Chemical Genetic Identification of PKC Epsilon Substrates in Mouse Brain.

PKC epsilon (PKCε) plays important roles in behavioral responses to alcohol and in anxiety-like behavior in rodents, making it a potential drug target for reducing alcohol consumption and anxiety. Identifying signals downstream of PKCε could reveal additional targets and strategies for interfering with PKCε signaling. We used a chemical genetic screen combined with mass spectrometry to identify direct substrates of PKCε in mouse brain and validated findings for 39 of them using peptide arrays and in vitro kinase assays. Prioritizing substrates with several public databases such as LINCS-L1000, STRING, GeneFriends, and GeneMAINA predicted interactions between these putative substrates and PKCε and identified substrates associated with alcohol-related behaviors, actions of benzodiazepines, and chronic stress. The 39 substrates could be broadly classified in three functional categories: cytoskeletal regulation, morphogenesis, and synaptic function. These results provide a list of brain PKCε substrates, many of which are novel, for future investigation to determine the role of PKCε signaling in alcohol responses, anxiety, responses to stress, and other related behaviors.

RNA Solutions: Synthesizing Information to Support Transcriptomics (RNASSIST).

MOTIVATION: Transcriptomics is a common approach to identify changes in gene expression induced by a disease state. Standard transcriptomic analyses consider differentially expressed genes (DEGs) as indicative of disease states so only a few genes would be treated as signals when the effect size is small, such as in brain tissue. For tissue with small effect sizes, if the DEGs do not belong to a pathway known to be involved in the disease, there would be little left in the transcriptome for researchers to follow up with. RESULTS: We developed RNA Solutions: Synthesizing Information to Support Transcriptomics (RNASSIST), a new approach to identify hidden signals in transcriptomic data by linking differential expression and co-expression networks using machine learning. We applied our approach to RNA-seq data of post-mortem brains that compared the Alcohol Use Disorder (AUD) group with the control group. Many of the candidate genes are not differentially expressed so would likely be ignored by standard transcriptomic analysis pipelines. Through multiple validation strategies, we concluded that these RNASSIST-identified genes likely play a significant role in AUD. AVAILABILITY AND IMPLEMENTATION: The RNASSIST algorithm is available at https://github.com/netrias/rnassist and both the software and the data used in RNASSIST are available at https://figshare.com/articles/software/RNAssist_Software_and_Data/16617250. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Blood and brain gene expression signatures of chronic intermittent ethanol consumption in mice.

Alcohol Use Disorder (AUD) is a chronic, relapsing syndrome diagnosed by a heterogeneous set of behavioral signs and symptoms. There are no laboratory tests that provide direct objective evidence for diagnosis. Microarray and RNA-Seq technologies enable genome-wide transcriptome profiling at low costs and provide an opportunity to identify biomarkers to facilitate diagnosis, prognosis, and treatment of patients. However, access to brain tissue in living patients is not possible. Blood contains cellular and extracellular RNAs that provide disease-relevant information for some brain diseases. We hypothesized that blood gene expression profiles can be used to diagnose AUD. We profiled brain (prefrontal cortex, amygdala, and hypothalamus) and blood gene expression levels in C57BL/6J mice using RNA-seq one week after chronic intermittent ethanol (CIE) exposure, a mouse model of alcohol dependence. We found a high degree of preservation (rho range: [0.50, 0.67]) between blood and brain transcript levels. There was small overlap between blood and brain DEGs, and considerable overlap of gene networks perturbed after CIE related to cell-cell signaling (e.g., GABA and glutamate receptor signaling), immune responses (e.g., antigen presentation), and protein processing / mitochondrial functioning (e.g., ubiquitination, oxidative phosphorylation). Blood gene expression data were used to train classifiers (logistic regression, random forest, and partial least squares discriminant analysis), which were highly accurate at predicting alcohol dependence status (maximum AUC: 90.1%). These results suggest that gene expression profiles from peripheral blood samples contain a biological signature of alcohol dependence that can discriminate between CIE and Air subjects.

Behavioral mechanisms of context fear generalization in mice.

There is growing interest in generalization of learned contextual fear, driven in part by the hypothesis that mood and anxiety disorders stem from impaired hippocampal mechanisms of fear generalization and discrimination. However, there has been relatively little investigation of the behavioral and procedural mechanisms that might control generalization of contextual fear. We assessed the relative contribution of different contextual features to context fear generalization and characterized how two common conditioning protocols-foreground (uncued) and background (cued) contextual fear conditioning-affected context fear generalization. In one experiment, mice were fear conditioned in context A, and then tested for contextual fear both in A and in an alternate context created by changing a subset of A's elements. The results suggest that floor configuration and odor are more salient features than chamber shape. A second experiment compared context fear generalization in background and foreground context conditioning. Although foreground conditioning produced more context fear than background conditioning, the two procedures produced equal amounts of generalized fear. Finally, results indicated that the order of context tests (original first versus alternate first) significantly modulates context fear generalization, perhaps because the original and alternate contexts are differentially sensitive to extinction. Overall, results demonstrate that context fear generalization is sensitive to procedural variations and likely reflects the operation of multiple interacting psychological and neural mechanisms.

Peroxisome proliferator-activated receptors α and γ are linked with alcohol consumption in mice and withdrawal and dependence in humans.

BACKGROUND: Peroxisome proliferator-activated receptor (PPAR) agonists reduce voluntary ethanol (EtOH) consumption in rat models and are promising therapeutics in the treatment for drug addictions. We studied the effects of different classes of PPAR agonists on chronic EtOH intake and preference in mice with a genetic predisposition for high alcohol consumption and then examined human genomewide association data for polymorphisms in PPAR genes in alcohol-dependent subjects. METHODS: Two different behavioral tests were used to measure intake of 15% EtOH in C57BL/6J male mice: 24-hour 2-bottle choice and limited access (3-hour) 2-bottle choice, drinking in the dark. We measured the effects of pioglitazone (10 and 30 mg/kg), fenofibrate (50 and 150 mg/kg), GW0742 (10 mg/kg), tesaglitazar (1.5 mg/kg), and bezafibrate (25 and 75 mg/kg) on EtOH intake and preference. Fenofibric acid, the active metabolite of fenofibrate, was quantified in mouse plasma, liver, and brain by liquid chromatography tandem mass spectrometry. Data from a human genome-wide association study (GWAS) completed in the Collaborative Study on the Genetics of Alcoholism (COGA) were then used to analyze the association of single nucleotide polymorphisms (SNPs) in different PPAR genes (PPARA, PPARD, PPARG, and PPARGC1A) with 2 phenotypes: DSM-IV alcohol dependence (AD) and the DSM-IV criterion of withdrawal. RESULTS: Activation of 2 isoforms of PPARs, α and γ, reduced EtOH intake and preference in the 2 different consumption tests in mice. However, a selective PPARδ agonist or a pan agonist for all 3 PPAR isoforms did not decrease EtOH consumption. Fenofibric acid, the active metabolite of the PPARα agonist fenofibrate, was detected in liver, plasma, and brain after 1 or 8 days of oral treatment. The GWAS from COGA supported an association of SNPs in PPARA and PPARG with alcohol withdrawal and PPARGC1A with AD but found no association for PPARD with either phenotype. CONCLUSIONS: We provide convergent evidence using both mouse and human data for specific PPARs in alcohol action. Reduced EtOH intake in mice and the genetic association between AD or withdrawal in humans highlight the potential for repurposing FDA-approved PPARα or PPARγ agonists for the treatment of AD.

RNA biomarkers for alcohol use disorder.

Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.

Binge-like ethanol drinking activates anaplastic lymphoma kinase signaling and increases the expression of STAT3 target genes in the mouse hippocampus and prefrontal cortex.

Alcohol use disorder (AUD) has a complex pathogenesis, making it a difficult disorder to treat. Identifying relevant signaling pathways in the brain may be useful for finding new pharmacological targets to treat AUD. The receptor tyrosine kinase anaplastic lymphoma kinase (ALK) activates the transcription factor STAT3 in response to ethanol in cell lines. Here, we show ALK activation and upregulation of known STAT3 target genes (Socs3, Gfap and Tnfrsf1a) in the prefrontal cortex (PFC) and ventral hippocampus (HPC) of mice after 4 days of binge-like ethanol drinking. Mice treated with the STAT3 inhibitor stattic drank less ethanol than vehicle-treated mice, demonstrating the behavioral importance of STAT3. To identify novel ethanol-induced target genes downstream of the ALK and STAT3 pathway, we analyzed the NIH LINCS L1000 database for gene signature overlap between ALK inhibitor (alectinib and NVP-TAE684) and STAT3 inhibitor (niclosamide) treatments on cell lines. These genes were then compared with differentially expressed genes in the PFC of mice after binge-like drinking. We found 95 unique gene candidates, out of which 57 had STAT3 binding motifs in their promoters. We further showed by qPCR that expression of the putative STAT3 genes Nr1h2, Smarcc1, Smarca4 and Gpnmb were increased in either the PFC or HPC after binge-like drinking. Together, these results indicate activation of the ALK-STAT3 signaling pathway in the brain after binge-like ethanol consumption, identify putative novel ethanol-responsive STAT3 target genes, and suggest that STAT3 inhibition may be a potential method to reduce binge drinking in humans.

The COVID-19 Gene and Drug Set Library.

The coronavirus (CoV) severe acute respiratory syndrome (SARS)-CoV-2 (COVID-19) pandemic has received rapid response by the research community to offer suggestions for repurposing of approved drugs as well as to improve our understanding of the COVID-19 viral life cycle molecular mechanisms. In a short period, tens of thousands of research preprints and other publications have emerged including those that report lists of experimentally validated drugs and compounds as potential COVID-19 therapies. In addition, gene sets from interacting COVID-19 virus-host proteins and differentially expressed genes when comparing infected to uninfected cells are being published at a fast rate. To organize this rapidly accumulating knowledge, we developed the COVID-19 Gene and Drug Set Library (https://amp.pharm.mssm.edu/covid19/), a collection of gene and drug sets related to COVID-19 research from multiple sources. The COVID-19 Gene and Drug Set Library is delivered as a web-based interface that enables users to view, download, analyze, visualize, and contribute gene and drug sets related to COVID-19 research. To evaluate the content of the library, we performed several analyses including comparing the results from 6 in-vitro drug screens for COVID-19 repurposing candidates. Surprisingly, we observe little overlap across these initial screens. The most common and unique hit across these screen is mefloquine, a malaria drug that should receive more attention as a potential therapeutic for COVID-19. Overall, the library of gene and drug sets can be used to identify community consensus, make researchers and clinicians aware of the development of new potential therapies, as well as allow the research community to work together towards a cure for COVID-19.

The COVID-19 Drug and Gene Set Library.

In a short period, many research publications that report sets of experimentally validated drugs as potential COVID-19 therapies have emerged. To organize this accumulating knowledge, we developed the COVID-19 Drug and Gene Set Library (https://amp.pharm.mssm.edu/covid19/), a collection of drug and gene sets related to COVID-19 research from multiple sources. The platform enables users to view, download, analyze, visualize, and contribute drug and gene sets related to COVID-19 research. To evaluate the content of the library, we compared the results from six in vitro drug screens for COVID-19 repurposing candidates. Surprisingly, we observe low overlap across screens while highlighting overlapping candidates that should receive more attention as potential therapeutics for COVID-19. Overall, the COVID-19 Drug and Gene Set Library can be used to identify community consensus, make researchers and clinicians aware of new potential therapies, enable machine-learning applications, and facilitate the research community to work together toward a cure.

A Pathway-Based Genomic Approach to Identify Medications: Application to Alcohol Use Disorder.

Chronic, excessive alcohol use alters brain gene expression patterns, which could be important for initiating, maintaining, or progressing the addicted state. It has been proposed that pharmaceuticals with opposing effects on gene expression could treat alcohol use disorder (AUD). Computational strategies comparing gene expression signatures of disease to those of pharmaceuticals show promise for nominating novel treatments. We reasoned that it may be sufficient for a treatment to target the biological pathway rather than lists of individual genes perturbed by AUD. We analyzed published and unpublished transcriptomic data using gene set enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to identify biological pathways disrupted in AUD brain and by compounds in the Library of Network-based Cellular Signatures (LINCS L1000) and Connectivity Map (CMap) databases. Several pathways were consistently disrupted in AUD brain, including an up-regulation of genes within the Complement and Coagulation Cascade, Focal Adhesion, Systemic Lupus Erythematosus, and MAPK signaling, and a down-regulation of genes within the Oxidative Phosphorylation pathway, strengthening evidence for their importance in AUD. Over 200 compounds targeted genes within those pathways in an opposing manner, more than twenty of which have already been shown to affect alcohol consumption, providing confidence in our approach. We created a user-friendly web-interface that researchers can use to identify drugs that target pathways of interest or nominate mechanism of action for drugs. This study demonstrates a unique systems pharmacology approach that can nominate pharmaceuticals that target pathways disrupted in disease states such as AUD and identify compounds that could be repurposed for AUD if sufficient evidence is attained in preclinical studies.

Dissecting Brain Networks Underlying Alcohol Binge Drinking Using a Systems Genomics Approach.

Alcohol use disorder (AUD) is a complex psychiatric disorder with strong genetic and environmental risk factors. We studied the molecular perturbations underlying risky drinking behavior by measuring transcriptome changes across the neurocircuitry of addiction in a genetic mouse model of binge drinking. Sixteen generations of selective breeding for high blood alcohol levels after a binge drinking session produced global changes in brain gene expression in alcohol-naïve High Drinking in the Dark (HDID-1) mice. Using gene expression profiles to generate circuit-level hypotheses, we developed a systems approach that integrated regulation of gene coexpression networks across multiple brain regions, neuron-specific transcriptional signatures, and knowledgebase analytics. Whole-cell, voltage-clamp recordings from nucleus accumbens shell neurons projecting to the ventral tegmental area showed differential ethanol-induced plasticity in HDID-1 and control mice and provided support for one of the hypotheses. There were similarities in gene networks between HDID-1 mouse brains and postmortem brains of human alcoholics, suggesting that some gene expression patterns associated with high alcohol consumption are conserved across species. This study demonstrated the value of gene networks for data integration across biological modalities and species to study mechanisms of disease.

From gene networks to drugs: systems pharmacology approaches for AUD.

The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.

Peroxisome Proliferator Activated Receptor Agonists Modulate Transposable Element Expression in Brain and Liver.

Peroxisome proliferator activated receptors (PPARs) are nuclear hormone receptors that act as transcription factors in response to endogenous lipid messengers. The fibrates and thiazolidinediones are synthetic PPAR agonists used clinically to treat dyslipidemia and Type 2 Diabetes Mellitus, respectively, but also improve symptoms of several other diseases. Transposable elements (TEs), repetitive sequences in mammalian genomes, are implicated in many of the same conditions for which PPAR agonists are therapeutic, including neurodegeneration, schizophrenia, and drug addiction. We tested the hypothesis that there is a link between actions of PPAR agonists and TE expression. We developed an innovative application of microarray data by mapping Illumina mouse WG-6 microarray probes to areas of the mouse genome that contain TEs. Using this information, we assessed the effects of systemic administration of three PPAR agonists with different PPAR subtype selectivity: fenofibrate, tesaglitazar, and bezafibrate, on TE probe expression in mouse brain [prefrontal cortex (PFC) and amygdala] and liver. We found that fenofibrate, and bezafibrate to a lesser extent, up-regulated probes mapped to retrotransposons: Short-Interspersed Elements (SINEs) and Long-Interspersed Elements (LINEs), in the PFC. Conversely, all PPAR agonists down-regulated LINEs and tesaglitazar and bezafibrate also down-regulated SINEs in liver. We built gene coexpression networks that partitioned the diverse transcriptional response to PPAR agonists into groups of probes with highly correlated expression patterns (modules). Most of the differentially expressed retrotransposons were within the same module, suggesting coordinated regulation of their expression, possibly by PPAR signaling. One TE module was conserved across tissues and was enriched with genes whose products participate in epigenetic regulation, suggesting that PPAR agonists affect TE expression via epigenetic mechanisms. Other enriched functional categories included phenotypes related to embryonic development and learning and memory, suggesting functional links between these biological processes and TE expression. In summary, these findings suggest mechanistic relationships between retrotransposons and PPAR agonists and provide a basis for future exploration of their functional roles in brain and liver.

Genome-Wide Expression Profiles Drive Discovery of Novel Compounds that Reduce Binge Drinking in Mice.

Transcriptome-based drug discovery has identified new treatments for some complex diseases, but has not been applied to alcohol use disorder (AUD) or other psychiatric diseases, where there is a critical need for improved pharmacotherapies. High Drinking in the Dark (HDID-1) mice are a genetic model of AUD risk that have been selectively bred (from the HS/Npt line) to achieve intoxicating blood alcohol levels (BALs) after binge-like drinking. We compared brain gene expression of HDID-1 and HS/Npt mice, to determine a molecular signature for genetic risk for high intensity, binge-like drinking. Using multiple computational methods, we queried LINCS-L1000 (Library of Integrated Network-Based Cellular Signatures), a database containing gene expression signatures of thousands of compounds, to predict candidate drugs with the greatest potential to decrease alcohol consumption. Our analyses predicted novel compounds for testing, many with anti-inflammatory properties, providing further support for a neuroimmune mechanism of excessive alcohol drinking. We validated the top 2 candidates in vivo as a proof-of-concept. Terreic acid (a Bruton's tyrosine kinase inhibitor) and pergolide (a dopamine and serotonin receptor agonist) robustly reduced alcohol intake and BALs in HDID-1 mice, providing the first evidence for transcriptome-based drug discovery to target an addiction trait. Effective drug treatments for many psychiatric diseases are lacking, and the emerging tools and approaches outlined here offer researchers studying complex diseases renewed opportunities to discover new or repurpose existing compounds and expedite treatment options.

Localization of PPAR isotypes in the adult mouse and human brain.

Peroxisome proliferator-activated receptors (PPARs) are nuclear hormone receptors that act as ligand-activated transcription factors. PPAR agonists have well-documented anti-inflammatory and neuroprotective roles in the central nervous system. Recent evidence suggests that PPAR agonists are attractive therapeutic agents for treating neurodegenerative diseases as well as addiction. However, the distribution of PPAR mRNA and protein in brain regions associated with these conditions (i.e. prefrontal cortex, nucleus accumbens, amygdala, ventral tegmental area) is not well defined. Moreover, the cell type specificity of PPARs in mouse and human brain tissue has yet to be investigated. We utilized quantitative PCR and double immunofluorescence microscopy to determine that both PPAR mRNA and protein are expressed ubiquitously throughout the adult mouse brain. We found that PPARs have unique cell type specificities that are consistent between species. PPARα was the only isotype to colocalize with all cell types in both adult mouse and adult human brain tissue. Overall, we observed a strong neuronal signature, which raises the possibility that PPAR agonists may be targeting neurons rather than glia to produce neuroprotection. Our results fill critical gaps in PPAR distribution and define novel cell type specificity profiles in the adult mouse and human brain.

PPAR agonists regulate brain gene expression: relationship to their effects on ethanol consumption.

Peroxisome proliferator-activated receptors (PPARs) are nuclear hormone receptors that act as ligand-activated transcription factors. Although prescribed for dyslipidemia and type-II diabetes, PPAR agonists also possess anti-addictive characteristics. PPAR agonists decrease ethanol consumption and reduce withdrawal severity and susceptibility to stress-induced relapse in rodents. However, the cellular and molecular mechanisms facilitating these properties have yet to be investigated. We tested three PPAR agonists in a continuous access two-bottle choice (2BC) drinking paradigm and found that tesaglitazar (PPARα/γ; 1.5 mg/kg) and fenofibrate (PPARα; 150 mg/kg) decreased ethanol consumption in male C57BL/6J mice while bezafibrate (PPARα/γ/ß; 75 mg/kg) did not. We hypothesized that changes in brain gene expression following fenofibrate and tesaglitazar treatment lead to reduced ethanol drinking. We studied unbiased genomic profiles in areas of the brain known to be important for ethanol dependence, the prefrontal cortex (PFC) and amygdala, and also profiled gene expression in liver. Genomic profiles from the non-effective bezafibrate treatment were used to filter out genes not associated with ethanol consumption. Because PPAR agonists are anti-inflammatory, they would be expected to target microglia and astrocytes. Surprisingly, PPAR agonists produced a strong neuronal signature in mouse brain, and fenofibrate and tesaglitazar (but not bezafibrate) targeted a subset of GABAergic interneurons in the amygdala. Weighted gene co-expression network analysis (WGCNA) revealed co-expression of treatment-significant genes. Functional annotation of these gene networks suggested that PPAR agonists might act via neuropeptide and dopaminergic signaling pathways in the amygdala. Our results reveal gene targets through which PPAR agonists can affect alcohol consumption behavior.

Neuroimmune pathways in alcohol consumption: evidence from behavioral and genetic studies in rodents and humans.

Immune or brain proinflammatory signaling has been linked to some of the behavioral effects of alcohol. Immune signaling appears to regulate voluntary ethanol intake in rodent models, and ethanol intake activates the immune system in multiple models. This bidirectional link raises the possibility that consumption increases immune signaling, which in turn further increases consumption in a feed-forward cycle. Data from animal and human studies provide overlapping support for the involvement of immune-related genes and proteins in alcohol action, and combining animal and human data is a promising approach to systematically evaluate and nominate relevant pathways. Based on rodent models, neuroimmune pathways may represent unexplored, nontraditional targets for medication development to reduce alcohol consumption and prevent relapse. Peroxisome proliferator-activated receptor agonists are one class of anti-inflammatory medications that demonstrate antiaddictive properties for alcohol and other drugs of abuse. Expression of immune-related genes is altered in animals and humans following chronic alcohol exposure, and the regulatory influences of specific mRNAs, microRNAs, and activated cell types are areas of intense study. Ultimately, the use of multiple datasets combined with behavioral validation will be needed to link specific neuroimmune pathways to addiction vulnerability.