility, (Singh et al., 2015; Thursz et al., 2015) thus further research is needed. Iloprost (PTGER3) has shown promise for managing bone marrow oedema and early stages osteonecrosis (for a review see Pountos and Giannoudis, 2018), of which excessive alcohol use is a risk factor for. Finally, PDE4 inhibitors are currently being investigated for ALD and the PDE4/PDE10 inhibitor, ibudilast, is being investigated for AUD (Ray et al., 2017; Rodriguez et al., 2019).

Numerous genes and associated drugs were also identified for the Cytochromes P450 family of enzymes, namely CYP3A4, which is implicated in the metabolism of numerous approved drugs including many antiretroviral drugs identified here (e.g., (Midde et al., 2016)). Although three small studies have examined nefazodone for AUD (Hernandez-Avila et al., 2004; Kranzler et al., 2000; Roy-Byrne, Peter P. Pages et al., 2000), interest in this medication waned given risk for severe liver toxicity in a minority of patients and an associated FDA black box warning (Edwards, 2003). Verapamil has been found to prevent cue-induced reinstatement of alcohol seeking in rats, suggesting it may be a promising compound for relapse prevention (Uhrig et al., 2017). Metyrapone has been found to prevent alcohol withdrawal associated working memory deficits and reestablish prefrontal cortex activity in withdrawn mice (Dominguez et al., 2017). In summary, the CYP3A4 findings suggest the TCRD may be a novel method for not only identifying putative treatment targets, but also flagging potential deleterious drug-alcohol interactions. Indeed, as research with nefazodone highlights, extra caution is required when developing or prescribing certain medications for AUD, particularly in patients with ALD, given the heightened risk for liver toxicity and/or drug-alcohol interactions (Leggio and Lee, 2017).

4. 1. Conclusions.

We incorporated regulatory information and drug-protein interaction data to highlight promising molecular targets and drugs for potential repurposing. Notably, iRIGS only matched a minority of the alcohol consumption SNPs (18.4%) to genes at a confidence (≥.75) consistent with previous studies (Li et al., 2020; Wang et al., 2019), with some SNPs not matched with confidence despite being in well-replicated loci (e.g., ADH1B), underscoring the need to apply multiple methods to assign function to genome-wide significant loci. We focused exclusively on the SNPs from the alcohol consumption GWAS (Liu et al., 2019) in this study because it included the largest sample size and the largest number of significant loci, however, it will be important for future studies to extend these methods to GWAS of other alcohol-related phenotypes (e.g., AUD diagnosis; (Kranzler et al., 2019)) and non-European Ancestry individuals. The methods utilized within this manuscript are readily available (https://github.com/CNPsyLab/Alcohol-Genetics-iRIGS-TCRD) and applicable to other complex traits.

Supplementary Material

1

Highlights.

  • Integrating regulatory information identified additional alcohol consumption genes

  • Some of these genes have been linked to liver function and alcohol administration

  • Searching drug-protein interactions can identify drug repurposing candidates

  • Some drug-protein interactions showed promise for managing hepatotoxicity

  • Future study is needed to further validate these promising findings

Acknowledgments

Role of Funding Source

LL is supported by the National Institute on Alcohol Abuse and Alcoholism Division of Intramural Clinical and Biological Research and the National Institute on Drug Abuse Intramural Research Program (ZIA-AA000218, Section on Clinical Psychoneuroendocrinology and Neuropsychopharmacology; PI: Leggio).

Footnotes

Conflict of Interest

No conflict declared.

Disclaimer: The opinions and assertions expressed herein are those of the authors and do not necessarily reflect the official policy or position of the Uniformed Services University, the Department of Defense or the National Institutes of Health.

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References

  1. Ageta H, Murayama A, Migishima R, Kida S, Tsuchida K, Yokoyama M, Inokuchi K, 2008. Activin in the Brain Modulates Anxiety-Related Behavior and Adult Neurogenesis. PLoS One 3, el869. doi: 10.1371/journal.pone.0001869 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bannai H, Niwa F, Sherwood MW, Shrivastava AN, Arizono M, Miyamoto A, Sugiura K, Lévi S, Triller A, Mikoshiba K, 2015. Bidirectional Control of Synaptic GABAAR Clustering by Glutamate and Calcium. Cell Rep. 13, 2768–2780. doi: 10.1016/J.CELREP.2015.12.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Blasio A, Wang J, Wang D, Varodayan FP, Pomrenze MB, Miller J, Lee AM, McMahon T, Gyawali S, Wang H-Y, Roberto M, McHardy S, Pleiss MA, Messing RO, 2018. Novel Small-Molecule Inhibitors of Protein Kinase C Epsilon Reduce Ethanol Consumption in Mice. Biol. Psychiatry 84, 193–201. doi: 10.1016/J.BIOPSYCH.2017.10.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Choi D-S, Wang D, Dadgar J, Chang WS, Messing RO, 2002. Conditional rescue of protein kinase C epsilon regulates ethanol preference and hypnotic sensitivity in adult mice. J. Neurosci 22, 9905–11. doi: 10.1523/JNEUROSCI.22-22-09905.2002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cozzoli DK, Courson J, Rostock C, Campbell RR, Wroten MG, McGregor EL, Caruana AL, Miller BW, Hu J-H, Wu Zhang P, Xiao B, Worley PF, Crabbe JC, Finn DA, Szumlinski KK, 2016. Protein Kinase C Epsilon Activity in the Nucleus Accumbens and Central Nucleus of the Amygdala Mediates Binge Alcohol Consumption. Biol. Psychiatry 79, 443–451. doi: 10.1016/J.BIOPSYCH.2015.01.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Deng J, Feng J, Liu T, Lu X, Wang W, Liu N, Lv Y, Liu Q, Guo C, Zhou Y, 2018. Beraprost sodium preconditioning prevents inflammation, apoptosis, and autophagy during hepatic ischemia-reperfusion injury in mice via the P38 and JNK pathways. Drug Des. Devel. Ther 12, 4067–4082. doi: 10.2147/DDDT.S182292 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Dominguez G, Belzung C, Pierard C, David V, Henkous N, Decorte L, Mons N, Beracochea D, 2017. Alcohol withdrawal induces long-lasting spatial working memory impairments: relationship with changes in corticosterone response in the prefrontal cortex. Addict. Biol. 22, 898–910. doi: 10.1111/adb.12371 [DOI] [PubMed] [Google Scholar]
  8. Eckel R, Szulc B, Walker MC, Kittler JT, 2015. Activation of calcineurin underlies altered trafficking of α2 subunit containing GABAA receptors during prolonged epileptiform activity. Neuropharmacology 88, 82–90. doi: 10.1016/J.NEUROPHARM.2014.09.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Edwards IR, 2003. Withdrawing drugs: nefazodone, the start of the latest saga. Lancet (London, England) 361, 1240. doi: 10.1016/S0140-6736(03)13030-9 [DOI] [PubMed] [Google Scholar]
  10. Gancarz AM, Wang Z-L, Schroeder GL, Damez-Werno D, Braunscheidel KM, Mueller E, Humby MS, Caccamise A, Martin JA, Dietz KC, Neve RL, Dietz DM, 2015. Activin receptor signaling regulates cocaine-primed behavioral and morphological plasticity. Nat. Neurosci 18, 959–61. doi: 10.1038/nn.4036 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Gobejishvili L, Ghare S, Khan R, Cambon A, Barker DF, Barve S, McClain C, Hill D, 2015. Misoprostol modulates cytokine expression through a cAMP pathway: Potential therapeutic implication for liver disease. Clin. Immunol 161, 291–299. doi: 10.1016/J.CLIM.2015.09.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Haridoss S, Yovchev MI, Schweizer H, Megherhi S, Beecher M, Locker I, Oertel M, 2017. Activin A is a prominent autocrine regulator of hepatocyte growth arrest. Hepatol. Commun 1, 852–870. doi: 10.1002/hep4.1106 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hernandez-Avila CA, Modesto-Lowe V, Feinn R, Kranzler HR, 2004. Nefazodone Treatment of Comorbid Alcohol Dependence and Major Depression. Alcohol. Clin. Exp. Res 28, 433–440. doi: 10.1097/01.ALC.0000118313.63897.EE [DOI] [PubMed] [Google Scholar]
  14. Iranshahy M, Rezaee R, Karimi G, 2019. Hepatoprotective activity of metformin: A new mission for an old drug? Eur. J. Pharmacol 850, 1–7. doi: 10.1016/J.EJPHAR.2019.02.004 [DOI] [PubMed] [Google Scholar]
  15. Kranzler EL, Modesto-Lowe V, Kirk J. Van, 2000. Naltrexone vs. Nefazodone for Treatment of Alcohol Dependence A Placebo-Controlled Trial. Neuropsychopharmacology 22, 493–503. doi: 10.1016/S0893-133X(99)00135-9 [DOI] [PubMed] [Google Scholar]
  16. Kranzler HR, Zhou EL, Kember RL, Vickers Smith R, Justice AC, Damrauer S, Tsao PS, Klarin D, Baras A, Reid J, Overton J, Rader DJ, Cheng Z, Tate JP, Becker WC, Concato J, Xu K, Polimanti R, Zhao EL, Gelernter J, 2019. Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nat. Commun 10, 1499. doi: 10.1038/s41467-019-09480-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Leggio L, Lee MR, 2017. Treatment of Alcohol Use Disorder in Patients with Alcoholic Liver Disease. Am. J. Med 130, 124–134. doi: 10.1016/J.AMJMED.2016.10.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Lesscher HMB, Wallace MJ, Zeng L, Wang V, Deitchman JK, McMahon T, Messing RO, Newton PM, 2009. Amygdala protein kinase C epsilon controls alcohol consumption. Genes, Brain Behav. 8, 493–499. doi: 10.1111/j.1601-183X.2009.00485.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Li H-J, Qu N, Hui L, Cai X, Zhang C-Y, Zhong B-L, Zhang S-F, Chen J, Xia B, Wang L, Jia Q-F, Li W, Chang EL, Xiao X, Li M, Li Y, 2020. Further confirmation of netrin 1 receptor (DCC) as a depression risk gene via integrations of multi-omics data. Transl. Psychiatry 10, 98. doi: 10.1038/s41398-020-0777-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, Datta G, Davila-Velderrain J, McGuire D, Tian C, Zhan X, Choquet H, Docherty AR, Faul JD, Foerster JR, Fritsche LG, Gabrielsen ME, Gordon SD, Haessler J, Hottenga J-J, Huang H, Jang S-Κ, Jansen PR, Ling Y, Magi R, Matoba N, McMahon G, Mulas A, Orru V, Palviainen T, Pandit A, Reginsson GW, Skogholt AH, Smith JA, Taylor AE, Turman C, Willemsen G, Young H, Young KA, Zajac GJM, Zhao W, Zhou W, Bjornsdottir G, Boardman JD, Boehnke M, Boomsma DI, Chen C, Cucca F, Davies GE, Eaton CB, Ehringer MA, Esko T, Fiorillo E, Gillespie NA, Gudbjartsson DF, Haller T, Harris KM, Heath AC, Hewitt JK, Hickie IB, Hokanson JE, Hopfer CJ, Hunter DJ, Iacono WG, Johnson EO, Kamatani Y, Kardia SLR, Keller MC, Kellis M, Kooperberg C, Kraft P, Krauter KS, Laakso M, Lind PA, Loukola A, Lutz SM, Madden PAF, Martin NG, McGue M, McQueen MB, Medland SE, Metspalu A, Mohlke KL, Nielsen JB, Okada Y, Peters U, Polderman TJC, Posthuma D, Reiner AP, Rice JP, Rimm E, Rose RJ, Runarsdottir V, Stallings MC, Stančáková A, Stefansson H, Thai KK, Tindle HA, Tyrfingsson T, Wall TL, Weir DR, Weisner C, Whitfield JB, Winsvold BS, Yin J, Zuccolo L, Bierut LJ, Hveem K, Lee JJ, Munafo MR, Saccone NL, Wilier CJ, Cornells MC, David SP, Hinds DA, Jorgenson E, Kaprio J, Stitzel JA, Stefansson K, Thorgeirsson TE, Abecasis G, Liu DJ, Vrieze S, 2019. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet 51, 237–244. doi: 10.1038/s41588-018-0307-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Loomans H, Andl C, 2014. Intertwining of activin A and TGFβ signaling: Dual roles in cancer progression and cancer cell invasion. Cancers (Basel). 7, 70–91. doi: 10.3390/cancers7010070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Lorenz-Guertin JM, Bambino MJ, Das S, Weintraub ST, Jacob TC, 2019. Diazepam Accelerates GABAAR Synaptic Exchange and Alters Intracellular Trafficking. Front. Cell. Neurosci 13, 163. doi: 10.3389/fncel.2019.00163 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Midde NM, Rahman MA, Rathi C, Li J, Meibohm B, Li W, Kumar S, 2016. Effect of Ethanol on the Metabolic Characteristics of HIV-1 Integrase Inhibitor Elvitegravir and Elvitegravir/Cobicistat with CYP3A: An Analysis Using a Newly Developed LC-MS/MS Method. PLoS One 11, e0149225. doi: 10.1371/journal.pone.0149225 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Misawa H, Ohashi W, Tomita K, Hattori K, Shimada Y, Hatton Y, 2017. Prostacyclin mimetics afford protection against lipopolysaccharide/d-galactosamine-induced acute liver injury in mice. Toxicol. Appl. Pharmacol 334, 55–65. doi: 10.1016/J.TAAP.2017.09.003 [DOI] [PubMed] [Google Scholar]
  25. Mitchell MC, Friedman LS, McClain CJ, 2017. Medical management of severe alcoholic hepatitis: Expert review from the clinical practice updates committee of the AGA Institute. Clin. Gastroenterol. Hepatol 15, 5–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Nelson M, Tipney H, Painter J, Shen J, 2015. The support of human genetic evidence for approved drug indications. Nature. [DOI] [PubMed] [Google Scholar]
  27. Ng PCY, Long BJ, Davis WT, Sessions DJ, Koyfman A, 2018. Toxic alcohol diagnosi and management: an emergency medicine review. Intern. Emerg. Med 13, 375–383. doi: 10.1007/s11739-018-1799-9 [DOI] [PubMed] [Google Scholar]
  28. Nicholson MW, Sweeney A, Pekle E, Alam S, Ali AB, Duchen M, Jovanovic JN, 2018. Diazepam-induced loss of inhibitory synapses mediated by PLCδ/ Ca2+/calcineurin signalling downstream of GABAA receptors. Mol. Psychiatry 23, 1851–1867. doi: 10.1038/s41380-018-0100-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Oprea TI, Bologa CG, Brunak S, Campbell A, Gan GN, Gaulton A, Gomez SM, Guha R, Hersey A, Holmes J, Jadhav A, Jensen LJ, Johnson GL, Karlson A, Leach AR, Ma’ayan A, Malovannaya A, Mani S, Mathias SL, McManus MT, Meehan TF, von Mering C, Muthas D, Nguyen D-T, Overington JP, Papadatos G, Qin J, Reich C, Roth BL, Schürer SC, Simeonov A, Sklar LA, Southall N, Tomita S, Tudose I, Ursu O, Vidovic D, Waller A, Westergaard D, Yang JJ, Zahoránszky-Köhalmi G, 2018. Unexplored therapeutic opportunities in the human genome. Nat. Rev. Drug Discov 17, 317–332. doi: 10.1038/nrd.2018.14 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Pakri Mohamed RM, Mokhtar MH, Yap E, Hanim A, Abdul Wahab N, Jaffar FHF, Kumar J, 2018. Ethanol-Induced Changes in PKCε: From Cell to Behavior. Front. Neurosci 12, 244. doi: 10.3389/fnins.2018.00244 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Pountos I, Giannoudis PV, 2018. The role of Iloprost on bone edema and osteonecrosis: Safety and clinical results. Expert Opin. Drug Saf 17, 225–233. doi: 10.1080/14740338.2018.1424828 [DOI] [PubMed] [Google Scholar]
  32. Ray LA, Bujarski S, Shoptaw S, Roche DJ, Heinzerling K, Miotto K, 2017. Development of the Neuroimmune Modulator Ibudilast for the Treatment of Alcoholism: A Randomized, Placebo-Controlled, Human Laboratory Trial. Neuropsychopharmacology 42, 1776–1788. doi: 10.1038/npp.2017.10 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Roberto M, Varodayan FP, 2017. Synaptic targets: Chronic alcohol actions. Neuropharmacology 122, 85–99. doi: 10.1016/j.neuropharm.2017.01.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Rodriguez WE, Wahlang B, Wang Y, Zhang J, Vadhanam MV, Joshi-Barve S, Bauer P, Cannon R, Ahmadi AR, Sun Z, Cameron A, Barve S, Maldonado C, McClain C, Gobejishvili L, 2019. Phosphodiesterase 4 Inhibition as a Therapeutic Target for Alcoholic Liver Disease: From Bedside to Bench. Hepatology 70, 1958–1971. doi: 10.1002/hep.30761 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Roy-Byrne Peter P. Pages KP, Russo JE, Jaffe C, Blume AW, Kingsley E, Cowley DS, Ries RK, 2000. Nefazodone treatment of major depression in alcohol-dependent patients: a double-blind, placebo-controlled trial. J. Clin. Psychopharmacol 20, 129–136. [DOI] [PubMed] [Google Scholar]
  36. Singh S, Murad MH, Chandar AK, Bongiomo CM, Singal AK, Atkinson SR, Thursz MR, Loomba R, Shah VH, 2015. Comparative Effectiveness of Pharmacological Interventions for Severe Alcoholic Hepatitis: A Systematic Review and Network Meta-analysis. Gastroenterology 149, 958–70.e12. doi: 10.1053/j.gastro.2015.06.006 [DOI] [PubMed] [Google Scholar]
  37. Thursz MR, Richardson P, Allison M, Austin A, Bowers M, Day CP, Downs N, Gleeson D, MacGilchrist A, Grant A, Hood S, Masson S, McCune A, Mellor J, O’Grady J, Patch D, Ratcliffe I, Roderick P, Stanton L, Vergis N, Wright M, Ryder S, Forrest EH, 2015. Prednisolone or Pentoxifylline for Alcoholic Hepatitis. N. Engl. J. Med 372, 1619–1628. doi: 10.1056/NEJMoa1412278 [DOI] [PubMed] [Google Scholar]
  38. Uhrig S, Vandael D, Marcantoni A, Dedic N, Bilbao A, Vogt MA, Hirth N, Broccoli L, Bernardi RE, Schonig K, Gass P, Bartsch D, Spanagel R, Deussing JM, Sommer WH, Carbone E, Hansson AC, 2017. Differential Roles for L-Type Calcium Channel Subtypes in Alcohol Dependence. Neuropsychopharmacology 42, 1058–1069. doi: 10.1038/npp.2016.266 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Voumvouraki A, Notas G, Koulentaki M, Georgiadou M, Klironomos S, Kouroumalis E, 2012. Increased serum activin-A differentiates alcoholic from cirrhosis of other aetiologies. Eur. J. Clin. Invest 42, 815–822. doi: 10.1111/j.1365-2362.2012.02647.x [DOI] [PubMed] [Google Scholar]
  40. Wang Q, Chen R, Cheng F, Wei Q, Ji Y, Yang H, Zhong X, Tao R, Wen Z, Sutcliffe JS, Liu C, Cook EH, Cox NJ, Li B, 2019. A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data. Nat. Neurosci 22, 691–699. doi: 10.1038/s41593-019-0382-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Wang Z-J, Martin JA, Gancarz AM, Adank DN, Sim FJ, Dietz DM, 2017. Activin A is increased in the nucleus accumbens following a cocaine binge. Sci. Rep 7, 43658. doi: 10.1038/srep43658 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Zheng F, Adelsberger FL, Müller MR, Fritschy J-M, Werner S, Alzheimer C, 2009. Activin tunes GABAergic neurotransmission and modulates anxiety-like behavior. Mol. Psychiatry 14, 332–346. doi: 10.1038/sj.mp.4002131 [DOI] [PubMed] [Google Scholar]
  43. Zheng F, Puppel A, Huber SE, Link AS, Eulenburg V, van Brederode JF, Muller CP, Alzheimer C, 2016. Activin Controls Ethanol Potentiation of Inhibitory Synaptic Transmission Through GABAA Receptors and Concomitant Behavioral Sedation. Neuropsychopharmacology 41, 2024–33. doi: 10.1038/npp.2015.372 [DOI] [PMC free article] [PubMed] [Google Scholar]

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