We performed a cross-tissue and cross-phenotypic analysis of genome wide methylomic variation in AUD using samples from independent cohorts involving post-mortem brain, blood, liver tissue, and various clinical and neuroimaging phenotypes with the goal of identifying disease-associated methylomic DNA variations. Results show that the gene encoding the enzyme Proprotein Convertase Subtilisin/Kexin 9 (PCSK9) was the primary target of epigenetic changes relevant to AUD across data sets (Lohoff et al, 2018). PCSK9 is predominantly expressed in the liver where it is synthesized and secreted. It targets low-density lipoprotein cholesterol receptors (LDL-R) in the liver cells and interferes with the regulation of LDL cholesterol (LDL-C) in the blood. Epigenetic regulation of PCSK9 expression by alcohol consumption is one potential mechanism to explain lipid metabolism abnormalities found in patients with heavy alcohol use. We continued and expanded on research related to PCSK9 and have several ongoing collaborations with Dr. L. Vendruscolo, Dr. G. Koob, Dr. B. Gao, and Dr. P. Pacher. The collaborations have resulted in the development of a novel animal model of liquid alcohol exposure that more closely resembles alcohol use in humans. In addition, various molecular characterizations have been conducted and are ongoing. Several human clinical studies are being developed. On clinical exam, individuals with AUD often appear older than stated age, suggesting that excessive alcohol consumption might accelerate aging. Studies have shown that individuals with AUD die earlier than healthy controls and are at a significantly increased risk for all-cause mortality 130, 131. Given the potential role of AUD in the aging process, it is important to understand this relationship on a molecular, epigenetic level. To better assess biological age, Hannum and Horvath developed epigenetic clocks that robustly correlate with chronological age 132, 133. These clocks are algorithms that take the weighted average of methylation levels at specific CpG sites to calculate DNA methylation age (DNAm age), which is highly correlated with chronological age. Given our interest in epigenetic regulation in AUD, we hypothesized that individuals with AUD have accelerated biological/epigenetic age. To test this hypothesis, we leveraged our existing data sets and collaborated with Dr. Steve Horvath (UCLA), the inventor of the epigenetic clock. We explored this question in five independent cohorts, including DNA methylation data derived from datasets from blood (n=129, n=329), liver (n=92; n=49), and postmortem prefrontal cortex (n=46). One blood dataset and one liver tissue dataset of individuals with ALC exhibited positive age acceleration (p<0.0001 and p=0.0069, respectively), while the other blood and liver tissue datasets both exhibited trends of positive age acceleration that were not significant (p=0.83 and p=0.57, respectively). Prefrontal cortex tissue exhibited a trend of negative age acceleration (p=0.19) 134. These results warrant further investigation into the role of chronic, heavy alcohol consumption in the aging process in a larger sample while controlling for potential confounds, such as race, gender, and blood cell composition (for whole blood). Another interest of CGET is to investigate the molecular mechanisms of negative affective states and how they contribute to the addiction cycle. Negative emotional states contribute to worsening of the addiction cycle and increase risk for relapse (Ahmed and Koob, 1998; George et al, 2014; Koob, 2015). The withdrawal/negative affect stage in humans is often characterized by symptoms of chronic irritability, physical pain, emotional pain, malaise, dysphoria, anhedonia, hopelessness, and loss of motivation for natural rewards. It is hypothesized that the brain attempts to overcome this state by activating the stress-response system (Koob and Le Moal, 2008), as evidenced by dysregulation of the HPA axis and CRF by all major drugs of abuse. In addition, there is strong evidence that anxiety-like responses occur that are modulated by CRF (Koob, 2015). Not much is known with regard to emotional cognitive processing during this addiction stage, and limited data exist regarding emotional learning as a consequence of negative reinforcement. Brain regions involved in negative emotion processing and learning tend to be located, and perhaps overlap, in the medial prefrontal cortex (mPFC), as shown in previous neuroimaging studies (Lissek et al, 2014; Lohoff et al, 2014; Mickey et al, 2011; Phelps et al, 2004). One hypothesis is that individuals, with a history of trauma/early life stress (ELS), might have a harder time unlearning negative emotional states, including low moods and anxiety. Thus, alcohol provides temporary relief initially. However, as the addiction cycle progresses, individuals experience more negative emotions related to withdrawal that they cannot unlearn and thus continue drinking to alleviate negative emotions despite negative consequences. Better understanding the underlying neurocircuitries and molecular mechanisms of negative emotion processing associated with alcohol addiction, including genetic and epigenetic contributors, is crucial for the development of novel interventions and effective prevention strategies. To investigate this, our sections human clinical protocol 15-AA-0127: (Epi)Genetic modulators of fear extinction in alcohol dependence was developed and is actively recruiting. This protocol aims to investigate underlying neurobiology and neurocircuitries of fear extinction in individuals with AUD with and without ELS.

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5
Fiscal Year
2019
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National Institute on Alcohol Abuse and Alcoholism
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Muench, Christine; Schwandt, Melanie; Jung, Jeesun et al. (2018) The major depressive disorder GWAS-supported variant rs10514299 in TMEM161B-MEF2C predicts putamen activation during reward processing in alcohol dependence. Transl Psychiatry 8:131
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Lohoff, F W; Sorcher, J L; Rosen, A D et al. (2018) Methylomic profiling and replication implicates deregulation of PCSK9 in alcohol use disorder. Mol Psychiatry 23:1-11
Zhu, Xi; Du, Xiaofei; Kerich, Mike et al. (2018) Random forest based classification of alcohol dependence patients and healthy controls using resting state MRI. Neurosci Lett 676:27-33

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